GenAI4ELab/papercli-papers huggingface dataset:
Trauma THOMPSON: A Dataset and Realistic Generative Framework for AI Copilots in Emergency Care
Authors: Yupeng Zhuo; Eddie Zhang; Xiangchen Yu; Aditya Pachpande; Andrew W. Kirkpatrick; Jessica Mckee; Juan Wachs
Venue: AAAI 2026 (main)

Abstract: We introduce Trauma THOMPSON, a dataset and suite of benchmarks designed to accelerate the development of AI-powered copilots for real-time decision-making in emergency and resource-limited medical settings. This work proposes a method to address a critical bottleneck for future deployment: models trained on simulations may not perform well in the real world. The dataset features 3,717 unscripted, first-person video clips of five emergency procedures, uniquely including "just-in-time" (JIT) interventions that mirror the improvisational nature of field medicine. To obtain realistic patient data without ethical issues and identity concerns that medical data often encounter, we also propose TraumaGen, a novel framework for generating photorealistic patient and wound images from manikins while preserving clinical context. We establish benchmarks for action recognition, anticipation, and visual question answering (VQA), evaluating state-of-the-art models to demonstrate the challenges and potential of our dataset. By focusing on realism and improvisation, Trauma THOMPSON provides a crucial resource and a clear path toward developing and validating robust AI assistants for future deployment in real-world emergency care.
Stratos: An End-to-End Distillation Pipeline for Customized LLMs Under Distributed Cloud Environments
Authors: Ziming Dai; Tuo Zhang; Fei Gao; Xingyi Cai; Xiaofei Wang; Cheng Zhang; Wenyu Wang; Chengjie Zang
Venue: AAAI 2026 (main)

Abstract: The growing industrial demand for customized and cost-efficient large language models (LLMs) is fueled by the rise of vertical, domain-specific tasks and the need to optimize performance under constraints such as latency and budget. Knowledge distillation, as an efficient model compression and transfer technique, offers a feasible solution. However, existing distillation frameworks often require manual intervention and struggle to meet such complex user-defined distillation requirements. To bridge this gap, we propose Stratos, an end-to-end LLM distillation pipeline that automates server/model selection, knowledge distillation, and deployment in distributed cloud environments. Given user-defined constraints on model performance and system budget, Stratos automatically selects Pareto-optimal servers, dynamically matches teacher–student pairs, and adapts distillation strategies based on task complexity to optimize cloud hosting. Experiments show that Stratos produces a student model that achieves four times the accuracy of its GPT-4o teacher baseline on a rare, domain-specific Mahjong reasoning task with reverse synthetic data and knowledge injection. Moreover, it achieves reduced latency and cost without compromising accuracy. These results highlight its promise for vertical-domain LLM deployment.
GAICo: A Deployed and Extensible Framework for Evaluating Diverse and Multimodal Generative AI Outputs
Authors: Nitin Gupta; Pallav Koppisetti; Kausik Lakkaraju; Biplav Srivastava
Venue: AAAI 2026 (main)

Abstract: The rapid proliferation of Generative AI (GenAI) into diverse, high-stakes domains necessitates robust and reproducible evaluation methods. However, practitioners often resort to ad-hoc, non-standardized scripts, as common metrics are often unsuitable for specialized, structured outputs (e.g., automated plans, time-series) or holistic comparison across modalities (e.g., text, audio, and image). This fragmentation hinders comparability and slows AI system development. To address this challenge, we present GAICo (Generative AI Comparator): a deployed, open-source Python library that streamlines and standardizes GenAI output comparison. GAICo provides a unified, extensible framework supporting a comprehensive suite of reference-based metrics for unstructured text, specialized structured data formats, and multimedia (images, audio). Its architecture features a high-level API for rapid, end-to-end analysis, from multi-model comparison to visualization and reporting, alongside direct metric access for granular control. We demonstrate GAICo's utility through a detailed case study evaluating and debugging complex, multi-modal AI Travel Assistant pipelines. GAICo empowers AI researchers and developers to efficiently assess system performance, make evaluation reproducible, improve development velocity, and ultimately build more trustworthy AI systems, aligning with the goal of moving faster and safer in AI deployment. Since its release on PyPI in Jun 2025, the tool has been downloaded over 16K times, across versions, by Dec 2025, demonstrating growing community interest.
Deploying Atmospheric and Oceanic AI Models on Chinese Hardware and Framework: Migration Strategies, Performance Optimization and Analysis
Authors: Yuze Sun; Wentao Luo; Yanfei Xiang; Jiancheng Pan; Jiahao Li; Quan Zhang; Xiaomeng Huang
Venue: AAAI 2026 (main)

