GenAI4ELab/papercli-papers huggingface dataset:
Brains vs. Algorithms? How Experts and Students See AI-Generated Distractors
Authors: Zifeng Liu; Hai Li; Jie Chao; Wanli Xing
Venue: AAAI 2026 (main)

Abstract: Multiple-choice questions (MCQs) are central to instruction and assessment, with distractors revealing student understanding and misconceptions. However, creating high-quality distractors is time-consuming, especially for emerging domains like K–12 AI education. This study explores using generative AI to support distractor creation in a self-paced online module integrating AI and Algebra 1. Five MCQs were selected to compare distractors written by human developers and ChatGPT, using expert reviews and log data from 80 students. Experts rated human distractors higher overall, though AI ones consistently ranked second. Log analysis showed human distractors drew more initial selections, while students who chose AI distractors spent more time engaging without differences in hint use or revisits. Transition patterns across attempts suggest AI-generated distractors can effectively guide students toward correct answers, highlighting their potential for scalable MCQ design.
Analysis of Motivations in Machine Learning Textbooks
Authors: Khushi Malik; Amber Richardson; Tingting Zhu; Lisa Zhang
Venue: AAAI 2026 (main)

Abstract: Recent work has explored the interests that draw learners to Machine Learning (ML), aiming to support their success and broaden participation in the field. However, whether strategies used in textbooks align with these interests is unexplored. We perform a thematic analysis of the introductions from ten openly available ML textbooks to identify their motivational strategies and compare them with student interests documented in prior research. We find that textbooks frequently motivate learners in their introductions by setting learning goals, previewing core ML topics to be covered, showcasing applications and current successes, and, less often, by using learner-centered strategies such as reassurance or curiosity prompts. We group these motivations into three overarching themes: theoretical, practical, and learner-centered. These motivations largely align with student interests, particularly in theory and applications, even in textbooks published before the recent surge of ML and Artificial Intelligence. These findings reveal how textbooks frame ML’s value and offer evidence-based guidance for developing future materials that better engage and support diverse learners.
Impact of a Data-driven Teaching Approach on 9th Graders Conceptual Understanding of Machine Learning
Authors: Erik Marx; Thiemo Leonhardt; Nadine Bergner
Venue: AAAI 2026 (main)

Abstract: This study aimed to investigate the impact of a data-driven teaching approach on students’ conceptual understanding of machine learning (ML). To this end, an exemplary intervention was designed and evaluated using a pre- and post-test design and a German-language Concept Inventory on Machine Learning. A total of 83 German ninth-grade students participated in the study. The results revealed significant learning gains related to data handling and the ML workflow. In contrast, conceptions about the inner workings of ML models largely persisted. The effectiveness of the intervention varied depending on context, with greater gains observed in the text generation domain than in facial recognition, highlighting challenges in cross-contextual transfer of understanding. A regression analysis showed no significant influence of students’ pre-instructional conceptions on learning outcomes. These findings demonstrate both the potential and the limitations of data-driven teaching approaches and emphasize the need for more explicit engagement with learners' misconceptions to foster deeper conceptual change.
EduMod-LLM: A Modular Approach for Designing Flexible and Transparent Educational Assistants
Authors: Meenakshi Mittal; Rishi Khare; Mihran Miroyan; Chancharik Mitra; Narges Norouzi
Venue: AAAI 2026 (main)

Abstract: With the growing use of Large Language Model (LLM)-based Question-Answering (QA) systems in education, it
is critical to evaluate their performance across individual
pipeline components. In this work, we introduce EduMod-LLM, a modular function-calling LLM pipeline, and present
a comprehensive evaluation along three key axes: function calling strategies, retrieval methods, and generative language models. Our framework enables fine-grained analysis by isolating and assessing each component. We benchmark function-calling performance across LLMs, compare
our novel structure-aware retrieval method to vector-based
and LLM-scoring baselines, and evaluate various LLMs for
response synthesis. This modular approach reveals specific
failure modes and performance patterns, supporting the development of interpretable and effective educational QA systems. Our findings demonstrate the value of modular function calling in improving system transparency and pedagogical alignment.
Effective Strategies for Teaching Machine Learning
Authors: Firas Moosvi; Fraida Fund; Varada Kolhatkar; Meiying Qin; Thomas Price; Lisa Zhang
Venue: AAAI 2026 (main)

