GenAI4ELab/papercli-papers huggingface dataset:
Breakable Machine: A K–12 Classroom Game for Transformative AI Literacy Through Spoofing and eXplainable AI (XAI)
Authors: Olli Hilke; Nicolas Pope; Juho Kahila; Henriikka Vartiainen; Teemu Roos; Tuomo Parkki; Matti Tedre
Venue: AAAI 2026 (main)

Abstract: This paper presents an eXplainable AI (XAI)-based classroom game “Breakable Machine” for teaching critical, transformative AI literacy through adversarial play and interrogation of AI systems. Designed for learners aged 10–15, the game invites students to spoof an image classifier by manipulating their appearance or environment in order to trigger high-confidence misclassifications. Rather than focusing on building AI models, this activity centers on breaking them—exposing their brittleness, bias, and vulnerability through hands-on, embodied experimentation. The game includes an XAI view to help students visualize feature saliency, revealing how models attend to specific visual cues. A shared classroom leaderboard fosters collaborative inquiry and comparison of strategies, turning the classroom into a site for collective sensemaking. This approach repositions AI education by treating model failure and misclassification not as problems to be debugged, but as pedagogically rich opportunities to interrogate AI as a sociotechnical system. In doing so, the game supports students in developing data agency, ethical awareness, and a critical stance toward AI systems increasingly embedded in everyday life.
Games of Representation: Developing Card-Based Activities to Teach About Representation and Bias in AI Datasets
Authors: Katherine S. Moore; Helen Zhang; Irene Lee
Venue: AAAI 2026 (main)

Abstract: Adolescents struggle to understand bias and representation in AI, particularly the concept of how datasets used in machine learning can be representative of populations or not. Within early experiments in teaching about investigating bias in AI systems using the Developing AI Literacy (DAILy) curriculum, we observed participating youth struggling to understand what it meant to be represented in the output of AI tools. For example, when using Google Image Search with prompts such as “physicist” and “outdoor recreation,” participating youth did not understand the question, “Are you represented in this outcome?” We saw an opportunity to address this challenge using the Kapor Foundation's Responsible AI and Tech Justice Guide. Drawing insights from three of the six core components of the framework presented in the guide, we developed Games of Representation (GR), a series of three card-based activities using SET game cards to teach concepts of population, sample, dataset, and representation. Through game play, players manipulate
datasets, role-play as stakeholders with competing interests, and explore real-world scenarios where representation matters. The GR games and corresponding guide provide educators, families, and care providers with flexible, hands-on activities for guided, playful conversations with adolescents about AI ethics. This work contributes practical resources for K-12 AI education while addressing critical gaps in youth understanding of statistical bias and stakeholder influence in AI development.
Catching the First Light of Tomorrow: A Hackathon-Based Framework for Introducing High School Students to AI Agents
Authors: Long Nguyen; Duc Nguyen; Quan Bui; Dung Phan; Dung Le; Khanh Nguyen; Quynh Vo; Khang Vo; Nam Duong; Anh Dinh; Tri Trinh; Chi Phan; An Nguyen; Thai Nguyen; Dang Le; Vinh Dang; Tho Quan
Venue: AAAI 2026 (main)

Abstract: Artificial Intelligence (AI), particularly in the form of intelligent AI agents, is transforming education, industry, and everyday life. These agents extend the capabilities of Large Language Models (LLMs) by integrating planning, decision-making, tool use, and multi-agent collaboration, enabling systems that can reason, adapt, and act in dynamic environments. As such systems become integral to modern workplaces and everyday problem solving, early exposure equips high school students with systems thinking, practical problem-solving skills, and ethical awareness, while preparing them to create applications that address real-world needs. Yet most high school AI programs focus on basic model usage and overlook the skills required to design and deploy agentic systems. Existing resources are largely aimed at university learners and assume substantial programming expertise, creating a significant accessibility gap. To address this need, we present a structured hackathon-based framework for introducing high school students to the design and application of AI agents. The framework combines expert-led lectures on core topics such as agent architectures, prompting strategies, reasoning methods, and tool-use protocols with a guided hackathon in which students collaboratively develop domain-specific agent-based chatbots. We provide a complete suite of instructional materials, step-by-step tutorials, and starter code to support hands-on learning, enabling participants to build functional agents capable of reasoning and interacting with external tools. Our approach bridges the gap between AI literacy and practical deployment while fostering creativity, collaboration, and responsible innovation, and our findings suggest that early engagement with agent-based AI design equips students with both technical proficiency and the mindset to shape the AI-driven future.
Bot Blitz: A Scalable Hands-On Workshop for Teaching AI and Robotics Concepts Through Narrative-Driven Problem Solving
Authors: Sandra Roach; Karis Boyd-Sinkler; Visrut Sudhakar; Shaundra Daily; Miroslav Pajic; Whitney McCoy
Venue: AAAI 2026 (main)

