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# Use Cases for the Application of Generative Artificial Intelligence for Researchers: A Survey

Jonas Witte, Samed Bayer, Ingo Weber

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## To cite this version:

Jonas Witte, Samed Bayer, Ingo Weber. Use Cases for the Application of Generative Artificial Intelligence for Researchers: A Survey. 2026. hal-05463006

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Preprint submitted on 16 Jan 2026

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# Use Cases for the Application of Generative Artificial Intelligence for Researchers: A Survey

JONAS WITTE, SAMED BAYER, and INGO WEBER, Technical University of Munich - School of CIT, Germany and Fraunhofer Society, Germany

Generative Artificial Intelligence (GenAI) has the potential to revolutionize scientific work. However, a large-scale structured overview that identifies use cases of GenAI in science and research, across disciplines, has not yet been conducted. This study addresses this gap by conducting a structured literature review of 204 papers. From the review, we identified seven categories (Ideation, Literature Review, Methodology, Data Collection, Programming, Data Analysis, and Writing) with a total of 118 use cases. This paper presents an overview over the literature in tutorial-like style, outlines opportunities for researchers to integrate GenAI into their work, and provides a foundation for the detailed investigation and implementation of specific use cases.

Additional Key Words and Phrases: Generative Artificial Intelligence, Use Cases, Researcher, Scientist

## 1 Introduction

Scientific research has long been a driver of technological and societal progress. Research is also humanity's main means of addressing its largest challenges, such as curing diseases, increasing travel and transit safety, and countering the sources and effects of climate change. The work that comprises scientific research is done by scientists and researchers<fnref n="1" />. Besides the main tasks of conducting scientific research, researchers also write papers, prepare and give presentations, apply for grants, read or review manuscripts of other researchers, and perform many more tasks. Technological advances have already had a major impact on how research is conducted, such as the introduction of computers or widespread Internet access. Generative Artificial Intelligence (GenAI)² is a recent branch of the long-standing field of AI, and is transforming the work in many professions. Among others, GenAI has been found to increase productivity and, at times, product quality in the fields of software development [148], professional writing [136], and management consulting [50], which suggest that researchers could benefit from using GenAI in their work. However, the work of researchers is very diverse; while some of it is discipline-specific, there is a variety of tasks that all or most researchers perform. Hence, we aim to create a comprehensive overview of discipline-agnostic use cases for applying GenAI in research. Such an overview offers researchers the opportunity to increase their productivity, while also providing introductory literature for researchers who want to investigate specific use cases. The list is not intended to specify where, and where not, GenAI should be used in research in the future. Ethical acceptability, task-specific suitability, and technical feasibility must be reviewed individually for each use case.

Several papers have already identified use cases describing how GenAI can be employed by researchers. Nejjar et al. [133] examine the use of GenAI for code generation and data analysis in scientific contexts, and in their outlook, list an additional 16 use cases for how GenAI could be leveraged in research. However, these use cases are not derived from any explicit methodological procedure. In contrast, Morris [129] conducted 20 interviews with researchers between January and February 2023. She identified use cases related to data: data, literature reviews, coding, discovery,

Authors' Contact Information: Jonas Witte, jonas.witte@tum.de; Samed Bayer, samed.bayer@tum.de; Ingo Weber, ingo.weber@tum.de, Technical University of Munich - School of CIT, Munich, Germany and Fraunhofer Society, Munich, Germany.

<footnote n="1">¹The distinction between scientist and researcher is not applied consistently in the literature. In this paper, we use the term "researchers" but also include "scientists" in its meaning.
²In this paper, we consider GenAI not as comprising only the underlying GenAI models, but also entire systems which may have access to the Internet, databases, and other systems as needed.</footnote>
and communication. Pigg [153] analyzes 35 YouTube and TikTok videos and synthesizes the use cases mentioned therein. She identifies 45 use cases, which she classifies into eight groups. Rowland [171] outlines use cases for student academic research, many of which are independent of the student context and applicable across all levels of research experience. Furthermore, editorials on this topic have been published and similarly enumerate use cases [73, 170, 181, 193]. In addition, several papers investigate how specific research disciplines can employ GenAI, such as Pack and Maloney [139] for *Language Education Research*, Bail [16] for *Social Science*, Korinek [106] for *Economic Research*, or Zimmermann et al. [219] for *Materials Science*.

Many of the published works are position papers or editorials in which the authors propose potential use cases of GenAI in research but do not develop or systematically investigate them. These papers typically do not follow a scientific methodology; rather, they reflect the authors’ subjective perspectives. Other studies lay a solid valuable foundation, but are limited due to their thematic scope and sample size. To complement this body of work, we conducted a structured literature review (SLR) to collect, categorize, and assess the broad range of publications about *use cases for improving researchers’ work using GenAI*. In this paper, we report on this SLR, covering 204 publications and insights we have gained from it. Note that our work focuses on *use cases* that are not specific to any field of science, but apply to *all or most fields of science and research*.

We structure the set of 118 discovered use cases into main categories, following the scientific process with the categories ideation, literature review, methodology, data collection, programming, data analysis, and writing. To enable researchers to consume the paper easily and hence foster the application of the insights, we present the material in tutorial-like style. Besides researchers-as-practitioners, this article will also serve researchers working on "LLMs for science" – be it behavioral work of understanding practices of GenAI in science, or design science to support researchers’ uses of GenAI with new approaches or tools.

The remainder of this article is structured as follows. Technical background for advanced use cases is given in Section 2. Subsequently, we outline the SLR methodology applied in Section 3. A large portion of the paper is comprised of the SLR results, Section 4. Following a broader discussion in Section 5, including limitations and threats to validity, Section 6 concludes the paper.

# 2 Technical Background

To effectively leverage GenAI in scientific research, it is essential to understand its underlying mechanisms and capabilities for adaptation. This chapter provides the technical vocabulary and concepts to understand how GenAI systems can be applied to support the use cases in this survey.

## 2.1 GenAI and LLMs

GenAI refers to AI models capable of generating new content, such as text, code, images, or mixed modalities, in response to user prompts, as well as the systems built around those models. At the core are *foundation models*, which are trained on massive corpora of data to learn general patterns that can later be adapted to specific downstream tasks [26]. While early models focused on a single modality, the latest models are increasingly multimodal, allowing a single model to process scientific documents, extract structured facts, and draft analysis code simultaneously. Similar multi-modality can be achieved on the system level by combining multiple single-modality models. However, most AI tools for research currently anchor on text and code, as these modalities are most important and easier to log, inspect, and evaluate in scientific workflows.

Functionally, these systems often serve as research assistants that can be integrated into various stages of the scientific lifecycle. Researchers could use them to process and summarize the literature, extract structured data from unstructured text, and draft code for analysis. Beyond these routine tasks, modern systems increasingly support higher-level cognitive work, such as generating
hypotheses and planning experimental protocols. These systems aim to become knowledgeable co-pilots, augmenting human expertise across the research process.

Technically, most GenAI models rely on the Transformer architecture [201]. Its core mechanism, *self-attention*, enables learning relationships between concepts across long sequences of text. This capability is developed during the *pre-training* phase, a computationally intensive process where the model learns to predict the next token based on massive datasets. A key distinction for researchers lies in the data used during this phase. General-purpose models learn from the broad internet, whereas domain-specific models (like SciBERT [20]) utilize scientific corpora. This allows them to internalize technical vocabulary and citation structures, resulting in a domain-specific base model that possesses knowledge but lacks the specific behavior required to follow user instructions.

Pretrained AI models behave mainly as next-token predictors rather than cooperative dialogue partners. To make them useful for research workflows, models undergo *post-training*. This typically starts with *supervised fine-tuning (SFT)* on instruction-response pairs to teach format and task execution. Then, *reinforcement learning from human feedback (RLHF)* is used to train a reward model from human preferences and optimize the model's policy to align outputs with user intent [138, 190]. After training, generating responses for new inputs is referred to as *inference*.

Despite such alignment, foundation models remain generalists. They often lack deep expertise in niche scientific domains, cannot access private laboratory data, and are unaware of papers published after their training cutoff, which results in hallucination or confabulation [185]. Therefore, to meet scientific standards of accuracy and depth, researchers or organizations need to move beyond passive usage to active adaptation. This necessity of bridging the gap between general capability and specific research needs leads directly to the *customization techniques* discussed next.

## 2.2 Customization Techniques

This subsection introduces customization techniques for adapting foundation models to scientific workflows. We adopt the techniques described by Bass et al. [17, Chapter 4], and complement them with information about usage in research. Each technique is presented with a practical example for researchers, in line with the tutorial-like style of this paper.

**Prompt Engineering** refers to the targeted and structured design of input prompts that guide a pretrained model to produce high-quality, consistent outputs [126]. In scientific workflows, effective prompts specify roles, task constraints, and an explicit output schema (e.g., JSON fields) to enable downstream parsing and auditing. Core prompting patterns include *few-shot prompting* [29] (teaching the desired output format and decision boundaries with a few labeled examples), *chain-of-thought (CoT) prompting* [209] (asking the model to write out its step-by-step reasoning before the final answer), *dynamic prompting* [172] (adapting the prompt automatically to the current context), and *self-consistency* [206] (sampling multiple answers and selecting the best/majority) [126].

### Example – Data Labeling with Few-Shot Learning

In a survey analysis, open-ended answers about trust in ML-based systems can be labeled as {low, medium, high} via few-shot prompting. The prompt supplies brief label definitions, two demonstrations per class (including one borderline case), and requires a single JSON object `{"label": "low|medium|high|uncertain", "rationale": "..."}`. The template sets a role (“research assistant”), states the task and criteria, presents the demonstrations, and enforces the schema. This design anchors decision boundaries, reduces forced guesses via *uncertain*, yields parsable outputs for downstream analysis, and can reduce the time-consuming and costly manual labeling process compared with purely human annotation or task-specific fine-tuning.

Prompting is the lowest-cost customization technique. It requires no model parameter updates and can be applied immediately across various scientific tasks, including classification, knowledge
extraction, summarization, code drafting/refactoring, and query generation. Only a small set of curated examples is needed for customization, and standard inference (hosted or local) is sufficient. Since prompting relies solely on a pretrained model, it can produce confident but incorrect (hallucinated) outputs and is limited to knowledge available up to the model's training cutoff.

**Retrieval Augmented Generation (RAG)** integrates a model with an external knowledge base, so answers are grounded in retrieved sources rather than relying solely on the model's internal memory up to its training date [110]. In scientific workflows, a typical pipeline parses documents (e.g., a library of PDFs), splits them into smaller text chunks, indexes them, and, at query time, retrieves the most relevant chunks [94]. These retrieved chunks are then inserted into the prompt as context for the final generation step [63].

Compared with prompting alone, RAG reduces hallucinations, overcomes knowledge cutoffs by citing up-to-date sources, and improves auditability via explicit provenance. It is also a low-effort way to keep a system current without retraining the base model. Limitations include coverage gaps (missing relevant documents), retrieval of outdated or low-quality sources, and the risk of prompt injection (hidden instructions in retrieved text that try to override the model's instructions) [214].

**Example – Answering a literature question from PDFs with RAG**

Given a large corpus of scientific PDFs, the documents are parsed into structured text, split into section-aware chunks, and indexed in a searchable database. When a user asks a question, the system searches the database to retrieve the most relevant chunks. The prompt then supplies these chunks and the user question as context, and instructs the model to answer strictly from the chunks with a short answer and citations. This design grounds responses in the literature, provides provenance for auditing, and stays current by updating the index as new papers appear.

**Parameter-Efficient Fine-Tuning (PEFT)** adapts a pretrained model to a new task or domain without retraining the entire model. Instead, it freezes the base model's weights and updates only a small set of parameters. Common approaches include *LoRA* (Low-Rank Adaptation), which injects small, trainable matrices into the model's layers [86], and *Adapters*, which insert lightweight modules between existing model layers [84]. For a summary of these families, their memory/compute trade-offs, and design choices, refer to the survey of Han et al. [80].

Compared with full model fine-tuning (below), PEFT reduces trainable parameters by orders of magnitude, lowers memory/compute cost, enables multi-task deployment (one big backbone model with many small task-specific adapters), and supports on-premise or privacy-sensitive settings where retraining the full model is impractical [84]. Limitations include underfitting if the task is too complex for the small set of trainable parameters, a distribution shift when base knowledge and adapter updates conflict, and quality issues when the backbone lacks the desired domain knowledge.

**Example – Domain-specific literature screening with LoRA**

A researcher trains a lightweight LoRA adapter on a small, labeled set of abstracts for a specific research area to classify papers (include/exclude) while the original model remains frozen. The model learns the include/exclude decision and returns a compact output. The process is faster and uses less compute than full fine-tuning. Also, the resulting adapter can be stored per research field, so multiple research fields can share the same backbone model. For example, researchers in the same lab can train separate adapters for different topics while sharing the same foundation model. This makes it ideal for research labs with limited hardware resources or strict data privacy requirements, allowing models to be updated efficiently as screening guidelines change.

**Full Model Fine-Tuning** adapts a pretrained model by updating *all* (or nearly all) of its weights on task- or domain-specific data. The pretrain-then-fine-tune paradigm in Natural Language
Processing (NLP) was established with systems such as BERT [53]. In the scientific domain, SciBERT showed that domain-specific pretraining followed by task-specific fine-tuning yields strong gains on downstream scientific NLP tasks, even with comparatively modest labeled data [20]. This is particularly necessary when the target domain diverges significantly from the general language patterns learned by standard models (e.g., in chemical notation or genomics).