Abstract: With the growing role of artificial intelligence in climate and weather research, efficient model training and inference are in high demand. Current models like FourCastNet and AI-GOMS depend heavily on GPUs, limiting hardware independence, especially for Chinese domestic hardware and frameworks. To address this issue, we present a framework for migrating large-scale atmospheric and oceanic models from PyTorch to MindSpore and optimizing for Chinese chips, and evaluating their performance against GPUs. The framework focuses on software-hardware adaptation, memory optimization, and parallelism. Furthermore, the model's performance is evaluated across multiple metrics, including training speed, inference speed, model accuracy, and energy efficiency, with comparisons against GPU-based implementations. Experimental results demonstrate that the migration and optimization process preserves the models' original accuracy while significantly reducing system dependencies and improving operational efficiency by leveraging Chinese chips as a viable alternative for scientific computing. This work provides valuable insights and practical guidance for leveraging Chinese domestic chips and frameworks in atmospheric and oceanic AI model development, offering a pathway toward greater technological independence.
A Guardrail Framework for Sensitive Financial Information Protection: A Taxonomy-Driven Approach
Authors: Mehdi Yekrangi; Houssem Chatbri; Claudia Beatrice Chianella; Owen O'Neill
Venue: AAAI 2026 (main)

Abstract: The increasing adoption of large language models in the fi-nancial sector introduces significant challenges related to the handling of sensitive financial information (SFI). Existing general-purpose content safety solutions, or guardrails, often fall short in detecting domain-specific risks inherent in finan-cial data processing. This study addresses these gaps by de-veloping a comprehensive taxonomy of SFI, grounded in globally recognized financial, information security, and AI governance standards. Leveraging this taxonomy, we synthe-sized an extensive dataset encompassing diverse categories of SFI and trained GARD (Generative Adversarial network Risk Detection) model to detect sensitive content in both in-puts and outputs of GenAI systems within the financial do-main. Our evaluation compared GARD against commercial guardrail solutions, including the OpenAI Moderation API and Microsoft Azure Content Safety (ACS). The results demonstrated that while commercial solutions maintained high precision, their recall was substantially lower, indicating many risky instances went undetected. In contrast, our model achieved a recall score of 0.98, significantly outperforming the benchmarks and enhancing SFI detection. These findings underscore the necessity of domain-specific guardrails tai-lored to the financial sector to ensure robust AI safety and compliance. In conclusion, this work contributes (1) A de-tailed taxonomy of SFI tailored for GenAI applications, (2) A comprehensive synthetic dataset that encompasses a wide range of sensitive topics relevant to the domain and (3) A high-performance risk detection model that can be deployed independently or alongside existing solutions to improve con-tent safety in financial services. This approach promotes trust, mitigates financial, legal, and reputational risks, and supports the responsible adoption of GenAI technologies in sensitive domains.
Exploring Cross-Cultural Perspectives on AI Education: Insights from Teachers in Nigeria and the USA
Authors: Cornelius O Adejoro; Oghenemaro Anuyah; Ali Raza; Karla Badillo-Urquiola; Tom Yeh
Venue: AAAI 2026 (main)