Abstract: As machine learning (ML) becomes integral in more disciplines, introductory courses in the field are attracting increasingly diverse audiences. Design of these introductory ML courses needs to be theoretically sound, but also intuitive, engaging, and accessible to a range of students. Effective teaching of ML must go beyond teaching the theoretical or practical mechanics of algorithms. In this paper, we synthesize effective teaching strategies from 6 experienced ML instructors across 5 institutions to help students define appropriate ML problems, build intuition, develop reasoning skills, and apply models responsibly. We organize these strategies into eight thematic areas: preparing students for success, motivating learners through real-world relevance, integrating ethics and societal impact, avoiding common methodological pitfalls in model evaluation, guiding students on design decisions, adapting effective classroom practices, assessing student learning, and preparing for the future. Each section offers practical examples of classroom-tested activities (or references to existing resources), and in many cases, reflections on our experiences with the strategies. Our aim is for this paper to be a starting point for instructors aiming to improve learning in introductory ML courses. We hope this is a resource-rich guide for teaching ML to diverse learners, grounded in both pedagogy and practice.
LeanTutor: Towards a Verified AI Mathematical Proof Tutor
Authors: Manooshree Patel; Rayna Bhattacharyya; Thomas Lu; Arnav Mehta; Niels Voss; Narges Norouzi; Gireeja Ranade
Venue: AAAI 2026 (main)

Abstract: This paper considers the development of an AI-based
provably-correct mathematical proof tutor. While Large
Language Models (LLMs) allow seamless communication in
natural language, they are error prone. Theorem provers
such as Lean allow for provable-correctness, but these are
hard for students to learn. We present a proof-of-concept
system (LeanTutor) by combining the complementary strengths
of LLMs and theorem provers. LeanTutor is composed of three
modules: (i) an autoformalizer/proof-checker, (ii) a
next-step generator, and (iii) a natural language feedback
generator. To evaluate the system, we introduce PeanoBench,
a dataset of 371 Peano Arithmetic proofs in human-written
natural language and formal language, derived from the
Natural Numbers Game.
AI Unplugged: Embodied Interactions for AI Literacy in Higher Education
Authors: Jennifer M. Reddig; Scott Moon; Kaitlyn Crutcher; Christopher J. MacLellan
Venue: AAAI 2026 (main)

Abstract: As artificial intelligence (AI) becomes increasingly integrated into daily life, higher education must move beyond code-centric instruction to foster holistic AI literacy. We present a novel pedagogical approach that integrates embodied, unplugged activities into a university-level Introduction to AI course. Inspired by the effectiveness of CS Unplugged in K-12 education, our physical, collaborative activities gave students a first-person perspective on AI decision-making. Through interactive games modeling Search Algorithms, Markov Decision Processes, Q-learning, and Hidden Markov Models, students built an intuition for complex AI concepts and more easily transitioned to mathematical formalizations and code implementations. We present four unplugged AI activities, describe how to bridge from unplugged activities to plugged coding tasks, reflect on implementation challenges, and propose refinements. We suggest that unplugged activities can effectively bridge conceptual reasoning and technical skill-building in university-level AI education.
Understanding the Effects of GenAI as No-Code Alternative for Teaching Machine Learning Workflows
Authors: Martin Strobel
Venue: AAAI 2026 (main)