Compared with lightweight customization (prompting or PEFT), full fine-tuning offers the greatest capacity to internalize target domain style, terminology, and task procedures. It often improves robustness and reduces the need for complex prompts. However, this comes with high costs in computing power and data preparation. There are also significant risks. First, the model may suffer from catastrophic forgetting, where it loses general capabilities such as basic logic while overfitting to the new scientific data [103]. Second, since the model updates all its weights, it could memorize sensitive training examples (e.g., patient names) and unintentionally disclose them later [36]. Practical mitigations include using high-quality data and mixing scientific data with general text during fine-tuning to maintain the model's overall stability.

## Example - Full fine-tuning with SciBERT for scientific NLP

SciBERT is pretrained on 1.14M full-text scientific papers from Semantic Scholar and then fine-tuned on downstream tasks [20]. One such task is citation-intent classification. Given sentences around citation markers labeled as *Background*, *Method*, or *Result Comparison* in the SciCite dataset, a pretrained SciBERT model is fine-tuned on labeled examples and then predicts the intent label for new citing sentences, optionally returning a confidence score [40]. Because SciBERT has been pretrained on scientific language, the fine-tuned model handles technical citation contexts reliably and provides compact outputs suitable for downstream analysis, illustrating how full model fine-tuning specializes a model for scientific context.

**Agents and Tool-Use** connects a Large Language Model (LLM) to external tools such as search engines, math libraries, databases, or laboratory instruments. Therefore, it can fetch real-time information, run computations, or take constrained actions instead of relying only on its static internal memory. An *agent* is an LLM-driven system that plans, calls tools, observes results, and executes a sequence of actions to achieve a goal. To do this effectively, agents rely on *reasoning*, which is the ability to decompose a complex user request into a sequence of logical steps. Frameworks like ReAct (Reason+Act) demonstrate that by interleaving reasoning with tool executions (actions), agents can significantly reduce hallucinations and more reliably complete complex tasks [211]. Models can also utilize methods like *Toolformer* to autonomously *learn when and how* to call APIs during generation, rather than using hard-coded API calls [177]. Beyond a single agent, multi-agent frameworks such as *AutoGen* assign specific roles to each agent (e.g., planner, retriever, critic) and coordinate tool-use through structured conversations among agents [210].

Compared with other customization techniques, tool-use/agents can (i) ground answers in live systems (search, databases), (ii) offload precise computation to specialized tools, and (iii) coordinate multi-step procedures with auditable traces [177, 211]. Trade-offs include higher latency/cost from multiple calls, brittleness to tool failures, and security vulnerabilities such as prompt injection, where external data tricks the agent into misusing a tool. Practical mitigations include whitelisting of allowed tools and requiring human approval before the agent executes sensitive actions [18].
## Example – Multi-agent literature assistant with tool-use

A researcher requests a summary of recent research on a specific protein from an agent-based system. A *Planner* agent decomposes the request into a series of steps: 1. Search for relevant papers, 2. Screen the results for relevance, 3. Synthesize the findings. The *Retriever* agent calls scholarly APIs and fetches a list of candidate papers. Next, an *Analyst* agent reads the abstracts (and selected PDFs) of these papers and extracts key information into a structured format. Finally, a *Critic* agent reviews the synthesized summary, checking for gaps or contradictions and potentially triggering another search if necessary. This multi-agent design grounds the final output in verifiable data and creates a transparent audit log. It can also be easily extended with other scientific tools, such as a statistics package for data analysis or a bioinformatics database.

## 3 Methodology

To identify the use cases of how researchers could employ GenAI, we conducted a SLR following the approach of vom Brocke et al. [204]. To define the scope of the SLR, we used the taxonomy by Cooper [43]. The taxonomy contains six different characteristics, each with several categories. The taxonomy is shown in Table 1, with gray-shaded cells indicating the options applicable here.

<table><caption>Table 1. Taxonomy by Cooper [43], with options applicable for this SLR highlighted in gray</caption><thead><tr><th colspan="5">Categories</th></tr></thead><tbody><tr><th>Focus</th><td>Research Outcomes</td><td>Research Methods</td><td>Theories</td><td>Applications</td></tr><tr><th>Goal</th><td>Integration</td><td colspan="2">Criticism</td><td>Central Issues</td></tr><tr><th>Organization</th><td>Historical</td><td colspan="2">Conceptional</td><td>Methodological</td></tr><tr><th>Perspective</th><td colspan="2">Neutral Representation</td><td colspan="2">Espousal of Position</td></tr><tr><th>Audience</th><td>Specialized Scholars</td><td>General Scholars</td><td>Practitioners & Politicians</td><td>General Public</td></tr><tr><th>Coverage</th><td>Exhaustive</td><td>Exhaustive & Selective</td><td>Representative</td><td>Central/Pivotal</td></tr></tbody></table>

The *Focus* of this SLR is *Applications* of GenAI in research, since, based on our research question, we want to identify use cases of how GenAI can be used by researchers. The *Goal* is *Integration*, since we summarize and structure use cases mentioned in the existing literature. The *Organization* is *Conceptual*, and later in this paper, we will provide more details about the organization of the use cases. The *Perspective* is *Neutral Representation*, as we will neither assess the feasibility nor provide an ethical perspective on the use cases. We collected and structured various use cases mentioned in the literature. The *Audience* is *General Scholars*, since we provide an overview of how researchers could use GenAI in their research and work. Accordingly, the list can help researchers to improve their research and increase their productivity. Furthermore, this overview can serve as a source of inspiration for further research into individual use cases. The *Coverage* of our SLR is *Representative*.

To identify the relevant keywords for the search string, we conducted an unstructured literature review. This allowed us to become familiar with some of the existing literature and to identify synonyms and related terms. Based on that, we created the following search string: ("GenAI" OR "Generative Artificial Intelligence" OR "Generative AI" OR "LLM" OR "Large Language Model") AND ("Researcher" OR "Scientist"). The string consists of two parts: the first part focuses on the technology and the second on the domain. The term *Researcher* describes a person who works scientifically, while the term *Scientist* is usually restricted to researchers in the natural sciences. However, the literature usually does not clearly differentiate between these terms, so we included both terms.

We chose *Google Scholar* as the database. We made this decision because Google Scholar covers a wide range of publishers and also includes papers on preprint servers. Even though papers on preprint servers pose the risk of lower quality, it is instrumental in this SLR to also include them in order to identify all use cases mentioned in the literature, especially since journal and conference
review cycles are long. Recent meta-research confirms that Google Scholar leads all scholarly databases in citation coverage and indexing breadth [76], typically exceeding 95% coverage. While no single source is truly exhaustive, the substantial volume of literature retrieved, as discussed below, suggests that querying additional databases would offer diminishing returns for this SLR. Note that we do not evaluate whether GenAI *should* be used in a specific use case or whether current foundation models are good enough to satisfy the needs of a use case. In balancing the option to include only high-quality papers with the need to consider the latest research, we opted for the inclusion of preprints. The same applies to tutorials, editorials, magazines, and similar sources. As long as it was indexed on *Google Scholar*, we included the publication. In this way we can obtain a holistic overview of the use cases of GenAI for researchers. The literature search was conducted on March 4, 2025.

We examined the first 1,000 entries on Google Scholar and conducted a title screening to assess whether a paper was relevant for our SLR. If we could not decide based on the title alone, we also screened the abstract. After the title and abstract screening, we retained 226 papers. Afterwards, we conducted a full-text analysis to identify the use cases. During this analysis, we identified and removed 22 papers that did not contain any relevant use cases. As a result, we identified at least one use case in 204 papers that describes how researchers could use GenAI in their research.

After collecting the papers, we performed a detailed full-text analysis. We read all the papers and documented the identified use cases. If a paper provided additional information about a specific use case, we recorded that as well. During this process, we also removed duplicates and generalized use cases that referred to broader categories, such as *Data Analysis*. After analyzing all papers we deleted duplicates and merged similar use cases into a single entry, arriving at the set of 118 use cases. Based on the content of the papers and the additional information provided, we then formulated descriptions for each use case.

A use case was included in our list even if the paper did not show that GenAI is currently capable of effectively supporting that use case. If a paper merely mentioned that GenAI could potentially be used in a specific research context, we still included it. This decision is based on two reasons. First, the pace of development in this field is extremely rapid and transformative, making it difficult to capture an accurate snapshot. For instance, a paper from 2023 might conclude that GenAI is not yet suitable for a specific use case, while a paper from 2025 might find that GenAI outperforms humans in that very area. This is due to the continuous improvement of both the foundation models and the surrounding systems, such as through customization. Second, the aim of this SLR is to identify potential use cases for GenAI in research—not to evaluate their current feasibility or effectiveness.

# 4 Results

In this section, we present the results of our SLR. We identified seven categories (Ideation, Literature Review, Methodology, Data Collection, Programming, Data Analysis, and Writing), and group the 118 use cases into the categories, as depicted in Figure 1. When describing a use case, we cite any publication *mentioning* this use case. It is important to note that only some of the cited papers report on completed research projects; others merely mention or apply a use case. As a side effect, this implies that we also cite papers that are not the original source – while not without downsides, this approach enables gaining a comprehensive overview of all use cases.

## 4.1 Ideation (ID: 1)

At the beginning of any research project, the research objective and, if applicable, the research question and hypotheses must first be formulated. GenAI could support this ideation process, and in our SLR, we were able to identify 6 use cases for this.
<figure><img src="image_4.png" /><figcaption>Fig. 1. Overview of the use cases</figcaption></figure>

**Development and Refinement of Research Ideas (ID: 1.1):** Identifying suitable research ideas often poses a challenge for researchers. GenAI could support researchers in developing, refining, and selecting research ideas and directions [5, 14, 19, 28, 48, 98, 107, 112, 119-121, 123, 129, 135, 137, 143, 149-151, 153, 160, 178, 183, 193, 213]. In this context, ideas could be derived from existing papers [112, 135, 160]. Furthermore, GenAI could review and evaluate existing ideas [19, 71, 106, 112, 160, 193] by systematically analyzing relevant literature [160] in order to identify particularly promising ideas. In addition, GenAI could provide feedback on ideas from different perspectives [51, 120] and iteratively further elaborate them [120]. Moreover, GenAI could generate motivations aligned with the core idea, highlighting its relevance [112, 120, 121, 135].

**Brainstorming Support (ID: 1.2):** GenAI could support researchers during brainstorming, enabling them to gain new perspectives and ideas for their research projects [1, 19, 37, 48, 51, 54, 78, 95, 98, 106, 107, 115, 119, 121, 123, 125, 132, 137, 143, 157, 166, 171, 181, 191]. The output could serve as a starting point on which researchers could then build. Brainstorming could also be used to identify analytical approaches [75] or data science techniques [181].

**Formulation of Research Problems and Goals (ID: 1.3):** GenAI could support researchers in the formulation and definition of research problems [5, 71, 193] as well as research goals [92].

**Formulation of Research Questions (ID: 1.4):** GenAI could suggest research questions to researchers [2, 16, 45, 46, 56, 82, 92, 98, 107, 119, 120, 129, 139, 153, 155, 171, 193, 213], refine them [5, 48, 51, 120, 195] or support the formulation of research questions [21, 35, 48, 149]. GenAI could further make long-term projections and generate research questions that could be relevant in the distant future [2]. In addition, GenAI could provide feedback on research questions [51] or, by accessing a database, assess the relevance and novelty of research questions [35, 119].

**Hypothesis Generation and Evaluation (ID: 1.5):** GenAI could support researchers in formulating hypotheses [7, 14, 21, 30, 32, 35, 41, 44, 56, 58, 66, 71-73, 81, 82, 90, 92, 97, 112, 115, 121, 122, 124, 134, 155, 166, 182, 188, 203] or refining them [92, 129, 182, 213]. To generate hypotheses, GenAI could conduct a literature review to identify what is already known, use datasets, or engage in simulated scientific debates among GenAI-instantiated expert roles using self-critique and self-play [71, 90, 166]. In addition, new hypotheses could be generated based on existing ones [71]. Hypothesis generation could also be performed iteratively by providing the GenAI with additional information over time [71, 92, 122, 182, 188, 213]. Furthermore, GenAI could be used to evaluate existing hypotheses regarding different criteria, such as correctness, quality, and novelty [71].

**Research Agenda Development (ID: 1.6):** GenAI could support researchers in developing a research agenda and thus help plan future research [71, 179].

## 4.2 Literature Review (ID: 2)

The foundation of any research project is a thorough understanding of the current state of knowledge related to the research topic. Due to the high similarity between the methodology of a structured literature review and the literature review at the beginning of a research project, these two topics are combined into a single section to avoid duplication of use cases. Through our SLR, we were able to identify 22 use cases in which GenAI could support the literature review.
**Source Selection Support for Literature Reviews (ID: 2.1):** When researchers enter a new research field or explore a topic outside their core expertise, and accordingly want to familiarize themselves with the literature, they are often confronted with a wide range of potential sources. GenAI could help to quickly obtain information about the sources. For example, GenAI could generate a list of the best sources within a research discipline [193]. In addition, GenAI could generate information about the areas in which the sources are strong in terms of topic and methodology [193].