Abstract: With artificial intelligence (AI) becoming more present in ed-
ucation globally, it is essential to consider how cultural con-
texts shape teachers’ perspectives, an understanding that sup-
ports more inclusive and sustainable learning systems. This
study draws on the African philosophy of Ubuntu to frame
our cross-cultural investigation of how children conceptu-
alize AI through the lens of their teachers. We conducted
semi-structured interviews with twelve middle school teach-
ers in Nigeria and the United States, asking them to interpret
AI-themed essays written by students. These teacher reflec-
tions revealed differing educational priorities, cultural val-
ues, and infrastructural realities: U.S. educators’ interpreta-
tions centered on personal development and future careers,
while Nigerian teachers highlighted students’ focus on fam-
ily, community well-being, and practical societal challenges.
Nigerian participants also pointed to the need for improved
infrastructure (e.g., electricity, internet), broader AI literacy,
and education policies that reflect local needs. Our findings il-
lustrate how culturally grounded worldviews, such as Ubuntu,
shape interpretations of AI and its role in society, and sug-
gest that AI education is never culturally neutral. We argue
that AI literacy initiatives must be designed not only to teach
technical skills but also to support educational sustainability,
defined here as inclusive, resilient, and culturally responsive
learning systems capable of evolving within diverse contexts.
We offer actionable recommendations for the HCI commu-
nity to co-design AI education tools that foreground collec-
tive well-being, foster global digital citizenship, and reduce
epistemic exclusion in the development of future technolo-
gies.
Sound-AI: A Pedagogical Tool for Exploring AI in Audio and Bioacoustic Research
Authors: Muhammad Azeem; Hoang D. Nguyen; Rosane Minghim
Venue: AAAI 2026 (main)

Abstract: Artificial intelligence offers powerful methods for audio processing and analysis. Still, complex workflows and the required programming skills often limit access for students and domain experts, such as marine bioacousticians and soundscape ecologists. We present "AI EcoSound Tutor", a code-free and interactive tool that lowers these barriers by allowing users to construct and explore a complete AI pipeline for audio data analysis. Starting from raw recordings, users can
choose from various feature extraction techniques (MFCC, OpenL3), apply dimensionality reduction methods (PCA, t-SNE, UMAP), and optionally perform unsupervised clustering (K-Means, GMM,HDBSCAN). The results are displayed with an interactive 2D visualisation where the user can compare multiple plots by employing various techniques, including PCA and t-SNE. Interactive plots enable the selection of points or clusters of interest, allowing exploration of spectrograms within the desired frequency range, and playing an audio clip corresponding to the selected points. An integrated "Help" feature provides explanations of each method (i.e., what it is, how it works, and its practical use in different domains, such as bioacoustics), fostering both conceptual understanding and useful skill acquisition as learning outcomes. For precomputed features or embeddings, this tool also supports training and evaluating a variety of machine learning models, providing visual feedback on the results. By merging accessibility, interactivity, pedagogy, and domain relevance, our application demystifies AI methods for interdisciplinary
education and supporting research in audio analysis.
Unplugged Activities on Machine Learning and Their Evaluation Through Mental States Attribution
Authors: Matteo Baldoni; Cristina Baroglio; Monica Bucciarelli; Sara Capecchi; Leonardo Castellani; Elena Gandolfi; Francesco Ianì; Elisa Marengo; Roberto Micalizio
Venue: AAAI 2026 (main)

Abstract: Theory of mind refers to the attribution of mental states that humans ascribe to other humans or objects (such as computer-based systems). Recently, the attribution of mental states has been investigated toward Artificial Intelligence (AI) as a basic manner to capture people's engagement toward it, and people's perception about AI social skills and AI capabilities. In line with this idea, mental state attribution can be used as an indirect measure of students' understanding of AI functioning, and in particular of the kind of interactions students may have with AI systems. Too often is the case of people using generative AI systems in ways that exceed their actual ways of functioning.
In our study, children of age in the range 9-12 were involved in one-shot unplugged activities concerning data and models. The unplugged activities were not aimed at teaching the theory of Machine Learning, but rather they were designed so as to provide awareness on some basic mechanisms and help developing a correct use of tools that are becoming more and more present in everyday life. This paper introduces the activities and reports the results that were achieved.
Bridging the Skills Gap: A Course Model for Modern Generative AI Education
Authors: Anya Bardach; Hamilton Murrah
Venue: AAAI 2026 (main)

