pwc-archive/papers-with-abstracts huggingface dataset:
Fine-Tuning Large Audio-Language Models with LoRA for Precise Temporal Localization of Prolonged Exposure Therapy Elements
Authors: Suhas BN, Andrew M. Sherrill, Jyoti Alaparthi, Dominik Mattioli, Rosa I. Arriaga, Chris W. Wiese, Saeed Abdullah
Date: 2025-06-11 00:00:00
Tasks: Temporal Localization

Prolonged Exposure (PE) therapy is an effective treatment for post-traumatic stress disorder (PTSD), but evaluating therapist fidelity remains labor-intensive due to the need for manual review of session recordings. We present a method for the automatic temporal localization of key PE fidelity elements -- identifying their start and stop times -- directly from session audio and transcripts. Our approach fine-tunes a large pre-trained audio-language model, Qwen2-Audio, using Low-Rank Adaptation (LoRA) to process focused 30-second windows of audio-transcript input. Fidelity labels for three core protocol phases -- therapist orientation (P1), imaginal exposure (P2), and post-imaginal processing (P3) -- are generated via LLM-based prompting and verified by trained raters. The model is trained to predict normalized boundary offsets using soft supervision guided by task-specific prompts. On a dataset of 313 real PE sessions, our best configuration (LoRA rank 8, 30s windows) achieves a mean absolute error (MAE) of 5.3 seconds across tasks. We further analyze the effects of window size and LoRA rank, highlighting the importance of context granularity and model adaptation. This work introduces a scalable framework for fidelity tracking in PE therapy, with potential to support clinician training, supervision, and quality assurance.
"I Said Things I Needed to Hear Myself": Peer Support as an Emotional, Organisational, and Sociotechnical Practice in Singapore
Authors: Kellie Yu Hui Sim, Kenny Tsu Wei Choo
Date: 2025-06-11 00:00:00

Peer support plays a vital role in expanding access to mental health care by providing empathetic, community-based support outside formal clinical systems. As digital platforms increasingly mediate such support, the design and impact of these technologies remain under-examined, particularly in Asian contexts. This paper presents findings from an interview study with 20 peer supporters in Singapore, who operate across diverse online, offline, and hybrid environments. Through a thematic analysis, we unpack how participants start, conduct, and sustain peer support, highlighting their motivations, emotional labour, and the sociocultural dimensions shaping their practices. Building on this grounded understanding, we surface design directions for culturally responsive digital tools that scaffold rather than supplant relational care. Drawing insights from qualitative accounts, we offer a situated perspective on how AI might responsibly augment peer support. This research contributes to human-centred computing by articulating the lived realities of peer supporters and proposing design implications for trustworthy and context-sensitive AI in mental health.
"Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported Interactions
Authors: Kellie Yu Hui Sim, Roy Ka-Wei Lee, Kenny Tsu Wei Choo
Date: 2025-06-11 00:00:00

Mental health is a growing global concern, prompting interest in AI-driven solutions to expand access to psychosocial support. Peer support, grounded in lived experience, offers a valuable complement to professional care. However, variability in training, effectiveness, and definitions raises concerns about quality, consistency, and safety. Large Language Models (LLMs) present new opportunities to enhance peer support interactions, particularly in real-time, text-based interactions. We present and evaluate an AI-supported system with an LLM-simulated distressed client, context-sensitive LLM-generated suggestions, and real-time emotion visualisations. 2 mixed-methods studies with 12 peer supporters and 5 mental health professionals (i.e., experts) examined the system's effectiveness and implications for practice. Both groups recognised its potential to enhance training and improve interaction quality. However, we found a key tension emerged: while peer supporters engaged meaningfully, experts consistently flagged critical issues in peer supporter responses, such as missed distress cues and premature advice-giving. This misalignment highlights potential limitations in current peer support training, especially in emotionally charged contexts where safety and fidelity to best practices are essential. Our findings underscore the need for standardised, psychologically grounded training, especially as peer support scales globally. They also demonstrate how LLM-supported systems can scaffold this development--if designed with care and guided by expert oversight. This work contributes to emerging conversations on responsible AI integration in mental health and the evolving role of LLMs in augmenting peer-delivered care.
When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration
Authors: Quan Shi, Carlos E. Jimenez, Shunyu Yao, Nick Haber, Diyi Yang, Karthik Narasimhan
Date: 2025-06-05 00:00:00
Tasks: Transfer Learning