**Literature Search (ID: 2.2):** Over the past decades, the number of published papers has increased steadily, making it ever more difficult to maintain an overview of the available literature and findings. GenAI could help identify relevant literature [5, 9, 14, 19, 37, 51, 70, 77, 88, 92, 102, 105, 108, 120, 121, 130, 135, 140, 143, 151, 153, 165, 166, 171, 178, 179, 183, 193]. To achieve this, the GenAI would need to be provided via the prompt with either the research question, keywords, journals, authors, a description of the research project, or a combination of these and other criteria [11, 51, 70, 131, 135, 178]. This could be particularly beneficial for interdisciplinary projects, where the relevant keywords for a keyword-based search could not be known in advance [129]. In this context, the researcher's disciplinary background could also be taken into account [120]. In addition, based on an existing paper, similar papers or research projects could be identified [9, 11, 35, 54, 92, 119-121]. GenAI could also generate a brief justification explaining why a particular paper is considered relevant [129]. For literature searches, GenAI could make use of both well-known and less well-known databases [11, 105, 186] and also search the internet [5, 106, 178]. Moreover, GenAI could monitor newly published papers within a researcher's field and notify the researcher as soon as a new relevant paper is published [135]. GenAI could also be employed to identify appropriate sources and evidence to support an existing claim [51, 85, 108]. Finally, GenAI could be used to predict whether a paper is likely to receive a high number of citations in the future [70, 129]. This could enable a more targeted selection from the growing number of published papers [129].

**Literature Organization and Management (ID: 2.3):** Organizing and managing literature is a time-consuming task, and GenAI could provide support in this regard [11, 70, 139]. GenAI tools could parse bibliographies and integrate them with academic databases [89]. In addition, GenAI could retrieve bibliographic information such as the DOI, URL, etc. for a given paper [178].

**Search String and Search Strategy Development (ID: 2.4):** GenAI could support the development of a boolean search string that could then be used in databases to identify relevant literature. For this purpose, GenAI could either generate a completely new search string [37, 77, 83, 102, 112, 160] or improve a human-created search string through reformulations as well as the addition and removal of terms [37, 102]. This could be extended to the generation and execution of lists of queries [92] and the generation of search strategies [37].

**Title and Abstract Screening (ID: 2.5):** The screening and filtering of papers is an essential component of structured literature reviews. However, even beyond structured literature reviews, papers must be filtered based on their titles and abstracts in order to identify those most relevant to one's own research, as there is nowadays a large volume of papers. GenAI could screen titles, abstracts, or even entire papers and, based on this, provide an assessment of whether a paper is relevant, after which papers could be filtered [77, 112, 130, 139, 212]. Through the prompt, GenAI could also be provided with predefined criteria, such as keywords, authors, journals, or a combination of these and other factors, as well as inclusion and exclusion criteria, in order to make the screening more personalized and goal-oriented [11, 212]. Automated screening could help reduce bias, as meta-information could be taken into account to a lesser extent than when screening is performed by a human [212].

**Text-to-Speech Conversion for Scientific Papers (ID: 2.6):** GenAI could be used to read scientific papers aloud to researchers [51, 89]. This could help researchers stay up to date with the current state of research and the growing volume of papers [89].
**Note-Taking and Organization of Information (ID: 2.7):** Researchers read a large number of papers and take notes on them in order to stay up to date with the current state of research. GenAI could help create notes [37, 171], organize information [149], and identify key topics in notes [171].

**Single-Paper and Section Summarization (ID: 2.8):** GenAI could be used to summarize entire papers or parts of a paper [6, 11, 14, 15, 19, 21, 22, 25, 41, 42, 58, 70, 75, 106, 109, 112, 114, 129, 135, 139, 149–151, 153, 155, 161, 166, 168, 170, 178, 186, 193, 195, 200, 202, 205, 207, 212, 217]. Researchers could specify certain requirements for the summary via the prompt, such as the length, style, or format of the summary [7, 115, 186]. In addition, summaries could be tailored to focus on specific aspects, such as the problem, method, or findings [7, 35, 153, 171, 186, 213]. For this purpose, it could also be sufficient to provide to GenAI only the link where the paper can be found online [178].

**Discipline-Specific Question Answering (ID: 2.9):** Researchers frequently face a wide range of domain-specific questions that often can only be answered through an extensive literature review. Since GenAI systems have been trained on very large datasets, they could be used by researchers to answer domain-specific questions and provide relevant information [21, 22, 30, 37, 51, 58, 64, 66, 70, 77, 150, 167, 170, 188, 197, 215]. GenAI could generate numbers and findings based on the specific requirements of the user [167]. If a number of papers are provided to the GenAI, the answer generation could be based on the material, and the sources could be cited accordingly [95].

**Chat with a Document (ID: 2.10):** GenAI could be used to engage in an interactive conversation with a paper. For this purpose, the text or paper must be provided to the GenAI, for example by uploading it. Subsequently, researchers could ask questions and follow-up questions [14, 135, 186, 202, 207]. This could allow researchers to retrieve specific information from a paper without having to read the entire paper. If multiple papers are provided to the GenAI, it is also possible to chat with multiple papers simultaneously [14, 64].

**Information Extraction from Literature and Databases (ID: 2.11):** GenAI could be used to transform papers into structured formats and to extract information from one or multiple papers or datasets [11, 21, 33, 35, 72, 74, 79, 95, 106, 129, 139, 150, 166, 182, 203, 212, 213, 216]. Via prompting, instructions could be provided specifying which data should be extracted, such as experimental procedures, key performance metrics [134], or alloy compositions [118]. More generally, prompts could target different information categories, for example, results, specific numerical values, definitions, topic or core idea, experimental design, authors, publication year, methodology, abstract or other predefined types of information [14, 44, 72, 112, 129, 178, 202, 213, 215]. The extracted data could subsequently be represented in formats such as JSON or in predefined templates to enable further processing [72, 95, 135, 150, 216]. Data extraction is not limited to plain text but could also be applied to tables and figures [46, 95, 213, 215, 216]. This approach could also support the screening of papers by extracting key information relevant for assessing inclusion and exclusion criteria, which could then be manually reviewed by researchers [213]. Furthermore, relevant information could be extracted during a (structured) literature review to systematically analyze the literature and to prepare the final report [11, 21, 33, 74, 130, 166].

**Concept Mapping and Coding (ID: 2.12):** Coding papers can be time-consuming when performed manually. GenAI could support thematic analysis by analyzing papers and assigning them to corresponding dimensions [58, 130, 179].

**Identification of Theories, Frameworks, and Concepts (ID: 2.13):** In scientific research, a wide range of theories, frameworks, and concepts are used, which researchers need to be familiar with. GenAI could help identify suitable theories and concepts that fit a given research project and that have already been applied in similar research contexts [14, 51, 120, 149, 153, 169, 179]. This could lead to time savings and to higher-quality research outputs.

**Explanation of Scientific Texts and Concepts (ID: 2.14):** Papers contain a wide range of terms, concepts, results, definitions, and so on, which can be difficult to understand due to their complexity.
GenAI could support researchers by providing explanations or definitions [14, 21, 38, 166, 179]. In addition, summaries and comparisons of theories, frameworks, constructs, and information could be generated [14, 37, 51, 106, 125, 129, 139, 153, 166, 171, 178, 179]. Prompts could be used to specify the desired style and level of detail of the explanation, enabling technical texts to be quickly translated into clear, accessible language [51, 171]. This is particularly beneficial for initial comprehension and for interdisciplinary research contexts. Moreover, GenAI could be used to clarify definitions [179]. GenAI could also be employed to explain the methodology of a paper [202].

**Conceptual and Interdisciplinary Relationship Discovery (ID: 2.15):** GenAI could support the discovery and understanding of conceptual relationships as well as interdisciplinary connections, which is often obscured by separate terminologies. For example, GenAI could identify relationships between authors [149]. In addition, GenAI could detect correlations [166] or uncover hidden connections between different research fields and concepts [129, 149, 179, 197].

**Theorizing (ID: 2.16):** In many scientific disciplines, the development of theories is a central component of research. GenAI could support this process by efficiently analyzing large volumes of literature and extracting relevant insights, for example, by supporting formulation and refinement of theoretical models or conceptual frameworks [44, 56, 97, 107, 179]. During theory building, humans could also engage in a dialogue with GenAI to further advance the current state of theory, while GenAI could question and critically reflect on existing theories [179].

**Paper and Content Comparison (ID: 2.17):** GenAI could support the comparison of multiple papers or pieces of content with each other [30, 213].

**Critical Appraisal of Scientific Literature (ID: 2.18):** GenAI could help to critically examine and contextualize existing scientific work and papers [27]. To this end, GenAI could support the analysis of the strengths and weaknesses of the existing literature and facilitate the critical reflection of prior research, for example by structuring and elaborating already identified issues [70, 71, 153, 179]. This could also include taking into account the subsequent citations of a paper [70].

**Multi-Paper Summarization and Topic Overview Generation (ID: 2.19):** Being familiar with the current state of research is fundamental to high-quality research. GenAI could support this by summarizing multiple papers [7, 16, 95, 108, 207] or by providing a general overview of a research field [9, 14, 16, 37, 48, 51, 52, 58, 60, 70, 71, 73, 98, 106, 119, 120, 129, 137, 139, 140, 149, 151, 153, 165, 181]. In doing so, the linguistic style could be taken into account and the text could be generated in simpler language [37]. This could also allow for the consideration of interdisciplinary perspectives [149]. It could be particularly helpful when one is newly familiarizing oneself with a research topic [153] or when aiming to stay up to date with the current state of research [52, 137, 165, 166, 202]. It could also support integrating one's own research more effectively into the existing body of research [120]. Grey literature could also be considered [186]. The content of the summary could be derived from provided papers, from the internet, based on the model's training data, or through querying databases [71, 95, 153, 165]. Additionally, GenAI could generate ideas for prompts that help researchers obtain better information about the topic [140].

**Literature Review Writing (ID: 2.20):** Writing literature reviews is both an essential part of research and often very time-consuming. GenAI could be used to support the writing of literature reviews, meta-reviews or related work sections [6, 16, 21, 22, 27, 45, 51, 58, 69, 70, 77, 90, 97, 98, 108, 109, 129, 130, 135, 137, 153, 155, 158, 170, 179, 193, 205].

**Research Trend Identification and Summarization (ID: 2.21):** GenAI could support identifying current research trends and topics [11, 89, 112, 129, 216] and predicting future trends [112].

**Research Gap Identification and Analysis (ID: 2.22):** The goal of researchers is to identify and address existing research gaps. However, the papers are continuously being published, causing the current state of knowledge to shift constantly. GenAI could support the analysis of existing research by identifying current research gaps or by generating summaries of open research questions [11,
14, 37, 51, 59, 60, 90, 107, 111, 129, 135, 151, 153, 156, 166, 179]. One possible implementation is to construct a text corpus from a specific research domain and have the GenAI analyze this corpus [59].

## 4.3 Methodology (ID: 3)

The selection and correct application of a method are of crucial importance. However, methods can be highly complex, in which case GenAI could provide support at various stages. This work distinguishes between general research support for methodology (4.3.1) and targeted support for qualitative (4.3.2) and quantitative (4.3.3) research. Through our SLR, we were able to identify 28 use cases in which GenAI could support methodological processes.

### 4.3.1 Methodology - General (ID: 3.1)
**Method Identification (ID: 3.1.1):** GenAI could be used to identify existing methods or approaches from the literature. This could be based on similar research problems or analysis techniques, from which approaches for the problem at hand could subsequently be derived [70, 135]. GenAI could also be used to ensure the novelty of a proposed approach by highlighting related and already existing methods [70]. In addition, GenAI could identify the most frequently used approaches in the literature for a given problem [70].

**Method Development (ID: 3.1.2):** The development of new methods represents a challenge for researchers. GenAI could propose new methods [166, 181] or be used for brainstorming during the development of new approaches [49].

**Method Selection and Recommendation (ID: 3.1.3):** Selecting an appropriate method is of critical importance. GenAI could be used in a supportive manner to recommend a suitable method for the current research project [48, 82, 112, 120, 135, 143, 153, 193] and to justify this recommendation with appropriate arguments [82, 112].

**Method Explanation (ID: 3.1.4):** Scientific methods exhibit a certain level of complexity. GenAI models contain extensive knowledge of scientific workflows and methods [131]. GenAI could be used to explain methods or to answer targeted questions about a specific method [37, 153]. GenAI could therefore provide guidance and support for methodological decisions [205].

**Methodological Feedback and Critique (ID: 3.1.5):** GenAI could be used to improve an existing method by providing feedback in the form of comments and suggestions [5, 13, 48, 51, 73, 112, 193]. This could lead to increased reliability and validity of research results [73]. In addition, GenAI could highlight potential ethical issues in the method before it is applied [16].

**Research and Experimental Design Planning and Optimization (ID: 3.1.6):** Designing research studies is a complex task in which GenAI could support researchers at multiple stages. GenAI could assist in designing and planning experiments and research designs [9, 35, 45, 48, 58, 59, 71, 73, 92, 95, 98, 102, 105, 107, 112, 121, 122, 124, 129, 134, 139, 151, 153, 155, 159, 160, 181, 193, 195], including the generation of a timetable [51]. Inputs for such generation could include the research problem, method, and other contextual constraints [135, 193]. Furthermore, GenAI could generate assumptions, proposal preferences, attributes, constraints, research variables, experimental parameters, and initial experimental conditions for these experiments [71, 95, 97, 115, 188]. GenAI could also be used for the operationalization of constructs [56]. In addition, GenAI could act as a feedback provider, enabling researchers to independently refine the design or select an appropriate design based on this feedback [129, 139]. When human users test a system and provide feedback, GenAI could be used to categorize this feedback and identify potential patterns [111]. GenAI could also support the generation of informed consent forms for specific experiments, in line with requirements of ethics committees [51].

