GenAI4ELab/papercli-papers huggingface dataset:
The POWER of Ikigai: Optimizing Life Fulfillment with an Integrated User Simulator and Adaptive Hobby Recommender
Authors: Ping Chen; Jonathan Leung; Yinan Zhang; Zhiqi Shen
Venue: AAAI 2025 (main)

Abstract: Health and longevity are topics of great interest, leading to an exploration of the Japanese concept of ikigai, known for its impact on a fulfilling, extended life. Ikigai levels are dynamic, changing with personal growth and life situations, but traditional assessment methods are time-consuming, discouraging frequent tracking. In this paper, we propose Personalized Optimization and Wellbeing Enhancement Recommendation (POWER), which integrates an ikigai simulator to pre- dict ikigai levels from profile information and a hobby recommender that uses reinforcement learning to adapt recommendations based on continuous user feedback. Our methods, validated through both offline data and an online user study, effectively capture and enhance ikigai.
From Crayons to Code: AI-Driven Insights into a Child’s Mental Health Through Drawings
Authors: Moomal Farhad; Mohammad Mehedy Masud; Aisha Alnaqbi; Rawan Mubarak; Aaisha Aladawi; Sara Alnaqbi
Venue: AAAI 2025 (main)

Abstract: Children's mental health is crucial for their development, but it's often overlooked, leading to psychological issues. Many children struggle to express their thoughts and feelings effectively. To address this issue, we have proposed a novel approach to analyze children's drawings for psychological screening using artificial intelligence. Specifically, we're focusing on the `draw a person' (DAP) test, where a child's drawing is used to identify potential indicators of their mental and emotional state. Thus, we are introducing an AI-powered technique to automate the psychological screening process for children using the DAP test, which a human professional would traditionally conduct. The screening tool would suggest whether the child needs or doesn’t need further psychological referral. We have collected a dataset consisting of children's drawings and labeled them by experts as either `need' or `no need', indicating whether the child needs or does not need a referral. We have proposed two alternative approaches for the screening process. The first approach consists of extracting features from the drawings following expert guidelines and training a classification model using the features to classify the drawing as either `need' or `no need'. We also propose an out-of-the-box technique applying prompt engineering on state-of-the-art LLMs to automatically extract features from the images. The second approach involves training an image classification model using the drawings. Both approaches are challenged by the issue of class imbalance, as most of the drawings correspond to the `no-need' class. To address this challenge, we introduce Siamese++, a novel Siamese network for image classification, which uses feature embedding and an adaptive distance threshold for classification, instead of the nearest neighbor classification employed by traditional Siamese. Our proposed method achieves a high F1 score (up to 88%) even with a large class imbalance and without the need for any image augmentation. Thus, we have proposed an innovative interdisciplinary integration of AI with psychology and developed novel techniques to solve the real-world problem of psychological screening.
Evaluation and Incident Prevention in an Enterprise AI Assistant
Authors: Akash V. Maharaj; David Arbour; Daniel Lee; Uttaran Bhattacharya; Anup Rao; Austin Zane; Avi Feller; Kun Qian; Yunyao Li
Venue: AAAI 2025 (main)

Abstract: Enterprise AI Assistants are increasingly deployed in domains where accuracy is paramount, making each erroneous output a potentially significant incident. This paper presents a comprehensive framework for monitoring, benchmarking, and continuously improving such complex, multi-component systems under active development by multiple teams. Our approach encompasses three key elements: (1) a hierarchical ``severity'' framework for incident detection that identifies and categorizes errors while attributing component-specific error rates, facilitating targeted improvements; (2) a scalable and principled methodology for benchmark construction, evaluation, and deployment, designed to accommodate multiple development teams, mitigate overfitting risks, and assess the downstream impact of system modifications; and (3) a continual improvement strategy leveraging multidimensional evaluation, enabling the identification and implementation of diverse enhancement opportunities. By adopting this holistic framework, organizations can systematically enhance the reliability and performance of their AI Assistants, ensuring their efficacy in critical enterprise environments. 
We conclude by discussing how this multifaceted evaluation approach opens avenues for various classes of enhancements, paving the way for more robust and trustworthy AI systems.
To Err Is AI: A Case Study Informing LLM Flaw Reporting Practices
Authors: Sean McGregor; Allyson Ettinger; Nick Judd; Paul Albee; Liwei Jiang; Kavel Rao; William H. Smith; Shayne Longpre; Avijit Ghosh; Christopher Fiorelli; Michelle Hoang; Sven Cattell; Nouha Dziri
Venue: AAAI 2025 (main)