Recent advancements in AI reasoning have driven substantial improvements across diverse tasks. A critical open question is whether these improvements also yields better knowledge transfer: the ability of models to communicate reasoning in ways humans can understand, apply, and learn from. To investigate this, we introduce Knowledge Integration and Transfer Evaluation (KITE), a conceptual and experimental framework for Human-AI knowledge transfer capabilities and conduct the first large-scale human study (N=118) explicitly designed to measure it. In our two-phase setup, humans first ideate with an AI on problem-solving strategies, then independently implement solutions, isolating model explanations' influence on human understanding. Our findings reveal that although model benchmark performance correlates with collaborative outcomes, this relationship is notably inconsistent, featuring significant outliers, indicating that knowledge transfer requires dedicated optimization. Our analysis identifies behavioral and strategic factors mediating successful knowledge transfer. We release our code, dataset, and evaluation framework to support future work on communicatively aligned models.
Secondary Stakeholders in AI: Fighting for, Brokering, and Navigating Agency
Authors: Leah Hope Ajmani, Nuredin Ali Abdelkadir, Stevie Chancellor
Date: 2025-06-08 00:00:00
Tasks: Navigate

As AI technologies become more human-facing, there have been numerous calls to adapt participatory approaches to AI development -- spurring the idea of participatory AI. However, these calls often focus only on primary stakeholders, such as end-users, and not secondary stakeholders. This paper seeks to translate the ideals of participatory AI to a broader population of secondary AI stakeholders through semi-structured interviews. We theorize that meaningful participation involves three participatory ideals: (1) informedness, (2) consent, and (3) agency. We also explore how secondary stakeholders realize these ideals by traversing a complicated problem space. Like walking up the rungs of a ladder, these ideals build on one another. We introduce three stakeholder archetypes: the reluctant data contributor, the unsupported activist, and the well-intentioned practitioner, who must navigate systemic barriers to achieving agentic AI relationships. We envision an AI future where secondary stakeholders are able to meaningfully participate with the AI systems they influence and are influenced by.
Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation
Authors: Gionnieve Lim, Bryan Chen Zhengyu Tan, Kellie Yu Hui Sim, Weiyan Shi, Ming Hui Chew, Ming Shan Hee, Roy Ka-Wei Lee, Simon T. Perrault, Kenny Tsu Wei Choo
Date: 2025-06-08 00:00:00

The emergence of Large Language Models (LLMs) presents a dual challenge in the fight against disinformation. These powerful tools, capable of generating human-like text at scale, can be weaponised to produce sophisticated and persuasive disinformation, yet they also hold promise for enhancing detection and mitigation strategies. This paper investigates the complex dynamics between LLMs and disinformation through a communication game that simulates online forums, inspired by the game Werewolf, with 25 participants. We analyse how Disinformers, Moderators, and Users leverage LLMs to advance their goals, revealing both the potential for misuse and combating disinformation. Our findings highlight the varying uses of LLMs depending on the participants' roles and strategies, underscoring the importance of understanding their effectiveness in this context. We conclude by discussing implications for future LLM development and online platform design, advocating for a balanced approach that empowers users and fosters trust while mitigating the risks of LLM-assisted disinformation.
Exploring Image Transforms derived from Eye Gaze Variables for Progressive Autism Diagnosis
Authors: Abigail Copiaco, Christian Ritz, Yassine Himeur, Valsamma Eapen, Ammar Albanna, Wathiq Mansoor
Date: 2025-06-07 00:00:00
Tasks: Diagnostic, Transfer Learning