**Mathematical and Formula Support (ID: 3.1.7):** Many scientific projects involve mathematical concepts and formulas. GenAI could support researchers by specifying or optimizing
equations [182]. In addition, GenAI could assist in solving mathematical problems [122, 183], constructing mathematical proofs [112, 183], supporting the formulation and derivation of complex formulas [6, 106], checking formulas for consistency and logical flaws [5], and solving equations while providing explanatory reasoning [58].

**Scenario and Simulation Generation (ID: 3.1.8):** GenAI could be used to generate scenarios and simulations [5, 6, 14, 16, 113, 124, 162, 173, 179, 180]. Such simulations could support various domains, such as simulating social interactions and dynamics [16, 98, 173], the effects of drugs [166], materials under different conditions [66], or physical experiments [122]. For these simulations, GenAI could also be used to predict outcomes [124]. Simulations could be particularly valuable when experimental conditions are difficult to investigate under real-world circumstances [16, 73]. GenAI could further support the conceptualization, calibration, and optimization of models, as well as provide feedback on model equations and parameter values [5, 129]. Simulations could also be used to reproduce existing research [16] or to test and refine hypotheses [66].

**Prototyping (ID: 3.1.9):** GenAI could rapidly create functional prototypes and designs [95, 98, 199], thereby significantly accelerating the development process, reducing resource consumption, and making the iteration between concept and implementation more efficient.

**Autonomous Laboratory Experimentation (ID: 3.1.10):** GenAI could be used to autonomously conduct experiments [134]. For example, GenAI could be deployed in combination with robotic systems to perform experiments [71, 122, 150], particularly when those experiments need to be repeated frequently [162].

**Documentation of Laboratory Notebooks (ID: 3.1.11):** Documenting the research process is an essential part of scientific research to ensure the traceability and reproducibility of research results. GenAI could support the documentation process [195]. For instance, all essential steps, configurations, and decisions could be documented [19]. Laboratory notebooks and documentation could also be summarized by GenAI [150, 195].

**Provide Feedback (ID: 3.1.12):** GenAI could function as an advisor by providing feedback on research and experiments [51, 216], for example by pointing out overlooked factors that should be considered [111]. In this way, GenAI could adopt the perspective of an external researcher [175].

### 4.3.2 Methodology - Qualitative Research (ID: 3.2). Question Design, Interview Guide and Observation Grid (ID: 3.2.1)
Formulating appropriate research interview questions is a crucial step in qualitative research to obtain high-quality results that are aligned with the research question. GenAI could generate interview questions from scratch [44, 73, 168, 192, 195, 207], provide suggestions [111, 168], or revise existing interview questions [144]. This could help to avoid biases or content-related gaps [144, 205]. Moreover, this could contribute to more ethical and respectful engagement with participants across diverse cultural backgrounds [144]. GenAI could also generate or revise interview guides or protocols [13, 44, 98, 205]. In addition, templates for observation grids could be generated [111].

**Interview Support (ID: 3.2.2):** Researchers and GenAI could jointly conduct interviews with study participants in order to leverage the strengths of both [13]. GenAI could make and adapt suggestions for interview protocols in real time to respond to the ongoing course of the conversation [144, 192]. The researcher could then freely decide whether to adopt these changes [192].

**GenAI-Based Interviewing (ID: 3.2.3):** GenAI could function as an interviewer, where no human conducts the interview with study participants, but rather the GenAI itself [13, 46, 117, 128, 192, 205]. This could enable a larger number of study participants to be interviewed [117]. During the interview, the GenAI could ask follow-up questions when responses are unclear or when additional details are required [117, 128]. The interview could be conducted via chat [117] or through a voice-based avatar [192].
**Interview Transcription (ID: 3.2.4):** Analyzing interviews requires transcription, which is a time-consuming task. GenAI could automate the transcription process [8, 14, 46, 111, 146, 205].

**Translation of Qualitative Interview Data (ID: 3.2.5):** GenAI could be used to translate interview data that are not understandable to a researcher into a language that is comprehensible to them [85]. This could lead to improved intercultural collaboration in academia.

**Chatting with Qualitative Data (ID: 3.2.6):** GenAI could be used to engage in a dialogue with qualitative data and to pose questions to the data [46, 82, 146]. This could help to generate insights [82], as well as to obtain an initial overview of the data.

**Qualitative Data Summarization (ID: 3.2.7):** GenAI could help to gain an overview of qualitative data by summarizing it [13, 46, 65, 82, 85, 97, 99, 100, 107, 146, 168, 205]. This could support researchers in identifying key themes more quickly [85], navigating large volumes of data, structuring relationships, and guiding the analysis more purposefully. In addition, summaries of clusters could be generated [65, 99].

**Codebook Generation and Refinement (ID: 3.2.8):** Creating a meaningful codebook for coding qualitative data is of fundamental importance in order to derive meaningful insights from the interviews and therefore represents a central challenge for researchers. GenAI could critique an existing codebook [82], generate appropriate codes, or propose new codes that have not yet been considered [39, 65, 79, 82, 98–100, 142, 205, 218]. For these codes, GenAI could provide descriptions explaining what each code represents conceptually [142]. Furthermore, GenAI could examine codebooks to detect duplicates or substantial similarities and overlaps between codes [142].

**Automated Coding and Classification (ID: 3.2.9):** Coding and classifying interviews, field observations, or open-ended survey responses is a time-consuming task for researchers. GenAI could be used to code and classify text documents [16, 39, 44, 46, 47, 61, 79, 82, 91, 97, 98, 102, 107, 112, 114, 127–129, 139, 142, 146, 157, 173, 184, 205, 213, 218]. This could involve inductive [91, 99, 100, 128], deductive [39, 61, 91, 98, 100, 128, 194], or thematic [82, 91, 111, 146, 156, 184] coding. When coding the data, GenAI could either be relied upon exclusively, or a collaborative approach between GenAI and researchers could be adopted [61, 91, 98–100, 128, 144, 146, 213]. Automating the coding process could lead to a reduction in *coder bias* [173]. Automated coding could also enable researchers to quickly test different coding schemes [46].

**Thematic Analysis (ID: 3.2.10):** GenAI could support researchers in conducting a thematic analysis by identifying themes from qualitative data [13, 14, 46, 51, 61, 65, 82, 91, 97–100, 128, 129, 139, 142, 152, 156, 184, 203, 205]. Furthermore, GenAI could identify key sentences and text passages and subsequently group them into thematic categories [13, 100, 157]. GenAI could then summarize the identified themes [13, 142, 151] and generate appropriate labels for these themes [142, 151]. Additionally, GenAI could be used to validate the themes identified by the researchers [14] and highlight potential counterexamples [61].

**Justification and Self-Assessment of GenAI-Supported Qualitative Analysis (ID: 3.2.11):** GenAI could be used not only to analyze qualitative data. It could also be employed to assess the accuracy of labels, codes, and themes [100] or to generate justifications for them [39, 213]. This could help researchers better understand and trace the decisions made by GenAI [39]. In addition, GenAI could be used to provide representative quotations for the identified themes [205].

**Theory Development via Conceptual Analysis (ID: 3.2.12):** GenAI could be used to identify connections between concepts and ideas [82, 157]. In addition, GenAI could suggest suitable frameworks from existing literature for the analysis of qualitative data [82]. Furthermore, GenAI could link identified themes to higher-level theoretical constructs [205] and generate new qualitative models [51]. In this way, GenAI could enable new interpretations [184, 205].
### 4.3.3 Methodology - Quantitative Research (ID: 3.3).
**Survey Generation (ID: 3.3.1):** Survey design has a major impact on the success of a survey. GenAI could be used to create an initial draft of a survey or an entire survey [13, 73, 111]. Based on this draft, researchers could develop the final version. In addition, GenAI could generate an introductory text for the survey [13]. The following use cases cover the different components and phases of a survey.

**Stimuli Generation (ID: 3.3.2):** Surveys often require stimuli such as vignettes or images that fulfill specific properties and are intended to elicit particular reactions or emotions from study participants. Especially in the case of images, it can be challenging to create such stimuli in a natural manner, and GenAI could provide support. GenAI could generate stimuli such as vignettes or images [16, 46, 129, 170, 175] that are designed to elicit specific emotions [16]. A major advantage of GenAI-generated stimuli is that researchers could deliberately design stimuli independently of real-world constraints [46, 170]. This could increase both the validity and the privacy of real individuals [16, 175].

**Item Generation and Refinement (ID: 3.3.3):** Quantitative surveys consist of items that must be answered by study participants. The appropriate selection and wording of these items is crucial for research success. GenAI could improve existing items [220] or provide critical feedback on existing items [51], for example regarding their wording [51, 175], whether items should be omitted or combined [51], or whether items might be interpreted differently by different groups [175]. GenAI could formulate items from scratch while taking previously defined criteria into account [13, 16, 111, 124, 220]. This use case could also be applied to the collection of demographic data [13, 14].

**Survey and Item Pretesting (ID: 3.3.4):** Designing a survey is a complex task that involves many adjustable factors. However, once a survey has been released to study participants, it can no longer be modified. GenAI could pretest the survey and its items by simulating responses [16, 108, 111, 175].

## 4.4 Data Collection (ID: 4)

Data collection refers to the process in which data related to a research question are gathered and prepared. Collecting high-quality data is therefore of crucial importance to enable reliable conclusions. However, this process is often very time- and resource-intensive, making GenAI a potentially valuable tool. Based on our SRL, we identified 12 use cases in which GenAI could support data collection.

**Dataset Creation and Mining (ID: 4.1):** Creating new datasets is an important component of research, but it is often time-, cost-, and resource-intensive. GenAI could effectively collect high-quality data from various sources [1, 12, 38, 53, 112, 128, 129, 187], for example by collecting and preprocessing emails, forum discussions, or social media posts [44, 107]. If GenAI has access to the internet, it could also retrieve information from online sources [186]. In addition, GenAI could suggest additional variables during data collection [111]. Beyond simple collection, GenAI could also support data and text mining for dataset creation [109, 149, 166, 203], thereby eliminating the need for programming skills to apply basic mining techniques [44]. In this context, the source data could be available in different formats or at varying levels of granularity [129]. GenAI could further be used to enter data into forms or databases [16, 105] and to filter data and content [139].

**Dataset Recommendation (ID: 4.2):** For many scientific experiments, datasets already exist that can be used for one's own research. However, obtaining an overview of the available datasets can be challenging. GenAI could support the identification of relevant existing datasets and data sources [5, 151, 174, 186] and assist in selecting the most suitable dataset [19, 56].

**Evaluation of Data Collection Methods (ID: 4.3):** There are numerous ways to collect data, and GenAI could provide guidance on data collection methods [181]. Moreover, GenAI could support researchers by evaluating data collection methods [139] or by recommending the most suitable data collection method based on the research question [54].
**Interactive Participant Data Collection (ID: 4.4):** When data are collected directly from humans, such as through surveys, significant challenges could arise. GenAI could help collect clean and complete data from study participants. GenAI could generate interactive prompts that participants interact with, thereby enabling the collection of data from them [98, 129]. These prompts could be tailored to the specific use case in order to effectively elicit the required data [129]. If necessary, GenAI could also ask follow-up questions to the participants [128]. This approach could thus lead to more detailed responses [128].

**Study Participant Recruitment (ID: 4.5):** Recruiting study participants is a central component of data collection. GenAI could generate suggestions on how to effectively recruit participants for a study based on a description of the research requirements [51, 98]. In addition, GenAI could support the generation of a draft email to recruit participants for a study, with the most important information about the study being provided [51]. Furthermore, inclusion criteria for study participants could be refined using GenAI [13, 98, 150], and participants could subsequently be screened against these criteria [150].

**Database Query Generation and Execution (ID: 4.6):** Databases often contain more data than is required for a specific research task. GenAI could generate queries to retrieve only the data that is actually needed [101, 180]. In general, GenAI could be used for information retrieval [19, 30, 131].

**Data Management and Organization (ID: 4.7):** Data management and the organization of large datasets are important components. GenAI could support data management [205] and organization [54, 202, 213], as well as facilitate structured data storage in versioned repositories [19].

**Data Cleaning (ID: 4.8):** Errors can occur when creating new datasets, and existing datasets often exhibit irregularities and inaccuracies, requiring researchers to perform extensive data cleaning. GenAI could support this data cleaning step [5, 12, 21, 115, 129, 174, 202, 212], for example by enabling data harmonization [129, 187, 207] and dataset systematization [139]. GenAI could standardize different spellings or generate variations to align data across sources [193]. Moreover, GenAI could identify potential errors, inconsistencies, outliers, and noisy data within datasets [54, 73, 115, 117, 129, 181]. In addition, unwanted duplicates could be detected and removed by GenAI [129]. In the context of open user studies, incomplete and low-quality data points could be identified and removed [117]. GenAI could further assess data points, identify generic responses, and treat them differently depending on the use case [117]. Moreover, GenAI could identify missing data points in a dataset [5] and apply appropriate strategies to handle them [16, 46, 115, 129, 181].

**Data Formatting (ID: 4.9):** Existing datasets are available in various formats that cannot be used directly for analysis. Researchers therefore have to manually, or with software tools, convert them into suitable formats, which is time-consuming. GenAI could support data formatting [12, 105, 115, 153, 195, 212], structuring [174], and transformation [181]. For example, an existing data set could be converted into a different format [5, 106] or GenAI could output directly in a specific format [212]. Moreover, GenAI could be used to sort data [144].