Abstract: In August of 2024, 495 hackers generated evaluations in an open-ended bug bounty targeting the Open Language Model (OLMo) from The Allen Institute for AI. A vendor panel staffed by representatives of OLMo's safety program adjudicated changes to OLMo's documentation and awarded cash bounties to participants who successfully demonstrated a need for public disclosure clarifying the intent, capacities, and hazards of model deployment. This paper presents a collection of lessons learned, illustrative of flaw reporting best practices intended to reduce the likelihood of incidents and produce safer large language models (LLMs). These include best practices for safety reporting processes, their artifacts, and safety program staffing.
Lessons for Editors of AI Incidents from the AI Incident Database
Authors: Kevin Paeth; Daniel Atherton; Nikiforos Pittaras; Heather Frase; Sean McGregor
Venue: AAAI 2025 (main)

Abstract: As artificial intelligence (AI) systems become increasingly deployed across the world, they are also increasingly implicated in AI incidents – harm events to individuals and society. As a result, industry, civil society, and governments worldwide are developing best practices and regulations for monitoring and analyzing  AI incidents. The AI Incident Database (AIID) is a project that catalogs AI incidents and supports further research by providing a platform to classify incidents for different operational and research-oriented goals. This study reviews the AIID’s dataset of 750+ AI incidents and two independent taxonomies applied to these incidents to identify common challenges to indexing and analyzing AI incidents. We find that certain patterns of AI incidents present structural ambiguities that challenge incident databasing and explore how epistemic uncertainty in AI incident reporting is unavoidable. We therefore report mitigations to make incident processes more robust to uncertainty related to cause, extent of harm, severity, or technical details of implicated systems. With these findings, we discuss how to develop future AI incident reporting practices.
Exploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models
Authors: Qiming Bao; Juho Leinonen; Alex Yuxuan Peng; Wanjun Zhong; Gaël Gendron; Timothy Pistotti; Alice Huang; Paul Denny; Michael Witbrock; Jiamou Liu
Venue: AAAI 2025 (main)

Abstract: Large language models (LLMs) have demonstrated strong capabilities in language understanding and generation, and their potential in educational contexts is increasingly being explored. One promising area is learnersourcing, where students engage in creating their own educational content, such as multiple-choice questions. A critical step in this process is generating effective explanations for the solutions to these questions, as such explanations aid in peer understanding and promote deeper conceptual learning. However, students often find it difficult to craft high-quality explanations due to limited understanding or gaps in their subject knowledge. To support this task, we introduce ``ILearner-LLM,'' a framework that uses iterative enhancement with LLMs to improve generated explanations. The framework combines an explanation generation model and an explanation evaluation model fine-tuned using student preferences for quality, where feedback from the evaluation model is fed back into the generation model to refine the output. Our experiments with LLaMA2-13B and GPT-4 using five large datasets from the PeerWise MCQ platform show that ILearner-LLM produces explanations of higher quality that closely align with those written by students. Our findings represent a promising approach for enriching the learnersourcing experience for students and for leveraging the capabilities of large language models for educational applications.
Making Transparency Advocates: An Educational Approach Towards Better Algorithmic Transparency in Practice
Authors: Andrew Bell; Julia Stoyanovich
Venue: AAAI 2025 (main)

Abstract: Concerns about the risks and harms posed by artificial intelligence (AI) have resulted in significant study into algorithmic transparency, giving rise to a sub-field known as Explainable AI (XAI). Unfortunately, despite a decade of development in XAI, an existential challenge remains: progress in research has not been fully translated into the actual implementation of algorithmic transparency by organizations. In this work, we test an approach for addressing the challenge by creating transparency advocates, or motivated individuals within organizations who drive a ground-up cultural shift towards improved algorithmic transparency.

Over several years, we created an open-source educational workshop on algorithmic transparency and advocacy. We delivered the workshop to professionals across two separate domains to improve their algorithmic transparency literacy and willingness to advocate for change. In the weeks following the workshop, participants applied what they learned, such as speaking up for algorithmic transparency at an organization-wide AI strategy meeting. We also make two broader observations: first, advocacy is not a monolith and can be broken down into different levels. Second, individuals' willingness for advocacy is affected by their professional field. For example, news and media professionals may be more likely to advocate for algorithmic transparency than those working at technology start-ups.
The Essentials of AI for Life and Society: An AI Literacy Course for the University Community
Authors: Joydeep Biswas; Don Fussell; Peter Stone; Kristin Patterson; Kristen Procko; Lea Sabatini; Zifan Xu
Venue: AAAI 2025 (main)