The prevalence of Autism Spectrum Disorder (ASD) has surged rapidly over the past decade, posing significant challenges in communication, behavior, and focus for affected individuals. Current diagnostic techniques, though effective, are time-intensive, leading to high social and economic costs. This work introduces an AI-powered assistive technology designed to streamline ASD diagnosis and management, enhancing convenience for individuals with ASD and efficiency for caregivers and therapists. The system integrates transfer learning with image transforms derived from eye gaze variables to diagnose ASD. This facilitates and opens opportunities for in-home periodical diagnosis, reducing stress for individuals and caregivers, while also preserving user privacy through the use of image transforms. The accessibility of the proposed method also offers opportunities for improved communication between guardians and therapists, ensuring regular updates on progress and evolving support needs. Overall, the approach proposed in this work ensures timely, accessible diagnosis while protecting the subjects' privacy, improving outcomes for individuals with ASD.
ScriptDoctor: Automatic Generation of PuzzleScript Games via Large Language Models and Tree Search
Authors: Sam Earle, Ahmed Khalifa, Muhammad Umair Nasir, Zehua Jiang, Graham Todd, Andrzej Banburski-Fahey, Julian Togelius
Date: 2025-06-06 00:00:00
Tasks: Game Design, Large Language Model

There is much interest in using large pre-trained models in Automatic Game Design (AGD), whether via the generation of code, assets, or more abstract conceptualization of design ideas. But so far this interest largely stems from the ad hoc use of such generative models under persistent human supervision. Much work remains to show how these tools can be integrated into longer-time-horizon AGD pipelines, in which systems interface with game engines to test generated content autonomously. To this end, we introduce ScriptDoctor, a Large Language Model (LLM)-driven system for automatically generating and testing games in PuzzleScript, an expressive but highly constrained description language for turn-based puzzle games over 2D gridworlds. ScriptDoctor generates and tests game design ideas in an iterative loop, where human-authored examples are used to ground the system's output, compilation errors from the PuzzleScript engine are used to elicit functional code, and search-based agents play-test generated games. ScriptDoctor serves as a concrete example of the potential of automated, open-ended LLM-based workflows in generating novel game content.
"We need to avail ourselves of GenAI to enhance knowledge distribution": Empowering Older Adults through GenAI Literacy
Authors: Eunhye Grace Ko, Shaini Nanayakkara, Earl W. Huff Jr
Date: 2025-06-06 00:00:00
Tasks: Chatbot

As generative AI (GenAI) becomes increasingly widespread, it is crucial to equip users, particularly vulnerable populations such as older adults (65 and older), with the knowledge to understand its benefits and potential risks. Older adults often exhibit greater reservations about adopting emerging technologies and require tailored literacy support. Using a mixed methods approach, this study examines strategies for delivering GenAI literacy to older adults through a chatbot named Litti, evaluating its impact on their AI literacy (knowledge, safety, and ethical use). The quantitative data indicated a trend toward improved AI literacy, though the results were not statistically significant. However, qualitative interviews revealed diverse levels of familiarity with generative AI and a strong desire to learn more. Findings also show that while Litti provided a positive learning experience, it did not significantly enhance participants' trust or sense of safety regarding GenAI. This exploratory case study highlights the challenges and opportunities in designing AI literacy education for the rapidly growing older adult population.
(AI peers) are people learning from the same standpoint: Perception of AI characters in a Collaborative Science Investigation
Authors: Eunhye Grace Ko, Soo Hyoung Joo
Date: 2025-06-06 00:00:00