Abstract: Teaching machine learning (ML) workflows to non-programmers
remains a challenge in introductory AI courses.
Traditionally, educators have turned to no-code tools such
as KNIME to lower barriers. With the rise of generative AI
(GenAI), students can now construct ML pipelines through
natural language prompts, potentially offering a new
“no-code” pathway. In a polytechnic-wide elective in
Singapore, students were given the choice of using either
KNIME or a GenAI chatbot for practical exercises and their
semester project. Survey responses, informal interviews,
and classroom observations revealed that both tools
supported conceptual learning, but students’ experiences
diverged: KNIME provided predictability and structured
guidance, while GenAI offered speed and flexibility yet
posed setup challenges and required coding familiarity.
Students valued having a choice, though this complicated
teaching logistics. Our experience suggests that GenAI can
complement—but not yet replace—traditional no-code
platforms, and that the design of introductory activities
is critical for adoption. We share lessons learned for
educators considering GenAI as an alternative in
workflow-based ML education.
Beyond Prompting: AI Safety Education in the Generative AI Era
Authors: Phan Xuan Tan; Eiji Kamioka; Van Nguyen
Venue: AAAI 2026 (main)

Abstract: Generative AI has moved from pilots to everyday practice,
delivering gains in productivity and accessibility while
surfacing present-day risks—hallucinations and reliability
failures, bias and unfairness, prompt-injection attacks,
and so on. These trends make AI safety education a core
competency. In this paper, we survey global AI safety
curricula and, in the Japanese context, observe strong
policy momentum but relatively few courses that explicitly
combine capability instruction with systematic safety
evaluation. In response, we developed a 7-week,
graduate-level intensive at a private science and
engineering university in Japan, with enrollment open to
international exchange students at both the undergraduate
and graduate levels. The curriculum progresses from
machine-learning foundations to generative models and
alignment, with introductory agent topics included to
support risk reasoning. Delivery combines weekly lectures,
invited talks from academia and industry, structured group
discussions, and a final presentation plus a paper-style
final project focused on risk evaluation and mitigation
planning. An end-of-course survey indicates high perceived
learning and positive experience and one student project
later resulted in a peer-reviewed workshop paper at ICLR 2025.
AI Scholars Program: Scaling AI Literacy Through K-12 Outreach
Authors: Xiaoyi Tian; Yasitha Rajapaksha; Ally Limke; Clara DiMarco; Emily Bryans Dobar; Marnie Hill; Jamie Payton; Tiffany Barnes
Venue: AAAI 2026 (main)

Abstract: As artificial intelligence (AI) becomes increasingly
integrated into daily life, there is a critical need for
developing AI literacy across all educational levels.
However, current AI education remains largely confined to
college-level computer science classrooms with limited
access for K-12 learners. We present the AI Scholars
Program, a novel approach that addresses the AI education
gap by preparing college computing students to serve as AI
education ambassadors in their communities and empowering
K-12 teachers to adopt AI education practices in their
classrooms. This experience report presents the curriculum
and its outcomes after one round of refinement. The program
offers structured AI learning through bi-weekly webinars,
resources, and collaborative opportunities to form teams
and conduct community outreach projects. Our program
invited 63 scholars from 30 institutions across the U.S.,
including 51 college students and 12 K-12 teachers. Their
outreach impacted over 230 K-12 learners. We examine
program outcomes for participants and projects through
pre/post surveys measuring computing attitudes and
self-efficacy for teaching AI, scholar interviews, and
outreach project reports. We share lessons learned and
challenges for designing similar programs, highlighting the
importance of involving educators for effective
community-engaged AI education. The program creates a
sustainable pipeline for college students to develop
technical skills and leadership while addressing K-12 AI
education shortages. We contribute insights for scaling AI
literacy and broadening participation in computing.
Large Participation Experiential Learning Activity for Multiagent Systems
Authors: Alan Tsang
Venue: AAAI 2026 (main)