Abstract: We describe the development of a one-credit course to promote AI literacy at the University of Texas at Austin. In response to a call for the rapid deployment of class that would serve a broad audience in Fall of 2023, we designed a 14-week seminar-style course that incorporated an interdisciplinary group of speakers who lectured on topics ranging from the fundamentals of AI to societal concerns including disinformation and employment. University students, faculty, and staff, and even community members outside of the University were invited to enroll in this online offering: The Essentials of AI for Life and Society. We collected feedback from course participants through weekly reflections and a final survey. Satisfyingly, we found that attendees reported gains in their AI literacy. We sought critical feedback through quantitative and qualitative analysis, which uncovered challenges in designing a course for this general audience. We utilized the course feedback to design a three-credit version of the course that is being offered in Fall of 2024. The lessons we learned and our plans for this new iteration may serve as a guide to instructors designing AI courses for a broad audience.
Developing a Postgraduate Program for AI in Medicine with Kern’s Six-Step Curriculum Development Approach in Singapore
Authors: Chang Cai; Michelle Jong; Yih Yng Ng; Jo-Anne Elizabeth Manski-Nankervis; Kum Ying Tham; Preman Rajalingam; Boon Keong Ang; Jennifer Anne Cleland; Joseph Sung; Xiuyi Fan
Venue: AAAI 2025 (main)

Abstract: Artificial Intelligence (AI) has rapidly transformed the medical field, necessitating significant changes in medical education to prepare healthcare professionals for future work requirements. However, the integration of AI into medical curricula has been slow and lacks standardization. In this paper, we present our work in developing a year-long postgraduate-level AI in Medicine program offered by a medical school at a public university in Singapore. Our curriculum design follows Kern's six-step approach to medical curriculum development, organized into a four-session framework. These sessions involved collaboration with hospital and university administrators, educators, industry experts, and healthcare professionals. The program is structured around three core courses: Foundational Healthcare AI, Clinical Applications of Healthcare AI, and Governance and Ethics for Healthcare AI. Each course comprises multiple modules with associated projects, emphasizing hands-on learning. The program adopts a problem-based learning approach, supported by a blended learning environment to accommodate the schedules of working healthcare professionals. Evaluations by industry experts highlight the program's potential to address critical gaps in the healthcare sector. This study contributes to the integration of AI into medical training by providing a standardized approach that can be adapted globally.
Artificial Intelligence for Future Presidents: Teaching AI Literacy to Everyone
Authors: Kate Candon; Nicholas C. Georgiou; Rebecca Ramnauth; Jessie Cheung; E. Chandra Fincke; Brian Scassellati
Venue: AAAI 2025 (main)

Abstract: The rapid and nearly pervasive impact of artificial intelligence on fields as diverse as medicine, law, banking, and the arts has made many students who would never enroll in a computer science class become interested in understanding elements of artificial intelligence.  Fueled by questions about how this technology would change their own fields, these students are not seeking to become experts in building AI systems but instead are searching for a sufficient understanding to be safe, effective, and informed users.  In this paper, we describe a first-of-its-kind course offering, "Artificial Intelligence for Future Presidents" designed and taught during the spring of 2024.  We share rationale on the design and structure of the course, consider how best to convey complex technical information to students without the background in programming or mathematics, and consider methods for supporting an understanding of the limits of this technology.
Automated Assessment of Student Self-Explanation in Code Comprehension Using Pre-Trained Language Models
Authors: Jeevan Chapagain; Vasile Rus
Venue: AAAI 2025 (main)

Abstract: Assessing students' responses, especially natural language responses, is a major challenge in education. In general, in education contexts, automatically evaluating what learners do or say is important as it enables personalized instruction, e.g., based on what the learner knows tailored tasks and feedback are given to the learner. Recently, deep learning techniques led to state-of-the-art methods in NLP such as transformer-based methods which resulted in significant performance improvements for many NLP tasks such as text classification and question answering. However, there is not much work exploring such methods for assessing students' free answers, particularly in the context of code comprehension, which brings additional challenges as the student explanations include code references as well. This paper explores the potential of applying automated assessments methods using transformers to code comprehension. We fine-tuned pre-trained transformer models, including BERT, RoBERTa, CodeBERT, and SciBERT, to see how well they can automatically judge students' responses to code comprehension tasks. Our results demonstrate that these models can significantly enhance the accuracy and reliability of automated assessments, offering insights into how the latest NLP techniques can be leveraged in computer science education to support personalized learning experiences.
Computational Thinking with Computer Vision: Developing AI Competency in an Introductory Computer Science Course
Authors: Tahiya Chowdhury
Venue: AAAI 2025 (main)

Abstract: Developing competency in artificial intelligence is becoming increasingly crucial for computer science (CS) students at all levels of the CS curriculum. However, most previous research focuses on advanced CS courses, as traditional introductory courses provide limited opportunities to develop AI skills and knowledge. This paper introduces an introductory CS course where students learn computational thinking through computer vision, a sub-field of AI, as an application context. The course aims to achieve computational thinking outcomes alongside critical thinking outcomes that expose students to AI approaches and their societal implications. Through experiential activities such as individual projects and reading discussions, our course seeks to balance technical learning and critical thinking goals. Our evaluation, based on pre-and post-course surveys, shows an improved sense of belonging, self-efficacy, and AI ethics awareness among students. The results suggest that an AI-focused context can enhance participation and employability, student-selected projects support self-efficacy, and ethically grounded AI instruction can be effective for interdisciplinary audiences. Students' discussions on reading assignments demonstrated deep engagement with the complex challenges in today's AI landscape. Finally, we share insights on scaling such courses for larger cohorts and improving the learning experience for introductory CS students.
AI Toolkit: Libraries and Essays for Exploring the Technology and Ethics Behind AI
Authors: Levin Ho; Morgan McErlean; Zehua You; Douglas Blank; Lisa Meeden
Venue: AAAI 2025 (main)