While the complexity of 21st-century demands has promoted pedagogical approaches to foster complex competencies, a persistent gap remains between in-class learning activities and individualized learning or assessment practices. To address this, studies have explored the use of AI-generated characters in learning and assessment. One attempt is scenario-based assessment (SBA), a technique that not only measures but also fosters the development of competencies throughout the assessment process. SBA introduces simulated agents to provide an authentic social-interactional context, allowing for the assessment of competency-based constructs while mitigating the unpredictability of real-life interactions. Recent advancements in multimodal AI, such as text-to-video technology, allow these agents to be enhanced into AI-generated characters. This mixed-method study investigates how learners perceive AI characters taking the role of mentor and teammates in an SBA mirroring the context of a collaborative science investigation. Specifically, we examined the Likert scale responses of 56 high schoolers regarding trust, social presence, and effectiveness. We analyzed the relationships between these factors and their impact on the intention to adopt AI characters through PLS-SEM. Our findings indicated that learners' trust shaped their sense of social presence with the AI characters, enhancing perceived effectiveness. Qualitative analysis further highlighted factors that foster trust, such as material credibility and alignment with learning goals, as well as the pivotal role of social presence in creating a collaborative context. This paper was accepted as an full paper for AIED 2025.
WoundAIssist: A Patient-Centered Mobile App for AI-Assisted Wound Care With Physicians in the Loop
Authors: Vanessa Borst, Anna Riedmann, Tassilo Dege, Konstantin Müller, Astrid Schmieder, Birgit Lugrin, Samuel Kounev
Date: 2025-06-06 00:00:00

The rising prevalence of chronic wounds, especially in aging populations, presents a significant healthcare challenge due to prolonged hospitalizations, elevated costs, and reduced patient quality of life. Traditional wound care is resource-intensive, requiring frequent in-person visits that strain both patients and healthcare professionals (HCPs). Therefore, we present WoundAIssist, a patient-centered, AI-driven mobile application designed to support telemedical wound care. WoundAIssist enables patients to regularly document wounds at home via photographs and questionnaires, while physicians remain actively engaged in the care process through remote monitoring and video consultations. A distinguishing feature is an integrated lightweight deep learning model for on-device wound segmentation, which, combined with patient-reported data, enables continuous monitoring of wound healing progression. Developed through an iterative, user-centered process involving both patients and domain experts, WoundAIssist prioritizes an user-friendly design, particularly for elderly patients. A conclusive usability study with patients and dermatologists reported excellent usability, good app quality, and favorable perceptions of the AI-driven wound recognition. Our main contribution is two-fold: (I) the implementation and (II) evaluation of WoundAIssist, an easy-to-use yet comprehensive telehealth solution designed to bridge the gap between patients and HCPs. Additionally, we synthesize design insights for remote patient monitoring apps, derived from over three years of interdisciplinary research, that may inform the development of similar digital health tools across clinical domains.
A Novel, Human-in-the-Loop Computational Grounded Theory Framework for Big Social Data
Authors: Lama Alqazlan, Zheng Fang, Michael Castelle, Rob Procter
Date: 2025-06-06 00:00:00

The availability of big data has significantly influenced the possibilities and methodological choices for conducting large-scale behavioural and social science research. In the context of qualitative data analysis, a major challenge is that conventional methods require intensive manual labour and are often impractical to apply to large datasets. One effective way to address this issue is by integrating emerging computational methods to overcome scalability limitations. However, a critical concern for researchers is the trustworthiness of results when Machine Learning (ML) and Natural Language Processing (NLP) tools are used to analyse such data. We argue that confidence in the credibility and robustness of results depends on adopting a 'human-in-the-loop' methodology that is able to provide researchers with control over the analytical process, while retaining the benefits of using ML and NLP. With this in mind, we propose a novel methodological framework for Computational Grounded Theory (CGT) that supports the analysis of large qualitative datasets, while maintaining the rigour of established Grounded Theory (GT) methodologies. To illustrate the framework's value, we present the results of testing it on a dataset collected from Reddit in a study aimed at understanding tutors' experiences in the gig economy.
Proactive Assistant Dialogue Generation from Streaming Egocentric Videos
Authors: Yichi Zhang, Xin Luna Dong, Zhaojiang Lin, Andrea Madotto, Anuj Kumar, Babak Damavandi, Joyce Chai, Seungwhan Moon
Date: 2025-06-06 00:00:00
Tasks: Dialogue Generation