Abstract: Multiagent systems is a key area within artificial intelligence (AI) that explores the behavior of interacting rational agents where the decisions of one agent impact others. Rooted in economic game theory, multiagent systems takes the idea of individual incentives from economic game theory and applies it to distributed computation and decentralized mechanisms.  It examines not only how certain overall economic or computational goals can be accomplished, but also why individual participants will choose to cooperate with reaching that goal.
While multiagent systems is grounded in rigorous mathematical theory, current pedagogical approaches often lack opportunities for students to connect abstract theory with real-world human dynamics. This disconnect is particularly pressing as AI increasingly operates in sociotechnical environments, where understanding human behavior and interaction is critical.
This paper presents the first exploration of using large participation activities to facilitate experiential learning to bridge this gap.  We report on a day-long resource allocation scenario involving up to 43 participants, designed to simulate multiagent interactions under pressure and with meaningful stakes, where learners can apply their theoretical knowledge to analyze and solve emerging problems. We propose ``megagames'' as a powerful pedagogical tool not only for multiagent systems, but also for other domains as well.
Learning to Use AI for Learning: Teaching Responsible Use of AI Chatbot to K-12 Students Through an AI Literacy Module
Authors: Ruiwei Xiao; Xinying Hou; Ying-Jui Tseng; Hsuan Nieu; Guanze Liao; John Stamper; Kenneth R. Koedinger
Venue: AAAI 2026 (main)

Abstract: As Artificial Intelligence (AI) becomes increasingly integrated into daily life, there is a growing need to equip the next generation with the ability to apply, interact with, evaluate, and collaborate with AI systems responsibly. Prior research highlights the urgent demand from K-12 educators to teach students the ethical and effective use of AI for learning. To address this need, we designed a Large-Language Model (LLM)-based module to teach prompting literacy. This includes scenario-based deliberate practice activities with direct interaction with intelligent LLM agents, aiming to foster secondary school students' responsible engagement with AI chatbots. We conducted two iterations of classroom deployment in 11 authentic secondary education classrooms, and evaluated 1) AI-based auto-grader's capability; 2) students' prompting performance and confidence changes towards using AI for learning; and 3) the quality of learning and assessment materials. Results indicated that the AI-based auto-grader could grade student-written prompts with satisfactory quality. In addition, the instructional materials supported students in improving their prompting skills through practice and led to positive shifts in their perceptions of using AI for learning. Furthermore, data from Study 1 informed assessment revisions in Study 2. Analyses of item difficulty and discrimination in Study 2 showed that True/False and open-ended questions could measure prompting literacy more effectively than multiple-choice questions for our target learners. These promising outcomes highlight the potential for broader deployment and highlight the need for broader studies to assess learning effectiveness and assessment design.
The Essentials of AI for Life and Society: A Full-Scale AI Literacy Course Accessible to All
Authors: Zifan Xu; Kristen Procko; Michael Munje; Kristin Patterson; Lea Sabatini; Joydeep Biswas; Peter Stone
Venue: AAAI 2026 (main)

Abstract: In Fall 2023, we introduced a new AI Literacy class called The Essentials of AI for Life and Society (CS 109), a one-credit, seminar course consisting mainly of guest lectures, which was open to the entire university, including students, staff, and faculty. Building on its success and popularity, this paper describes our significant expansion of the course into a full-scale three-credit undergraduate course (CS 309), with an expanded emphasis on student engagement, interactivity, and ethics-related components. To knit together content from the guest lecturers, we implemented a flipped classroom. This model used weekly asynchronous learning modules---integrating pre-recorded expert lectures, collaborative readings, and ethical reflections---which were then unified by the course instructor during a live, interactive discussion session. To maintain the broad accessibility of the material (no prerequisites), the course introduced substantive, non-programming homework assignments in which students applied AI concepts to
grounded, real-world problems. This work culminated in a final project analyzing the ethical and societal implications of a chosen AI tool. The redesigned course received overwhelmingly positive student feedback, highlighting its interactivity, coherence, and accessible
and engaging assignments. This paper details the course's evolution, its pedagogical structure, and the lessons learned in developing a core AI literacy course. All course materials are freely available for others to use and build upon.
Situating Youth Agency in Designing AI & Art Policies
Authors: Safinah Ali; Ayat Abodayeh; Vishesh Kumar; Cynthia Breazeal
Venue: AAAI 2026 (main)