**Bias Detection and Quality Check (ID: 4.10):** When working with an existing dataset, the quality of the data must first be assessed. However, even when a dataset is created manually, quality issues or biases in the data can arise. GenAI could review a dataset to ensure data quality [5, 38, 117] and to examine whether biases are present in the data [48, 85, 115]. In addition, inconsistencies within the data could be identified [170].

**Data Annotation and Labeling (ID: 4.11):** In research, labeled or annotated data are often required. When such data are created by humans, this can be a very time-consuming task. GenAI could support researchers in this context and take over these tasks [5, 13, 16, 19, 23, 39, 46, 54, 61, 79, 87, 91, 106, 114, 128, 129, 141, 163, 173, 196, 217]. Such labeled data could, among other uses, be employed for fine-tuning a model [141]. GenAI could also be used to assess the quality of human-labeled data [141]. This use case also includes Named Entity Recognition [55].
**Synthetic Data Generation (ID: 4.12):** Collecting real-world data is a major challenge that is often associated with high costs and substantial time investment. However, GenAI could be used to generate synthetic data, which could subsequently be employed for analysis [9, 16, 85, 106, 108, 114, 115, 129, 139, 145, 147, 170, 173, 175, 181, 187, 195]. This enables the substitution of human participants in surveys [13, 115, 124], potentially extending to the simulation of entire patterns of human behavior [13, 16, 59, 106, 120, 124, 173, 175]. Because the data are synthetic, specific requirements could be imposed on the data [16, 99, 173, 175], allowing hypotheses to be tested under varying conditions [73]. The generated data could be qualitative, such as interview data [13, 44, 82, 99, 173, 205], or quantitative [13, 124, 175, 181], and could be represented as text or images [181]. The synthetic data could either be generated based on the training data of the GenAI model or by providing existing datasets to the GenAI within the instruction to generate similar data [82, 192, 205]. This use case also applies to generating additional data points to complement an existing dataset [13]. This approach could lead to a significantly more diverse sample [13, 16, 82], particularly with respect to, for example, marginalized groups [59], and could increase the overall sample size [173], for instance by mirroring the profiles of real study participants [10, 13]. Synthetic data could be particularly useful when it is difficult to collect or publish the relevant data [129, 181], for example because only a small number of individuals are eligible or because they are hard to reach [13], when accessing the data would be too dangerous [16], or when it is not possible to collect real data at all [129]. Moreover, the use of synthetic data could reduce the amount of truly sensitive data while simultaneously preserving the anonymity of the underlying individuals [82, 170]. In this context, synthetic data could be generated on the basis of real data in order to effectively mask them [170]. The data could also initially be used solely to test the research method, for example by experimenting with different conditions, before data are collected in a real-world setting [13, 16, 23, 51, 98, 111, 175]. However, the generated data could be used not only for analysis, but also to train models [46] or to reproduce research findings [59, 108].

## 4.5 Programming (ID: 5)

Programming plays a key role in many research projects. Advances in GenAI could allow more researchers without a computer science background to engage in programming tasks. Our SLR identified 13 uses cases where GenAI could support programming.

**Requirements Analysis and Software Design Support (ID: 5.1):** GenAI could support researchers in requirements analysis and software design [12, 169].

**Code Generation (ID: 5.2):** GenAI could generate software code and is not limited to a single programming language, but could write code in various languages such as Python or R [5, 6, 9, 21, 28, 30, 31, 41, 42, 56, 58, 66, 70, 75, 77, 81, 82, 89, 90, 92, 98, 106, 112, 115, 121, 129, 139, 152, 155, 162, 166, 167, 169, 181–183, 186, 193, 202, 203]. To achieve this, researchers could describe the functionality of the program, the method, etc. in natural language and specify the desired programming language, after which GenAI generates the corresponding code [106, 129, 166]. In addition, mathematical formulas could be provided to GenAI as input and subsequently translated into the respective programming language [5, 182]. GenAI could write new code from scratch; however, if existing code is provided, it could extend it with additional functionalities or complete it [31, 182]. Furthermore, GenAI could edit and modify existing code [186]. The generated code could be used, among other things, for simulations (ID: 3.1.8) or data analyses (ID: 6.8) [30, 41, 56, 82, 98, 129, 139]. GenAI-generated code could be particularly useful when code needs to be written in an unfamiliar programming language [129] or when researchers have only limited programming experience [129, 139, 152, 166, 169, 202]. Moreover, GenAI could be used as a brainstorming partner to develop new programming solutions [166].
**Documentation, Library, and API Recommendation and Explanation (ID: 5.3):** GenAI could support researchers in understanding documentation, libraries, resources, and APIs. Instead of looking up the function of a package in documentation, GenAI could be prompted to explain the function [129, 166]. Furthermore, GenAI could point to helpful documentation, libraries, resources and APIs that could be used to solve a programming problem [129, 166]. This support in information retrieval could lead to a reduction in development time for researchers [166].

**Algorithm and Data Structure Selection (ID: 5.4):** GenAI could support the identification of suitable algorithms for a given use case [181]. To this end, advantages, disadvantages, and potential implementation details could be highlighted [181]. By selecting appropriate algorithms and data structures, GenAI could lead to improved code efficiency and performance [186].

**Code Improvement (ID: 5.5):** GenAI could be used to perform code refinement and optimization [31, 91, 166, 207] and to improve code quality and maintainability [31, 166]. This could, for example, be achieved through code refactoring [166, 186]. Overall, this could lead to more efficient and more readable code [166].

**Code Validation and Debugging (ID: 5.6):** GenAI could be used to review code, identify bugs within the code, and subsequently debug them [5, 13, 30, 31, 56, 58, 75, 98, 106, 155, 186, 191]. As input, the code and/or the corresponding error message could be provided [75, 106, 186], or the problem could be described in natural language [166]. Subsequently, GenAI could not only directly modify the code but also provide explanatory guidance to find a solution [166, 186].

**Software Testing (ID: 5.7):** Writing tests is an important but time-consuming and complex task. GenAI could suggest suitable libraries or tools for this purpose or provide insights into different testing approaches [166]. GenAI could also consolidate the outputs of different software tests into a single message, enabling the GenAI to generate improved code based on this information [92].

**Documentation Writing (ID: 5.8):** Comprehensive documentation can improve maintainability and reusability [129, 166], which in turn leads to a reduction in the development time [129]. In addition, collaboration among multiple researchers could be enhanced [166], and reviewers could understand the code more quickly. By analyzing the code, GenAI could generate comprehensive documentation [129, 186] that includes, for example, explanations, usage examples, and API references [166]. In some cases, code developed at a research institution must be reimplemented when the original developer leaves the institutions and the code is insufficiently documented [129], which can lead to delays in research.

**Code Translation Across Programming Languages (ID: 5.9):** GenAI could translate existing software code into another programming language [5, 31, 106, 186]. One application scenario is when a required package or library is not available in a particular programming language [186] or when code snippets from another project are intended to be reused [106].

**Script Reuse and Adaptation (ID: 5.10):** GenAI could support the rewriting of existing code so that it can be reused for a related task or data analysis [56]. This would save researchers time by reducing the need to manually rewrite code.

**Natural Language Translation (ID: 5.11):** GenAI could translate existing code into natural language [186]. This could be used to summarize the code [31] or to explain existing code to researchers [106], either by providing a line-by-line explanation or by describing the functionality of specific code segments [186].

**Code Execution and Simulation (ID: 5.12):** GenAI could simulate code [5] and execute code when it is part of a larger system with a code interpreter [30, 66, 71, 75].

**Programming Skill Development (ID: 5.13):** GenAI could be used to help researchers improve their programming skills [166] and to provide programming guidelines [5].
## 4.6 Data Analysis (ID: 6)

The correct analysis of qualitative and quantitative data is of fundamental importance in order to make informed statements and decisions and to maintain the integrity of research. Through our SLR, we were able to identify 12 use cases in which GenAI could support data analysis.

**Data Analysis Planning (ID: 6.1):** GenAI could be used to generate a data analysis strategy [56, 75, 124] or to create appropriate validation and testing frameworks [48, 179].

**Data Understanding Support (ID: 6.2):** Understanding a dataset is essential in order to perform analyses. GenAI could support data exploration [41]. For example, GenAI could generate summaries of the data [5, 90, 101, 151, 195, 205], or present the dataset in an understandable format [202]. GenAI could also provide useful background information [69] or translate acronyms used [95].

**Analysis Method Selection (ID: 6.3):** Selecting appropriate analysis methods is fundamental to proper data analysis. GenAI could suggest and evaluate different analytical methods [9, 75, 82, 129, 139, 193] that align with the research questions and variable specification [51, 203].

**Analysis Method Explanation (ID: 6.4):** Correctly applying statistical methods can be challenging. GenAI could explain different methods and analysis tools, such as RStudio or Excel, and provide step-by-step instructions with examples [51, 56, 139, 205].

**Data Analysis Tutoring (ID: 6.5):** GenAI could act as a tutor for data analysis [106] by providing suggestions on how to handle data [9], proposing evaluation criteria [56], or offering recommendations for improved model analysis and verification processes [5].

**Computational Task Execution (ID: 6.6):** GenAI could be used for computational tasks [70, 178] and perform basic calculations and analyses, such as descriptive and summary statistics [13, 51]. In addition, sensitivity analyses could also be performed [5].

**Pattern, Trend, and Relationship Identification (ID: 6.7):** Identifying correlations, patterns, trends, relationships, and similar structures within large datasets can be challenging. GenAI could provide support at this stage by uncovering such structures [8, 14, 44, 46, 47, 54, 59, 69, 82, 90, 91, 105, 107, 129, 131, 146, 153, 157, 165, 166, 173, 179, 181, 183, 193, 203]. To facilitate the identification of patterns, GenAI could generate appropriate visualizations [152]. For qualitative data, GenAI could help identify patterns in textual data [44, 82, 91, 92, 99, 142] and group similar content [85, 99]. This could be applied across multiple documents [91].

**Application of Different Analytical Methods (ID: 6.8):** Correctly applying different analytical methods is challenging. GenAI could apply various statistical methods to the data [5, 13, 34, 107], such as linear regression, ANOVA, chi-square, or forecasting [55, 64, 92]. In addition, GenAI could generate formulas for specific analyses [56].

**Sentiment and Position Analysis (ID: 6.9):** GenAI could be used to identify, assess, and classify sentiments and emotions in textual data [34, 44, 54, 69, 87, 91, 97, 100, 106, 114, 127, 156, 166, 173, 202, 203, 207]. In addition, positions and viewpoints could be extracted from texts, such as political stances [114]. This could lead to a reduction in human workload and to fewer biases caused by individual subjectivity.

**Hypothesis Testing (ID: 6.10):** GenAI could be used to create hypothesis testing plans [92] and to test hypotheses by analyzing data and results [56, 73, 124, 188, 192].

**Result Interpretation and Discussion (ID: 6.11):** GenAI could interpret the results or support the interpretation process [5, 15, 30, 41, 56, 58, 75, 81, 92, 95, 101, 122, 149, 152, 165, 166, 179, 181, 187, 193, 203, 213, 218], e.g., by presenting multiple alternative interpretations [131]. This could subsequently facilitate discussion of the results [58, 181].

**GenAI-Based Evaluation (ID: 6.12):** GenAI could be used by researchers as automated evaluators that assess the quality of research artifacts or GenAI outputs [145, 200]. Such evaluations
could be reference-free or reference-based [200] and complement or replace human evaluations or task-specific metrics [145].

## 4.7 Writing (ID: 7)

Writing papers, and thus communicating research results, represents the final step of a research project. Yet, for many researchers, this task poses a major challenge [129], particularly in technical fields. Through our SLR, we identified 25 use cases in which GenAI could support researchers in writing papers.

**Structuring, Planning, Outlining, and Drafting of Texts (ID: 7.1):** GenAI could be used to create or revise drafts [7, 9, 14, 15, 51, 54, 58, 77, 78, 98, 115, 123, 129, 144, 153, 156, 158, 164, 166, 178, 193, 195, 202] and outlines [37, 58, 68, 73, 107, 120, 149, 153, 161, 168, 171, 181, 186] for entire papers or specific sections. In addition, GenAI could suggest which topics should be addressed on particular chapters [197]. Researchers could provide relevant constraints as input such as target length or specific themes [58].

**Academic Text Generation (ID: 7.2):** GenAI could support researchers in generating text [21, 22, 25, 28, 35, 41, 58, 98] such as abstracts [9, 15, 21, 45, 58, 107, 153, 158, 181], conclusions [38, 92, 123, 153], introductions [70, 107, 116, 123, 153], methodology sections [9, 15, 92, 116, 202], results descriptions [15, 92], and discussions of results [107, 116]. Input for the generated text could include, for example, rough notes [129], an existing paper draft [107], collected data [7, 9, 41, 92], an existing outline [129, 202], the abstract [95], or the title [116]. Furthermore, it could transform tables into running text [107, 153]. User could predefine requirements such as text length [107, 181], level of formality [186], or writing style [69, 107, 167]. Even if GenAI is not used to generate entire sections or chapters, it could serve as a formulation aid [129]. This could help overcome writer's block and "blank-sheet syndrome", since an initial draft is already available for authors to refine [9, 153, 186]. Through fine-tuning, a model could even learn an individual's writing style to better preserve their personal voice [107]. This could be particularly valuable in cases of illness, physical limitations, or for non-native English speakers [123].