Abstract: Research on how the popularization of generative Artificial Intelligence (AI) tools impacts learning environments has led to hesitancy among educators to teach these tools in classrooms, creating two observed disconnects. Generative AI competency is increasingly valued in industry but not in higher education, and students are experimenting with generative AI without formal guidance. The authors argue students across fields must be taught to responsibly and expertly harness the potential of AI tools to ensure job market readiness and positive outcomes. Computer Science trajectories are particularly impacted, and while many consistently top ranked Computer Science departments in the United States teach the mechanisms and frameworks underlying AI, few have started offering courses on applications for existing generative AI tools. A course was developed at a private research university to teach undergraduate and graduate Computer Science students applications for generative AI tools in software development. Two mixed method surveys indicated students overwhelmingly found the course valuable and effective. Co-authored by the instructor and one of the graduate students, this paper explores the context, implementation, and impact of the course through data analysis and reflections from both perspectives. It additionally offers recommendations for replication in and beyond Computer Science departments.
Balancing Scaffolding and Autonomy: A Case Study in Designing a Scalable Undergraduate Machine Learning Research Course
Authors: Xinyue Chen; Sharon Jessica; Xu Wang; Sindhu Kutty
Venue: AAAI 2026 (main)

Abstract: Undergraduate research experiences are often limited to small-scale apprenticeship models, leaving many students without accessible entry points into research practice. This paper presents the design and evaluation of a semester-long course for undergraduates to gain research experience in Machine Learning. The course, led by one faculty instructor, enables nearly a hundred students to engage in structured research through a scaffolded
replication-and-extension project, where students first replicate a published research project and then implement novel additions. The course integrates instructional modules (e.g., guided paper reading, proposal writing,
public presentation) with project milestones (e.g., replication, extension, poster) to support research learning for students with diverse backgrounds. Every component of research is visited several times, with each iteration having progressively increased autonomy coupled with simultaneously decreased scaffolding. We find that the scaffolding modules help students develop foundational conceptual and procedural understanding of doing research, and the project milestones on replication and extension help them gain execution skills gradually. Students also report developing a researcher mindset and feeling like they understand the research process better.
We discuss the principles used to design a scalable research-based course: balancing scaffolding to provide foundational understanding with autonomy for students to “feel like real researchers”.
Integrating AI Competencies into Teacher Education Programs
Authors: Amy Eguchi; Stacy George; Lucretia Fraga; Nancye Blair Black; Alecia Blackwood; J. Camille Dempsey; G. Sue Kasun
Venue: AAAI 2026 (main)

Abstract: The rapid advancement and integration of artificial intelligence (AI) into our everyday lives, work, and classrooms have added demands for PK-12 education to ensure that students are given opportunities to obtain AI competencies essential for responsible participation in the AI-driven future. AI literacy encompasses technical knowledge, ethical awareness, and critical evaluation of AI tools, as well as the ability to collaborate with AI systems in creative and productive ways. Research highlights the importance of age-appropriate approaches that address foundational AI knowledge, data literacy, ethics, problem-solving, and creativity, ensuring students can both use, analyze, critically evaluate, and design AI solutions for real-world challenges. This not only requires schools to prepare learners with AI competencies, but also colleges and universities to ensure that we prepare our preservice teachers to be AI-ready, who can integrate technological, pedagogical, and content knowledge in classroom practice. This paper highlights the importance of purposefully integrating AI competencies into teacher education programs and offers practical examples of how such integration can be achieved.
Building AI Hardware Expertise: Edge AI Curriculum Design and Implementation in German Universities
Authors: Ann-Marie Gursch; Xuanshu Luo; Lilian Hasse; Carsten Trinitis; Ulrike Lucke; Martin Werner; Milos Krstic
Venue: AAAI 2026 (main)