Abstract: AI technologies have long-term societal implications that impact youth, prompting a need for critical AI literacy for students. While current K-12 AI curricula have increasingly integrated societal impact and ethics concepts in AI curricula, there is a need to center youth’s agency in decision-making around AI systems that impact them. In this work, we engaged 94 middle and high school art students in a Policy Design learning activity as a part of an Art and AI learning workshop. Students worked in groups to create policies around AI's use in art, considering stakeholders like artists, AI companies, and consumers. Findings revealed that students developed nuanced, actionable policies that reflected a deep understanding of AI's impact on the art ecosystem, including issues of copyright, artist compensation, and transparency. The activity empowered students to think critically about AI’s ethical implications on various systems in the AI and art ecosystem  and fostered a sense of agency in shaping its future. This work demonstrates the value of integrating policy design into K-12 AI curricula, providing youth with the skills and perspectives to become informed, ethical citizens in an AI-driven world.
AI Education Across the Curriculum: Design and Pilot Study of a Cross-Disciplinary Module Set
Authors: Jie Chao; Rebecca Ellis; Shiyan Jiang; Daria Smyslova; Qiuqing Li; Amato Nocera; Christy Byrd; Zhen Wu; Carolyn P. Rosé; Stephen Callahan; Dianne O'Grady-Cunniff
Venue: AAAI 2026 (main)

Abstract: Artificial intelligence (AI) education has garnered growing
attention from both educational researchers and
practitioners in recent years. Among the various emerging
approaches, integrating AI education across the
curriculum—particularly within core disciplines—offers
distinct advantages. This strategy foregrounds the
inherently interdisciplinary nature of AI and enables
students to investigate its connections with subjects such
as mathematics and English language arts (ELA).
Furthermore, it holds promise for broadening participation
by engaging all students, including those historically
underrepresented and underserved in the field of AI. To
date, most efforts to integrate AI education have been
situated within individual classrooms, often led by a
single teacher. While such initiatives provide valuable
entry points, they overlook the reality that students’
learning experiences span multiple classrooms and
disciplines. As students transition between subjects, they
inevitably synthesize ideas—both consciously and
unconsciously—from diverse instructional contexts.
Recognizing this, we take a whole-school perspective that
considers the cumulative and interconnected nature of
students’ learning experiences. With this perspective, we
explore a coordinated, cross-disciplinary approach in which
students engage with AI through a set of curriculum modules
spanning mathematics, ELA, and social studies. Each module
is discipline-specific yet designed to contribute to a
cohesive, cross-disciplinary exploration of AI. These
modules are further framed by a self-paced introductory
unit, which establishes foundational concepts, and a
culminating application-and-reflection unit, which supports
integration and transfer of learning. This paper describes
the design of the AI Education Across the Curriculum module
set and reports preliminary findings from a pilot
implementation conducted in Spring 2025. By examining both
the pedagogical design and initial findings, we aim to
contribute to the growing body of research on scalable,
equitable, and interdisciplinary models for AI education.
Beetrap-MC: A Minecraft-Based AI Literacy Tool for Teaching Filter Bubbles to Middle School Students
Authors: Erfan Farhadi; Kenneth Fei; Yifan Jiang; Zhen Bai
Venue: AAAI 2026 (main)

Abstract: Recommendation systems shape much of what people, including youth, encounter online, influencing their exposure to information and ideas. Understanding their workings and potential downsides, such as filter bubbles, is increasingly important. At the same time, Minecraft remains one of the most popular and accessible game platforms among students worldwide, making it a promising medium for AI literacy outreach. Building on a previous in-person augmented reality application called BeeTrap, in which players, acting as bees, pollinate flowers and see how similar choices narrow their environment, we created BeeTrap-MC, a Minecraft-based version aimed at broader reach and accessibility. Unlike the original facilitator-led group workshop, BeeTrap-MC is designed for students individual playthrough. We conducted a study with nine middle school participants, using pre-/post-assessments and qualitative interviews to evaluate its effectiveness. Results showed significant learning gains in key AI concepts, such as understanding filter bubbles and their consequences. We also discuss key differences in design, usability and outcomes between BeeTrap-MC and the original, reflecting on trade-offs in adapting a group-based embodied experience into a shorter, self-guided digital format.