**Title and Abstract Generation (ID: 7.3):** GenAI could generate suitable and concise paper titles, e.g., based on the abstract [9, 92, 95, 106, 151, 153, 171, 181] as well as appropriate keywords [181].

**Correction of Spelling, Grammar, and Punctuation (ID: 7.4):** GenAI could support researchers in detecting and correcting errors in spelling, grammar, and punctuation [14, 25, 32, 52, 57, 78, 90, 98, 107, 149, 164, 171, 176, 178, 181, 193, 202, 203].

**Example Generation (ID: 7.5):** GenAI could be used to generate examples [14, 90, 186]. This could help explain complex scientific topics through understandable examples, thereby making these topics more accessible [14]. The examples could, e.g., be used to enhance the introduction of a paper by adding an interesting illustrative example [24].

**Figure and Illustration Generation (ID: 7.6):** By providing a description in natural language, GenAI could generate illustrative and explanatory graphics [90, 151, 202], for example to visualize concepts [202] or to create flowcharts [95].

**Data and Result Visualization (ID: 7.7):** GenAI could generate tables, charts, graphs, and diagrams from natural language input to visualize research results [13, 21, 28, 51, 75, 81, 92, 95, 98, 106, 121, 129, 150, 151, 166, 174, 181, 203].

**Argumentation Development (ID: 7.8):** GenAI could be applied at various stages of argumentation to support researchers. On the one hand, it could be used to generate new arguments [57, 58, 181]. GenAI could also be employed to identify unclear and weak arguments [181], detect arguments that are difficult to follow [106], and since GenAI is capable of adopting both sides of a discussion, it could effectively generate counterarguments [106]. In addition, GenAI could be used to assess the structure of arguments [193].
**Misleading Statement Detection and Correction (ID: 7.9):** GenAI could support the detection and correction of misleading statements [154], thereby improving clarity of information.

**Identification of Implications and Limitations of Studies (ID: 7.10):** GenAI could be used to identify implications and limitations of a study [48, 153, 181, 193], which could contribute to increasing transparency in research.

**Future Work Recommendation (ID: 7.11):** GenAI could help suggest future work and experiments [95, 166, 183, 197], e.g., based on the study's own findings [124].

**Fact Checking (ID: 7.12):** When a paper or sections of a paper are provided to the GenAI as input, the GenAI could verify the correctness of the statements contained therein [105, 123].

**Rewriting of Text (ID: 7.13):** GenAI could assist in rewriting existing texts [42, 178] to overcome weaknesses in the writing, by producing texts in a different style [69, 170], or, when appropriately trained, by emulating the individual style of specific researchers [107], or by paraphrasing content [57, 58, 116, 171].

**Ensuring Consistency in Academic Writing (ID: 7.14):** GenAI could help identify different formulations in papers and harmonize them to ensure consistency [7, 193]. This leads to greater consistency within the paper, making it easier to understand and follow. It could be useful when multiple researchers are working on a document but have different writing styles [193].

**Reference Management (ID: 7.15):** GenAI could support reference management, for example by revising and organizing references [54, 149, 178]. In addition, GenAI could assist with citation management by handling the recording and management of citations [14, 89].

**Citation and Reference Support (ID: 7.16):** GenAI could generate appropriate citations [41, 51, 92, 116, 135, 139, 153, 178, 197, 213]. Based on the given manuscript draft it could identify passages where references are missing [70, 181, 197] and directly suggest relevant sources. Moreover, GenAI could review existing references and search for alternatives that could be more suitable or more frequently cited [70]. It could also identify additional sources to strengthen specific arguments [193]. Furthermore, GenAI could be used to assess the quality of cited sources, verify their existence [153, 186], or identify import papers that have not yet been included [193]. In doing so, it has the potential to make researchers' work more efficient [70].

**Technical Writing in LaTeX and Markdown (ID: 7.17):** Many journals and conferences provide LaTeX templates for paper submission, especially in disciplines like math, physics, engineering and computer science. However, not all researchers are professional-level proficient in LaTeX. GenAI could support the writing of LaTeX-Code [92, 106, 153, 178, 181], for example, by generating tables [106]. When provided with existing LaTeX code, it could assist in debugging [181] and optimizing the code [178]. Moreover, GenAI could adapt the code to comply with the requirements of the target journal or conference. If connected to the internet, it could directly retrieve the guidelines from the publisher's website [178]. Comparable support is also available for Markdown [215].

**Formatting of Texts (ID: 7.18):** GenAI models could be used for formatting [107, 123, 129, 149]. This could be particularly helpful when a paper is submitted to different conferences and journals, as they have varying formatting requirements, which could save time [129, 151].

**Formatting and Generation of Bibliographies and References (ID: 7.19):** GenAI could assist in formatting bibliographies and references in papers [115, 178]. It could also assist in converting references into different citation styles, such as APA or Chicago, required by specific outlets, and it could directly translate the references from text into BibTeX format [102, 106, 150, 153, 181, 193]. This is particularly useful when a paper is to be submitted to a different outlet than originally planned, since researchers often spend considerable time creating and adjusting references [89, 129]. Moreover, GenAI could help reduce errors, thereby contributing to the integrity of science [89].

**Translation of Texts (ID: 7.20):** GenAI could be used to translate written text into another language [8, 28, 32, 35, 51, 58, 64, 69, 70, 73, 90, 105, 106, 114, 115, 132, 149, 150, 152, 165, 178, 203, 215].
[215]. This would enable non-English-speaking researchers to publish their research findings in international journals and conferences, thereby overcoming language barriers. Furthermore, GenAI could be used to translate English-language literature into the researchers' native languages.

**Text Quality and Readability Improvement (ID: 7.21):** The use of GenAI could help researchers substantially improve the quality, clarity, and readability of scientific texts. GenAI could overall enhance the text quality and comprehensibility, as well as adapt or simplify formulations [14, 51, 58, 78, 92, 106, 140, 151, 168, 178]. GenAI could detect and remove duplicate content [15] and function as a language editor [58] by providing feedback on style, writing quality, word choice, and expression [42, 98, 105, 106, 144, 149, 151, 164, 178, 193]. Furthermore, GenAI could suggest synonyms [14, 38, 78, 98, 164], improve syntax [45, 178, 181], and clarify domain-specific terminology [92]. GenAI could also optimize text structure, for example by suggesting the reorganization of sections, rearranging paragraphs, or combining them to improve textual flow [123, 149, 171, 178]. Furthermore, GenAI could improve the consistency of content [144], strengthen the logical coherence of arguments [151], and adapt stylistic expression [150]. This includes optimizing sentence structure [14, 78, 89]. Through these measures, GenAI could support a coherent and fluent reading flow, enhance overall style and comprehensibility, and improve cohesion [14, 42, 44, 90, 149, 151, 153, 171, 178], and help articulate results more precisely and clearly [14, 51, 78, 89, 153, 181, 193]. Overall, GenAI could be used to linguistically refine and polish a manuscript [73, 75, 164, 202], with additions that could be tailored to specific target audiences [51].

**Style Checking (ID: 7.22):** GenAI could be used to check a document or text against specific requirements, such as the length of the text or the formatting of references [92, 149, 171]. If a journal has such requirements, GenAI could be applied—provided that both the requirements and the document are available—with the instruction to compare the requirements to the document and highlight any errors. This use case could be employed not only by authors but also by outlets to automatically review submissions for formal adherence to guidelines [181].

**Shortening of Papers and Abstracts (ID: 7.23):** Journals and conferences have different requirements regarding the length of papers and abstracts. GenAI could assist in shortening scientific texts so that they meet these requirements while preserving as much as content depth as possible [129, 153, 161, 171, 181]. This could be particularly useful when a paper is submitted to a different outlet than originally anticipated.

**Scientific Paper Review (Pre-Submission) (ID: 7.24):** GenAI could provide feedback prior to submission to a journal or conference [42, 45, 58, 71, 90, 92, 106, 107, 129, 131, 164, 166]. GenAI could evaluate the quality and completeness of the manuscripts [193]. In addition, GenAI could highlight potential errors or inconsistencies [181]. Furthermore, the manuscripts could be reviewed from multiple perspectives, enabling targeted improvement suggestions [166]. Feedback could address, for example, the structure, the abstract, or the entire manuscript [9, 51, 58, 193]. Moreover, GenAI could provide feedback on the clarity, conciseness and the use of domain-specific terminology [51, 193], thereby reducing the potential for misunderstandings prior to the official peer-review process [193]. A GenAI-generated pre-submission review could therefore improve manuscript quality before submission [166], which could in turn accelerate the publication process [166]. When the target outlet is specified, the review could also be tailored accordingly [9].

**Journal and Conference Recommendation (ID: 7.25):** Nowadays, there is a vast number of journals and conferences, with diverse requirements regarding topics, methodologies, and scientific quality. GenAI could assist in identifying the most suitable journal or conference for a paper, which could be particularly helpful to early-career researcher in placing their work appropriately [129].
# 5 Discussion

## 5.1 Dimensions of Human-GenAI Interaction in Research

Reflecting on the SLR, we observe two higher-level dimensions, differentiating how GenAI could be used by researchers in their workflows: *GenAI-led* vs. *Researcher-led* and *Once* vs. *Iterative*.

*GenAI-led* means that the model provides the initial output, such as research questions, which then serve as the basis for further work. In contrast, *Researcher-led* means that the researcher provides the initial content, such as an already formulated research question, and the model is tasked with verifying, improving, or criticizing this content. In both approaches, the researcher retains control over what content is used for the next steps of the research. In addition, the researcher remains responsible for the quality of the generated content.

The distinction between *Once* and *Iterative* lies in how often the model is expected to generate responses. In the *Once* approach, the model is prompted a single time, and the resulting content is subsequently used by the researcher. In the *Iterative* approach, a dialogue between the model and the researcher or among multiple agents (e.g., multiple instances of a model with distinct roles) emerges, allowing for follow-up questions, comments, and critique that iteratively refine and improve the output. Which approach is preferable cannot be determined universally, as it depends on the specific use case.

While application scenarios can be conceptualized on these dimensions, real-world workflows may employ hybrid forms, combining elements of either end of the spectrum, or points in between.

## 5.2 The Evolving Role of GenAI in Scientific Research

The European Union, with its *European Strategy for Artificial Intelligence in Science* [62] presented in October 2025, and the United States, with the *Genesis Mission* [198] presented in November 2025, will invest substantially in exploring how GenAI can be used to advance scientific discovery.

In the SLR, we present 118 use cases that illustrate how GenAI could be employed by researchers in their workflows. However, the question arises as to whether the use of GenAI complies with scientific requirements regarding ethics, integrity, and quality. Since GenAI is based on probabilistic approaches, reproducibility is not guaranteed to the same extent as with deterministic approaches. Moreover, there is a risk of confabulation, i.e., GenAI provides incorrect information (with plausible appearance). If such erroneous information is adopted by researchers without verification, this could compromise the integrity of scientific research.

The intensive use of GenAI by researchers may also lead to a homogenization of research. This occurs, on the one hand, because researchers rely on the same easily accessible data sources, and on the other hand, because existing inequalities in the training data may be amplified when new models are trained. GenAI is particularly strong at reproducing existing and well-known patterns, but less capable of generating innovative and original content. An illustrative example is the use case *Identification of Theories, Frameworks, and Concepts*. In current research, certain theories are disproportionately applied, and this is reflected in the training data of models. Due to the probabilistic nature of generative models, it is likely that these theories are more frequently output than those that have been used less often so far. This may either be because the theories are less suitable to explain the phenomenon or because they are relatively new. However, if researchers rely solely on the model's output for identifying appropriate theories, these theories will become even more prevalent in the training data of subsequent models, further increasing the likelihood that they will be output again – thus creating a feedback cycle. This effect could become particularly pronounced if not only ideas are adopted from the models, but entire text sections or even full papers are generated. Although research thrives on referencing established theories that have proven valid across numerous studies, the intensive use of GenAI by researchers could mark a first
<table><caption>Table 2. Stages of GenAI involvement in scientific research.</caption><thead><tr><th colspan="3">Human-GenAI Collaboration</th><th>GenAI Only</th></tr><tr><th></th><th>GenAI-led</th><th>Researcher-led</th><th>GenAI Research</th></tr></thead><tbody><tr><th>Once</th><td>GenAI generates a single initial output (e.g., research questions, hypotheses) → researcher continues with this basis.</td><td>Researcher provides initial content (e.g., research question) → GenAI reviews or improves once (e.g., editing, checking consistency).</td><td rowspan="2">GenAI conducts independent research with minimal or no human intervention, including literature research, data collection, data analysis, interpretation of results, and writing a paper.</td></tr><tr><th>Iterative</th><td>Dialogue or multi-agent exchange where GenAI drives exploration (e.g., iterative generation of hypotheses).</td><td>Interactive refinement where researcher leads (e.g., iteratively improving methods, discussing results).</td></tr></tbody></table>

step toward diminishing innovation. At its core, science also depends on maintaining diversity in the choice of theories, concepts, and methodologies in order to obtain a comprehensive view.

The scientific community must establish *norms and guidelines*, defining when and how GenAI may be used in research. However, as yet there are no standardized guidelines from journals and conferences on how GenAI could be used in a paper. Therefore, authors should always review the respective guidelines before submitting a paper. A unified rule book within the scientific community would provide authors with certainty that they are allowed to use GenAI in research. Such a unified rule book could also mandate that the use of GenAI in a paper be documented and reported in a standardized manner, in order to increase transparency.