Abstract: Edge artificial intelligence (AI) redistributes AI computation from distant cloud to local processors for real-time processing and enhanced privacy. This fundamental shift underscores a critical gap in current university curricula, which predominantly focus on AI fundamentals and algorithms while often neglecting essential AI hardware topics. To address this deficiency, this paper presents Edge AI, a postgraduate curriculum co-designed by two universities in Germany. Guided by the Dagstuhl triangle, the curriculum is designed to comprehensively cover technical, sociocultural, and application perspectives. Courses are developed using the Four-Component Instructional Design model to encourage action-oriented skill development, with a Learning Management System template available to assist in the design of individual courses. Selected practical courses are formulated as self-managed projects and inverted classrooms, enabling students to learn at their own pace with just-in-time guidance. All curriculum materials are accessible online and maintained by the Open Science Framework to enhance collaboration across institutions and promote applicability in diverse domains. Evaluation results from 176 students over two years (2023-2025) demonstrate universal satisfaction across various curriculum components.
MAGIC: Multi-Agent Argumentation and Grammar Integrated Critiquer
Authors: Joaquín Jordán; Xavier Yin; Melissa Fabros; Gireeja Ranade; Narges Norouzi
Venue: AAAI 2026 (main)

Abstract: Automated Essay Scoring (AES) and Automatic Essay Feedback (AEF) systems aim to reduce the workload of human raters in educational assessment. However, most existing systems prioritize numeric scoring accuracy over feedback quality and are primarily evaluated on pre-secondary school level writing. This paper presents Multi-Agent Argumentation and Grammar Integrated Critiquer (MAGIC), a framework using five specialized agents to evaluate prompt adherence, persuasiveness, organization, vocabulary, and grammar for both holistic scoring and detailed feedback generation. To support evaluation at the college level, we collated a dataset of Graduate Record Examination (GRE) practice essays with expert-evaluated scores and feedback. MAGIC achieves substantial to near-perfect scoring agreement with humans on the GRE data, outperforming baseline LLM models while providing enhanced interpretability through its multi-agent approach. We also compare MAGIC's feedback generation capabilities against ground truth human feedback and baseline models, finding that MAGIC achieves strong feedback quality and naturalness.
‘What Do Children Think About AI?’: Insights and Educational Implications from Primary School Students’ Perceptions of AI
Authors: Maria Kasinidou; Styliani Kleanthous; Jahna Otterbacher
Venue: AAAI 2026 (main)

Abstract: AI has great potential to transform education and daily life. However, before integrating AI into classrooms, it is crucial to first educate children on what AI is and how to use it responsibly. Effective AI education should build on children's existing perceptions, address misconceptions, and establish a solid foundation for AI literacy. This study explores primary school students’ perception of AI and its relationship to their demographic characteristics and digital skills. A survey was conducted in seven local schools, with 233 students participating. The results indicate that most of them were unfamiliar with AI, and those who attempted to define or depict it often associated it with robots or digital devices. The study also found significant differences in students’ AI perceptions based on factors like gender, grade, and prior digital skills training. These variables were also linked to students’ awareness and understanding of AI. These findings underscore the need for targeted AI educational interventions for primary school students, leveraging their existing perceptions.
Educating the Public in Artificial Intelligence: Insights from a Large-Scale Course
Authors: Maria Kasinidou; Styliani Kleanthous; Jahna Otterbacher; Evgenia Christoforou
Venue: AAAI 2026 (main)

Abstract: As AI technologies grow more influential in shaping modern life, there is an urgent need to make AI literacy accessible beyond academic and technical communities. This paper presents the design, delivery, and evaluation of an online AI course targeting the general public. The course combined asynchronous lectures, interactive live sessions, and reflective assignments. Of the 343 people who registered, 169 completed the program. Using validated instruments administered before and after the course, we measured changes in participants’ attitudes toward AI and their AI literacy. Our findings revealed statistically significant changes in AI literacy, specifically in awareness, usage, and evaluation constructs, as well as a rise in positive attitudes toward AI. High satisfaction scores and qualitative feedback further support the course’s effectiveness. These findings reinforce the importance of inclusive, scalable educational interventions for empowering the public to navigate AI technologies.