Abstract: In this paper we describe the development and evaluation of AITK, the Artificial Intelligence Toolkit. This open-source project contains both Python libraries and computational essays (Jupyter notebooks) that together are designed to allow a diverse audience with little or no background in AI to interact with a variety AI tools, exploring in more depth how they function, visualizing their outcomes, and gaining a better understanding of their ethical implications. These notebooks have been piloted at multiple institutions in a variety of humanities courses centered on the theme of responsible AI. In addition, we conducted usability testing of AITK. Our pilot studies and usability testing results indicate that AITK is easy to navigate and effective at helping diverse users gain a better understanding of AI and its ethical implications. Our goal, in this time of rapid innovations in AI, is for AITK to provide an accessible resource for faculty from any discipline looking to incorporate AI topics into their courses and for anyone eager to learn more about AI on their own.
Using Explainable AI and Hierarchical Planning for Outreach with Robots
Authors: Rushang Karia; Jayesh Nagpal; Daksh Dobhal; Pulkit Verma; Rashmeet Kaur Nayyar; Naman Shah; Siddharth Srivastava
Venue: AAAI 2025 (main)

Abstract: Understanding how robots plan and execute tasks is crucial in today's world, where they are becoming more prevalent in our daily lives. However, teaching non-experts, such as K-12 students, the complexities of robot planning can be challenging. This work presents an open-source platform, JEDAI.Ed, that simplifies the process using a visual interface that abstracts the details of various planning processes that robots use for performing complex mobile manipulation tasks. Using principles developed in the field of explainable AI, this intuitive platform enables students to use a high-level intuitive instruction set to perform complex tasks, visualize them on an in-built simulator, and to obtain helpful hints and natural language explanations for errors. Finally, JEDAI.Ed, includes an adaptive curriculum generation method that provides students with customized learning ramps. This platform's efficacy was tested through a user study with university students who had little to no computer science background. Our results show that JEDAI.Ed is highly effective in increasing student engagement, teaching robotics programming, and decreasing the time need to solve tasks as compared to baselines.
Human-Computer Interaction for AI Systems Design: Reflections on an Online Course on Human-AI Interaction for Professionals
Authors: Per Ola Kristensson; Emily Patterson
Venue: AAAI 2025 (main)

Abstract: Human–Computer Interaction for AI Systems Design is an eight-week short online course aimed at professional students. It is part of an online course platform called Cambridge Advance Online, which is a joint effort between Cambridge University Press & Assessment and the University of Cambridge. This course launched in July 2023 amidst a massive increase in interest in AI and its applications, and quickly became one of the platform's highest-enrolling courses, attracting about 50 students per quarterly course run. To date, more than 200 students have completed the course, and more than 90 percent have rated their experience `good' or `excellent'. This paper reports on our experiences in designing and teaching this course.
Bridging the AI Gap: Evaluating the Impact of an AI Education Program for Caregivers on Parental Leave
Authors: Kristina L. Kupferschmidt; Flora Wan; Juan Carrasquilla Alvarez; Dora Gaviria Castaño; Graham W. Taylor; Sedef Akinli Kocak
Venue: AAAI 2025 (main)

Abstract: Artificial Intelligence (AI) literacy is increasingly important across many fields, yet caregivers remain underrepresented in AI-related fields due to a combination of systemic and individual barriers. To address this, the Caregivers and Machine Learning (C&ML) program developed and delivered an accessible AI education program to caregivers on parental leave. Two cohorts participated in this 6-week interprofessional program, featuring fundamental machine learning concepts, hands-on programming assignments, and a capstone project. This study examines the program's impact on participants, focusing on their motivations and barriers before, during, and after the program as outcomes after completion.

Post-program surveys and semi-structured interviews highlight that caregivers often face barriers such as the rapid pace of AI, discrimination, and balancing caregiving responsibilities with learning new skills. The C&ML program's flexible structure and personalized support network were critical in enabling participants to fully engage in the program, leading to significant improvements in their knowledge of ML and increased confidence in applying these skills. After completing the program, 20\% of participants transitioned into AI-related roles or pursued further education. This research highlights the value of targeted, inclusive educational programs for underrepresented groups and provides practical recommendations for refining future AI training programs for caregivers.