Currently, there are two major trends in research regarding how GenAI should be used in scientific work. On the one hand, there are new conferences that explicitly focus on conducting research using GenAI, such as the "Open Conference of AI Agents for Science 2025" [3]. At this conference, the primary author has to be an GenAI, and the reviews were mostly written by GenAI [3]. In addition, there is a growing number of minitracks, workshops, and challenges addressing this topic [4, 93, 189]. On the other hand, there are increasingly strong voices arguing that GenAI should not be part of research processes [96]. In this paper, we present only isolated use cases; however, a combination of these use cases within a larger system is possible, potentially enabling fully automated research. There are already research projects that have developed exactly these kinds of systems [67, 121].

Therefore, the fundamental question arises as to what role GenAI will assume in science in the future. In Table 2, we distinguish between two stages: *Human-GenAI Collaboration*, which includes our two dimensions from the previous section, and *GenAI Only*. In contrast to *Human-GenAI Collaboration*, in a *GenAI Only* scenario, humans would no longer be responsible for the quality of research. Instead, this responsibility would be transferred to the GenAI.

These observations lead to big questions regarding the future of GenAI in science: What does humanity need from science and how do we best achieve this, by human-GenAI collaboration or GenAI-only scientific discovery? Will GenAI-driven research advance to the point that humans in the loop can no longer contribute to advances and only slow down the speed of progress, as suggested in the AI-2027 [104] report? Or is integrity, ensured by personal responsibility of human researchers, such an integral aspect of effectively growing the body of scientific knowledge, that the most successful mode of interaction is human-GenAI collaboration with humans retaining personal responsibility? These questions are unanswered at the time of writing, and indicate strongly that a large-scale discussion about the future of science in the age of GenAI is pressing.
## 5.3 Limitations & Future Work

The methodological design decision to include grey literature in the analysis provides the advantages described in the methodology, but it also has the drawback that the quality of the papers cannot always be ensured. This SLR identifies use cases from mentioned uses and use cases in the existing literature. However, in some cases, only the use cases themselves are mentioned without providing further information that would offer additional context on how exactly GenAI could support researchers. Further research should analyze these use cases in more depth, for example by developing concrete systems, but also by conducting empirical, behavioral, or qualitative studies that examine how and under which conditions GenAI can meaningfully support researchers. Future work could build on the list of identified use cases and examine which of these are already supported by systems or empirical evidence. This could facilitate researchers in the active application of these use cases in their work and in demonstrating that the results meet scientific standards. In addition to conducting active research, researchers have other responsibilities such as reviewing papers, writing research proposals, or preparing presentations. These tasks were not within the scope of this paper and should be identified in future work. Since GenAI in research is a rapidly growing field, this list of use cases does not represent a final or exhaustive overview. It should be revised and extended at regular intervals to maintain a comprehensive perspective.

## 6 Conclusion

Research is an important part of modern society and is irreplaceable in various areas of life. GenAI offers various possibilities to support it throughout the entire research process. This is particularly important because cycles in science are becoming shorter and shorter, increasing the pressure to stay up to date with the latest research results and to generate new findings. We identified a total of 118 use cases for how researchers could use GenAI. This shows that science has already identified many potential applications, from ideation to the writing of papers. However, the limitations of GenAI, such as biases, reliability, and accountability, must always be considered. These are of crucial importance in science in particular, both to ensure the integrity of research and to enable far-reaching decisions to be made based on research results. Researchers should therefore always use GenAI in a reflected manner. In the coming years, the role of humans in science could change from knowledge production to more critical evaluation of generated content – or in more radical ways. The next few years will show which of the use cases we identified will become scientific standard and which ones the community will consider less helpful or inappropriate to use.

## Acknowledgments

In this paper, GenAI was used for the following use cases: 2.8, 2.10, 2.14, 7.1, 7.4, 7.7, 7.14, 7.17, 7.20, and 7.24. FhGenie [208], Gemini, Claude, and ChatGPT were used, each with different models.

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# Appendix

## A Use Case Overview with Reference Classification

The following table provides an overview of all 118 identified use cases, organized by category. References are classified as either peer-reviewed or non-peer-reviewed literature, where non-peer-reviewed literature includes arXiv preprints, SSRN working papers, bioRxiv/chemRxiv preprints, technical reports, website citations, editorials, and comments.

<table><caption>Table 3. Use Cases for GenAI in Research with Reference Classification</caption><thead><tr><th>ID</th><th>Use Case</th><th>Peer-Reviewed</th><th>Non-Peer-Reviewed</th></tr></thead><tbody><tr><td colspan="4"><strong>Category 1: Ideation</strong></td></tr><tr><td>1.1</td><td>Development and Refinement of Research Ideas</td><td>[5, 14, 48, 106, 119, 123, 135, 137, 149-151, 153, 178]</td><td>[19, 28, 51, 71, 98, 107, 112, 120, 121, 129, 143, 160, 183, 193, 213]</td></tr><tr><td>1.2</td><td>Brainstorming Support</td><td>[1, 37, 48, 54, 75, 78, 95, 106, 115, 119, 123, 125, 137, 157, 171]</td><td>[19, 51, 98, 107, 121, 132, 143, 166, 181, 191]</td></tr><tr><td>1.3</td><td>Formulation of Research Problems and Goals</td><td>[5]</td><td>[71, 92, 193]</td></tr><tr><td>1.4</td><td>Formulation of Research Questions</td><td>[2, 5, 16, 21, 45, 46, 48, 56, 82, 119, 139, 149, 153, 171, 195]</td><td>[35, 51, 92, 98, 107, 120, 129, 155, 193, 213]</td></tr><tr><td>1.5</td><td>Hypothesis Generation and Evaluation</td><td>[7, 14, 21, 30, 44, 56, 66, 72, 82, 90, 115, 203]</td><td>[32, 35, 41, 58, 71, 73, 81, 92, 97, 112, 121, 122, 124, 129, 134, 155, 166, 182, 188, 213]</td></tr><tr><td>1.6</td><td>Research Agenda Development</td><td>[179]</td><td>[71]</td></tr><tr><td colspan="4"><strong>Category 2: Literature Review</strong></td></tr><tr><td>2.1</td><td>Outlet Selection Support for Literature Reviews</td><td>-</td><td>[193]</td></tr><tr><td>2.2</td><td>Literature Search</td><td>[5, 9, 11, 14, 37, 54, 70, 77, 85, 88, 105, 106, 108, 119, 130, 131, 135, 151, 153, 171, 178, 179, 186]</td><td>[19, 35, 51, 92, 102, 120, 121, 129, 140, 143, 165, 166, 183, 193]</td></tr><tr><td>2.3</td><td>Literature Organization and Management</td><td>[11, 70, 89, 139, 178]</td><td>-</td></tr><tr><td>2.4</td><td>Search String and Search Strategy Development</td><td>[37, 77, 83]</td><td>[92, 102, 112, 160]</td></tr><tr><td>2.5</td><td>Title and Abstract Screening</td><td>[11, 77, 130, 139, 212]</td><td>[112]</td></tr><tr><td>2.6</td><td>Text-to-Speech Conversion for Scientific Papers</td><td>[89]</td><td>[51]</td></tr><tr><td>2.7</td><td>Note-Taking and Organization of Information</td><td>[37, 149, 171]</td><td>-</td></tr></tbody></table>

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<table><caption>Table 3 – Continued from previous page</caption><thead><tr><th>ID</th><th>Use Case</th><th>Peer-Reviewed</th><th>Non-Peer-Reviewed</th></tr></thead><tbody><tr><td>2.8</td><td>Single-Paper and Section Summarization</td><td>[6, 7, 11, 14, 15, 21, 42, 70, 75, 106, 109, 114, 115, 135, 139, 149-151, 153, 168, 171, 178, 186, 195, 200, 202, 205, 207, 212]</td><td>[19, 22, 25, 35, 41, 58, 112, 129, 155, 161, 166, 170, 193, 217]</td></tr><tr><td>2.9</td><td>Discipline-Specific Question Answering</td><td>[21, 30, 37, 64, 66, 70, 77, 95, 150, 167]</td><td>[22, 51, 58, 170, 188, 197, 215]</td></tr><tr><td>2.10</td><td>Chat with a Document</td><td>[14, 64, 135, 186, 202, 207]</td><td>-</td></tr><tr><td>2.11</td><td>Information Extraction from Literature and Databases</td><td>[11, 14, 21, 44, 46, 72, 74, 95, 106, 118, 130, 135, 139, 150, 178, 202, 203, 212, 216]</td><td>[33, 35, 79, 112, 129, 134, 166, 182, 213, 215]</td></tr><tr><td>2.12</td><td>Concept Mapping and Coding</td><td>[130, 179]</td><td>[58]</td></tr><tr><td>2.13</td><td>Identification of Theories, Frameworks, and Concepts</td><td>[14, 149, 153, 169, 179]</td><td>[51, 120]</td></tr><tr><td>2.14</td><td>Explanation of Scientific Texts and Concepts</td><td>[14, 21, 37, 38, 106, 125, 139, 153, 171, 178, 179, 202]</td><td>[51, 129, 166]</td></tr><tr><td>2.15</td><td>Conceptual and Interdisciplinary Relationship Discovery</td><td>[149, 179]</td><td>[129, 166, 197]</td></tr><tr><td>2.16</td><td>Theorizing</td><td>[44, 56, 179]</td><td>[51, 97, 107]</td></tr><tr><td>2.17</td><td>Paper and Content Comparison</td><td>[30]</td><td>[213]</td></tr><tr><td>2.18</td><td>Critical Appraisal of Scientific Literature</td><td>[27, 70, 153, 179]</td><td>[71]</td></tr><tr><td>2.19</td><td>Multi-Paper Summarization and Topic Overview Generation</td><td>[7, 9, 14, 16, 37, 48, 70, 95, 106, 108, 119, 137, 139, 149, 151, 153, 186, 202, 207]</td><td>[51, 52, 58, 60, 71, 73, 98, 120, 129, 140, 165, 166, 181]</td></tr><tr><td>2.20</td><td>Literature Review Writing</td><td>[6, 16, 21, 27, 45, 70, 77, 90, 108, 109, 130, 135, 137, 153, 158, 179, 205]</td><td>[22, 51, 58, 69, 97, 98, 129, 155, 170, 193]</td></tr><tr><td>2.21</td><td>Research Trend Identification and Summarization</td><td>[11, 89, 216]</td><td>[112, 129]</td></tr><tr><td>2.22</td><td>Research Gap Identification and Analysis</td><td>[11, 14, 37, 90, 135, 151, 153, 156, 179]</td><td>[51, 59, 60, 107, 111, 129, 166]</td></tr><tr><td colspan="4"><strong>Category 3.1: Methodology – General</strong></td></tr><tr><td>3.1.1</td><td>Method Identification</td><td>[70, 135]</td><td>-</td></tr><tr><td>3.1.2</td><td>Method Development</td><td>[49]</td><td>[166, 181]</td></tr><tr><td>3.1.3</td><td>Method Selection and Recommendation</td><td>[48, 82, 135, 153]</td><td>[112, 120, 143, 193]</td></tr><tr><td>3.1.4</td><td>Method Explanation</td><td>[37, 131, 153, 205]</td><td>-</td></tr><tr><td>3.1.5</td><td>Methodological Feedback and Critique</td><td>[5, 13, 16, 48]</td><td>[51, 73, 112, 193]</td></tr><tr><td>3.1.6</td><td>Research and Experimental Design Planning and Optimization</td><td>[9, 45, 48, 56, 95, 105, 115, 135, 139, 151, 153, 195]</td><td>[35, 51, 58, 59, 71, 73, 92, 97, 98, 102, 107, 111, 112, 121, 122, 124, 129, 134, 155, 159, 160, 181, 188, 193]</td></tr><tr><td>3.1.7</td><td>Mathematical and Formula Support</td><td>[5, 6, 106]</td><td>[58, 112, 182, 183]</td></tr><tr><td>3.1.8</td><td>Scenario and Simulation Generation</td><td>[5, 6, 14, 16, 66, 173, 179]</td><td>[73, 98, 113, 122, 124, 129, 162, 166, 180]</td></tr><tr><td>3.1.9</td><td>Prototyping</td><td>[95]</td><td>[98, 199]</td></tr><tr><td>3.1.10</td><td>Autonomous Laboratory Experimentation</td><td>[150]</td><td>[71, 122, 134, 162]</td></tr></tbody></table>