Recent advances in conversational AI have been substantial, but developing real-time systems for perceptual task guidance remains challenging. These systems must provide interactive, proactive assistance based on streaming visual inputs, yet their development is constrained by the costly and labor-intensive process of data collection and system evaluation. To address these limitations, we present a comprehensive framework with three key contributions. First, we introduce a novel data curation pipeline that synthesizes dialogues from annotated egocentric videos, resulting in \dataset, a large-scale synthetic dialogue dataset spanning multiple domains. Second, we develop a suite of automatic evaluation metrics, validated through extensive human studies. Third, we propose an end-to-end model that processes streaming video inputs to generate contextually appropriate responses, incorporating novel techniques for handling data imbalance and long-duration videos. This work lays the foundation for developing real-time, proactive AI assistants capable of guiding users through diverse tasks. Project page: https://pro-assist.github.io/
Scenarios in Computing Research: A Systematic Review of the Use of Scenario Methods for Exploring the Future of Computing Technologies in Society
Authors: Julia Barnett, Kimon Kieslich, Jasmine Sinchai, Nicholas Diakopoulos
Date: 2025-06-05 00:00:00
Tasks: Systematic Literature Review

Scenario building is an established method to anticipate the future of emerging technologies. Its primary goal is to use narratives to map future trajectories of technology development and sociotechnical adoption. Following this process, risks and benefits can be identified early on, and strategies can be developed that strive for desirable futures. In recent years, computer science has adopted this method and applied it to various technologies, including Artificial Intelligence (AI). Because computing technologies play such an important role in shaping modern societies, it is worth exploring how scenarios are being used as an anticipatory tool in the field -- and what possible traditional uses of scenarios are not yet covered but have the potential to enrich the field. We address this gap by conducting a systematic literature review on the use of scenario building methods in computer science over the last decade (n = 59). We guide the review along two main questions. First, we aim to uncover how scenarios are used in computing literature, focusing especially on the rationale for why scenarios are used. Second, in following the potential of scenario building to enhance inclusivity in research, we dive deeper into the participatory element of the existing scenario building literature in computer science.
Personalized Interpretability -- Interactive Alignment of Prototypical Parts Networks
Authors: Tomasz Michalski, Adam Wróbel, Andrea Bontempelli, Jakub Luśtyk, Mikolaj Kniejski, Stefano Teso, Andrea Passerini, Bartosz Zieliński, Dawid Rymarczyk
Date: 2025-06-05 00:00:00

Concept-based interpretable neural networks have gained significant attention due to their intuitive and easy-to-understand explanations based on case-based reasoning, such as "this bird looks like those sparrows". However, a major limitation is that these explanations may not always be comprehensible to users due to concept inconsistency, where multiple visual features are inappropriately mixed (e.g., a bird's head and wings treated as a single concept). This inconsistency breaks the alignment between model reasoning and human understanding. Furthermore, users have specific preferences for how concepts should look, yet current approaches provide no mechanism for incorporating their feedback. To address these issues, we introduce YoursProtoP, a novel interactive strategy that enables the personalization of prototypical parts - the visual concepts used by the model - according to user needs. By incorporating user supervision, YoursProtoP adapts and splits concepts used for both prediction and explanation to better match the user's preferences and understanding. Through experiments on both the synthetic FunnyBirds dataset and a real-world scenario using the CUB, CARS, and PETS datasets in a comprehensive user study, we demonstrate the effectiveness of YoursProtoP in achieving concept consistency without compromising the accuracy of the model.