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<table><caption>Table 3 – Continued from previous page</caption><thead><tr><th>ID</th><th>Use Case</th><th>Peer-Reviewed</th><th>Non-Peer-Reviewed</th></tr></thead><tbody><tr><td>3.1.11</td><td>Documentation of Laboratory Notebooks</td><td>[150, 195]</td><td>[19]</td></tr><tr><td>3.1.12</td><td>Provide Feedback</td><td>[175, 216]</td><td>[51, 111]</td></tr><tr><td colspan="4"><strong>Category 3.2: Methodology – Qualitative Research</strong></td></tr><tr><td>3.2.1</td><td>Question Design, Interview Guide and Observation Grid</td><td>[13, 44, 144, 168, 192, 195, 205, 207]</td><td>[73, 98, 111]</td></tr><tr><td>3.2.2</td><td>Interview Support</td><td>[13, 144, 192]</td><td>–</td></tr><tr><td>3.2.3</td><td>GenAI-Based Interviewing</td><td>[13, 46, 192, 205]</td><td>[117, 128]</td></tr><tr><td>3.2.4</td><td>Interview Transcription</td><td>[14, 46, 146, 205]</td><td>[8, 111]</td></tr><tr><td>3.2.5</td><td>Translation of Qualitative Interview Data</td><td>[85]</td><td>–</td></tr><tr><td>3.2.6</td><td>Chatting with Qualitative Data</td><td>[46, 82, 146]</td><td>–</td></tr><tr><td>3.2.7</td><td>Qualitative Data Summarization</td><td>[13, 46, 82, 85, 146, 168, 205]</td><td>[65, 97, 99, 100, 107]</td></tr><tr><td>3.2.8</td><td>Codebook Generation and Refinement</td><td>[82, 142, 205]</td><td>[39, 65, 79, 98–100, 218]</td></tr><tr><td>3.2.9</td><td>Automated Coding and Classification</td><td>[16, 44, 46, 82, 114, 127, 139, 142, 144, 146, 156, 157, 173, 184, 194, 205]</td><td>[39, 47, 61, 79, 91, 97–100, 102, 107, 111, 112, 128, 129, 213, 218]</td></tr><tr><td>3.2.10</td><td>Thematic Analysis</td><td>[13, 14, 46, 82, 139, 142, 152, 156, 157, 184, 203, 205]</td><td>[51, 61, 65, 91, 97–100, 128, 129]</td></tr><tr><td>3.2.11</td><td>Justification and Self-Assessment of GenAI-Supported Qualitative Analysis</td><td>[205]</td><td>[39, 99, 213]</td></tr><tr><td>3.2.12</td><td>Theory Development via Conceptual Analysis</td><td>[82, 157, 184, 205]</td><td>[51]</td></tr><tr><td colspan="4"><strong>Category 3.3: Methodology – Quantitative Research</strong></td></tr><tr><td>3.3.1</td><td>Survey Generation</td><td>[13]</td><td>[73, 111]</td></tr><tr><td>3.3.2</td><td>Stimuli Generation</td><td>[16, 46, 175]</td><td>[129, 170]</td></tr><tr><td>3.3.3</td><td>Item Generation and Refinement</td><td>[13, 14, 16, 175, 220]</td><td>[51, 111, 124]</td></tr><tr><td>3.3.4</td><td>Survey and Item Pretesting</td><td>[16, 108, 111, 175]</td><td>[111]</td></tr><tr><td colspan="4"><strong>Category 4: Data Collection</strong></td></tr><tr><td>4.1</td><td>Dataset Creation and Mining</td><td>[13, 16, 44, 54, 105, 109, 139, 149, 186, 187, 203]</td><td>[8, 107, 111, 122, 128, 129, 166]</td></tr><tr><td>4.2</td><td>Dataset Recommendation</td><td>[5, 56, 151, 186]</td><td>[19, 174]</td></tr><tr><td>4.3</td><td>Evaluation of Data Collection Methods</td><td>[54, 139]</td><td>[181]</td></tr><tr><td>4.4</td><td>Interactive Participant Data Collection</td><td>–</td><td>[98, 128, 129]</td></tr><tr><td>4.5</td><td>Study Participant Recruitment</td><td>[13, 150]</td><td>[51, 98]</td></tr><tr><td>4.6</td><td>Database Query Generation and Execution</td><td>[30, 131]</td><td>[19, 101, 180]</td></tr><tr><td>4.7</td><td>Data Management and Organization</td><td>[54, 202, 205]</td><td>[19, 213]</td></tr><tr><td>4.8</td><td>Data Cleaning</td><td>[5, 12, 16, 21, 46, 54, 115, 139, 187, 202, 207, 212]</td><td>[73, 117, 129, 174, 181, 193]</td></tr><tr><td>4.9</td><td>Data Formatting</td><td>[5, 12, 105, 106, 115, 144, 153, 195, 212]</td><td>[174, 181]</td></tr><tr><td>4.10</td><td>Bias Detection and Quality Check</td><td>[5, 38, 48, 85, 115]</td><td>[117, 170]</td></tr><tr><td>4.11</td><td>Data Annotation and Labeling</td><td>[5, 13, 16, 23, 46, 54, 87, 106, 114, 173, 196]</td><td>[19, 39, 55, 61, 79, 91, 128, 129, 141, 163, 217]</td></tr></tbody></table>

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<table><caption>Table 3 – Continued from previous page</caption><thead><tr><th>ID</th><th>Use Case</th><th>Peer-Reviewed</th><th>Non-Peer-Reviewed</th></tr></thead><tbody><tr><td>4.12</td><td>Synthetic Data Generation</td><td>[9, 13, 16, 23, 44, 46, 82, 85, 106, 108, 114, 115, 139, 147, 173, 175, 187, 192, 195, 205]</td><td>[10, 51, 59, 73, 98, 99, 111, 120, 124, 129, 145, 170, 181]</td></tr><tr><td colspan="4"><strong>Category 5: Programming</strong></td></tr><tr><td>5.1</td><td>Requirements Analysis and Software Design Support</td><td>[12, 169]</td><td>–</td></tr><tr><td>5.2</td><td>Code Generation</td><td>[5, 6, 9, 21, 30, 42, 56, 66, 70, 75, 77, 82, 89, 90, 106, 115, 139, 152, 167, 169, 186, 202, 203]</td><td>[28, 31, 41, 58, 81, 92, 98, 112, 121, 129, 155, 162, 166, 181–183, 193]</td></tr><tr><td>5.3</td><td>Documentation, Library, and API Recommendation and Explanation</td><td>–</td><td>[129, 166]</td></tr><tr><td>5.4</td><td>Algorithm and Data Structure Selection</td><td>[186]</td><td>[181]</td></tr><tr><td>5.5</td><td>Code Improvement</td><td>[186, 207]</td><td>[31, 91, 166]</td></tr><tr><td>5.6</td><td>Code Validation and Debugging</td><td>[5, 13, 30, 56, 75, 106, 186]</td><td>[31, 58, 98, 155, 166, 191]</td></tr><tr><td>5.7</td><td>Software Testing</td><td>–</td><td>[92, 166]</td></tr><tr><td>5.8</td><td>Documentation Writing</td><td>[186]</td><td>[129, 166]</td></tr><tr><td>5.9</td><td>Code Translation Across Programming Languages</td><td>[5, 106, 186]</td><td>[31]</td></tr><tr><td>5.10</td><td>Script Reuse and Adaptation</td><td>[56]</td><td>–</td></tr><tr><td>5.11</td><td>Natural Language Translation</td><td>[106, 186]</td><td>[31]</td></tr><tr><td>5.12</td><td>Code Execution and Simulation</td><td>[5, 30, 66, 75]</td><td>[71]</td></tr><tr><td>5.13</td><td>Programming Skill Development</td><td>[5]</td><td>[166]</td></tr><tr><td colspan="4"><strong>Category 6: Data Analysis</strong></td></tr><tr><td>6.1</td><td>Data Analysis Planning</td><td>[48, 56, 75, 179]</td><td>[124]</td></tr><tr><td>6.2</td><td>Data Understanding Support</td><td>[5, 90, 95, 151, 195, 202, 205]</td><td>[41, 69, 101]</td></tr><tr><td>6.3</td><td>Analysis Method Selection</td><td>[9, 75, 82, 139, 203]</td><td>[51, 129, 193]</td></tr><tr><td>6.4</td><td>Analysis Method Explanation</td><td>[56, 139, 205]</td><td>[51]</td></tr><tr><td>6.5</td><td>Data Analysis Tutoring</td><td>[5, 9, 56, 106]</td><td>–</td></tr><tr><td>6.6</td><td>Computational Task Execution</td><td>[5, 13, 70, 178]</td><td>[51]</td></tr><tr><td>6.7</td><td>Pattern, Trend, and Relationship Identification</td><td>[14, 44, 46, 54, 82, 85, 90, 105, 131, 142, 146, 152, 153, 157, 173, 179, 203]</td><td>[8, 47, 59, 69, 91, 92, 99, 107, 129, 165, 166, 181, 183, 193]</td></tr><tr><td>6.8</td><td>Application of Different Analytical Methods</td><td>[5, 13, 34, 56, 64]</td><td>[55, 92, 107]</td></tr><tr><td>6.9</td><td>Sentiment and Position Analysis</td><td>[34, 44, 54, 87, 106, 114, 127, 156, 173, 202, 203, 207]</td><td>[69, 91, 97, 99, 166]</td></tr><tr><td>6.10</td><td>Hypothesis Testing</td><td>[56, 192]</td><td>[73, 92, 124, 188]</td></tr><tr><td>6.11</td><td>Result Interpretation and Discussion</td><td>[5, 15, 30, 56, 75, 95, 131, 149, 152, 179, 187, 203]</td><td>[41, 58, 81, 92, 101, 122, 165, 166, 181, 193, 213, 218]</td></tr><tr><td>6.12</td><td>GenAI-Based Evaluation</td><td>[200]</td><td>[145]</td></tr></tbody></table>

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<table><caption>Table 3 – Continued from previous page</caption><thead><tr><th>ID</th><th>Use Case</th><th>Peer-Reviewed</th><th>Non-Peer-Reviewed</th></tr></thead><tbody><tr><td colspan="4">Category 7: Writing</td></tr><tr><td>7.1</td><td>Structuring, Planning, Outlining, and Drafting of Texts</td><td>[7, 9, 14, 15, 37, 54, 77, 78, 115, 123, 144, 149, 153, 156, 158, 164, 168, 171, 178, 186, 195, 202]</td><td>[51, 58, 68, 73, 98, 107, 120, 129, 161, 166, 181, 193, 197]</td></tr><tr><td>7.2</td><td>Academic Text Generation</td><td>[7, 9, 15, 21, 38, 45, 70, 95, 116, 123, 153, 158, 167, 186, 202]</td><td>[22, 25, 28, 35, 41, 58, 69, 92, 98, 107, 129, 181]</td></tr><tr><td>7.3</td><td>Title and Abstract Generation</td><td>[9, 95, 106, 151, 153, 171]</td><td>[92, 181]</td></tr><tr><td>7.4</td><td>Correction of Spelling, Grammar, and Punctuation</td><td>[14, 57, 78, 90, 149, 164, 171, 178, 202, 203]</td><td>[25, 32, 52, 98, 107, 176, 181, 193]</td></tr><tr><td>7.5</td><td>Example Generation</td><td>[14, 24, 90, 186]</td><td>[24]</td></tr><tr><td>7.6</td><td>Figure and Illustration Generation</td><td>[90, 95, 151, 202]</td><td>-</td></tr><tr><td>7.7</td><td>Data and Result Visualization</td><td>[13, 21, 75, 95, 106, 150, 151, 203]</td><td>[28, 51, 81, 92, 98, 121, 129, 166, 174, 181]</td></tr><tr><td>7.8</td><td>Argumentation Development</td><td>[57, 106]</td><td>[58, 181, 193]</td></tr><tr><td>7.9</td><td>Misleading Statement Detection and Correction</td><td>[154]</td><td>-</td></tr><tr><td>7.10</td><td>Identification of Implications and Limitations of Studies</td><td>[48, 153]</td><td>[181, 193]</td></tr><tr><td>7.11</td><td>Future Work Recommendation</td><td>[95]</td><td>[124, 166, 183, 197]</td></tr><tr><td>7.12</td><td>Fact Checking</td><td>[105, 123]</td><td>-</td></tr><tr><td>7.13</td><td>Rewriting of Text</td><td>[42, 57, 116, 171, 178]</td><td>[58, 69, 107, 170]</td></tr><tr><td>7.14</td><td>Ensuring Consistency in Academic Writing</td><td>[7]</td><td>[193]</td></tr><tr><td>7.15</td><td>Reference Management</td><td>[14, 54, 89, 149, 178]</td><td>-</td></tr><tr><td>7.16</td><td>Citation and Reference Support</td><td>[70, 116, 135, 139, 153, 178, 186]</td><td>[41, 51, 92, 181, 193, 197, 213]</td></tr><tr><td>7.17</td><td>Technical Writing in LaTeX and Markdown</td><td>[106, 153, 178]</td><td>[92, 181, 215]</td></tr><tr><td>7.18</td><td>Formatting of Texts</td><td>[123, 149, 151]</td><td>[107, 129]</td></tr><tr><td>7.19</td><td>Formatting and Generation of Bibliographies and References</td><td>[89, 106, 115, 150, 153, 178]</td><td>[102, 129, 181, 193]</td></tr><tr><td>7.20</td><td>Translation of Texts</td><td>[64, 70, 90, 105, 106, 114, 115, 149, 150, 152, 178, 203]</td><td>[8, 28, 32, 35, 51, 58, 69, 73, 132, 165, 215]</td></tr><tr><td>7.21</td><td>Text Quality and Readability Improvement</td><td>[14, 15, 38, 42, 44, 45, 75, 78, 89, 90, 105, 106, 123, 144, 149–151, 153, 164, 168, 171, 178, 202]</td><td>[51, 58, 73, 92, 98, 140, 181, 193]</td></tr><tr><td>7.22</td><td>Style Checking</td><td>[149, 171]</td><td>[92, 181]</td></tr><tr><td>7.23</td><td>Shortening of Papers and Abstracts</td><td>[153, 171]</td><td>[129, 161, 181]</td></tr><tr><td>7.24</td><td>Scientific Paper Review (Pre-Submission)</td><td>[9, 42, 45, 90, 106, 131, 164]</td><td>[51, 58, 71, 92, 107, 129, 166, 181, 193]</td></tr><tr><td>7.25</td><td>Journal and Conference Recommendation</td><td>-</td><td>[129]</td></tr></tbody></table>

Received 16 January 2026; revised T.B.D.; accepted T.B.D.