Abstract: As artificial intelligence (AI) becomes increasingly prevalent in society, there is a critical need for accessible K-12 educational resources that introduce students to AI and robotics concepts through engaging, hands-on experiences. In this paper, we present a scalable workshop framework that uses narrative-driven problem solving to teach fundamental AI and autonomous systems concepts to students in grades 5-12. Developed through a collaboration between AI researchers and education specialists, Bot Blitz employs Sphero RVR+ robots within immersive storylines ranging from fairground rescue missions for younger students to urban traffic management scenarios for high schoolers. Preliminary observations from workshops with 56 students show high engagement levels and successful completion of programming challenges.
Co-Designing Unplugged Learning Activities with K-2 Teachers for Early AI Literacy Education
Authors: Jessica Vandenberg; Keisha Bailey; Claire Aguiar; Cecilia Xuning Zhang; Danny Schmidt; Treshonda Rutledge; Bradford Mott; Joseph P. Wilson
Venue: AAAI 2026 (main)

Abstract: Introducing AI concepts in the earliest years of schooling can help children make sense of intelligent technologies, yet few resources exist for K–2 classrooms. This paper presents the design and outcomes of a professional development (PD) program supporting K–2 teachers as they explored AI literacy and co-designed unplugged classroom activities. Grounded in AI4K12's Five Big Ideas in AI framework, the PD combined hands-on learning, collaborative design, and micro-teaching opportunities. Guiding activities included Train the AI (pattern recognition), What Happens Next? (consequences of AI use), Who Did the Robot Hear? (data diversity), and Teach the Robot (model training). Educators then created screen-free, English Language Arts-aligned activities using storytelling, sorting, and embodied play to introduce AI topics such as machine learning and fairness in age-appropriate ways. The PD emphasized integrating AI into existing K–2 literacy routines, lowering implementation barriers while supporting vocabulary development, reasoning, and empathy. Teacher reflections revealed growing confidence in adapting AI topics for young learners and highlighted the value of peer collaboration, clear language, and tactile materials.
From Embeddings to Chatbots: Playful NLP Activities for Middle School AI Literacy
Authors: Jessica Vandenberg; Alex Goslen; Claire Aguiar; Wookhee Min; Veronica Cateté; Bradford Mott
Venue: AAAI 2026 (main)

Abstract: As large language models (LLMs) and chatbots become increasingly prevalent, there is an urgent need to create engaging, age-appropriate learning activities that foster foundational AI literacy with a focus on natural language processing (NLP). This paper presents the iterative design and implementation of three instructional activities that introduce middle school learners (ages 11--14) to NLP concepts through playful, hands-on experiences aligned with the AI4K12 Big Idea of Natural Interaction. These activities include: (1) an unplugged card game that develops students' understanding of embeddings and similarity, (2) an unplugged collaborative sentence-generation challenge that illustrates how language models work, and (3) a web-based educational game in which students design and interact with chatbots. Each activity was implemented and refined across multiple educational contexts, including teacher professional development workshops, summer camps, and classroom implementations.
All activities are designed to be easy to set up, requiring only commonly available classroom technology (e.g., laptops) and a few inexpensive materials (e.g., decks of cards), and are supported with facilitation guides and reflection prompts. Early implementations revealed areas for refinement, leading to clearer scaffolding that helped students connect gameplay to underlying NLP concepts, and post-refinement surveys indicated that students found the activities both enjoyable and educational. Findings suggest that blending unplugged and digital formats enhances comprehension, and that tailoring content to students' local contexts supports engagement. By making these activities openly available, this work contributes to the growing ecosystem of K–12 AI education resources and offers practical guidance for integrating NLP concepts into classroom instruction.
Speaker Anonymization for Children's Oral Reading Assessment
Authors: Sandipan Dhar; Srikanth Raj Chetupalli; Preeti Rao
Venue: AAAI 2026 (main)

Abstract: Speaker anonymization aims to modify the speech signal in order to protect the identity of a speaker while preserving the linguistic content. Despite the increasing use of children's voices in educational applications, such as oral reading fluency (ORF) assessment, there is little work on the anonymization aspects. In this work, we investigate the effectiveness of available speaker anonymization methods drawing from traditional speech-production based approaches and a neural codec based method. We investigate the trade-off between privacy protection, measured as the degree of anonymity, and utility preservation, which in the current context of ORF assessment, includes the segmental and suprasegmental features of children’s read speech utterances. We report objective and subjective evaluations using two child-speaker datasets: MPS and SpeechOcean. Our objective evaluation results indicate that the speech-production based method of vocal tract length normalization coupled with pitch-transposition achieves the best balance between privacy and utility. Subjective listening results indicate that naturalness is achievable across methods while the neural method fails to preserve age characteristics, which are more easily controlled by the speech-production driven methods.
An Explanation-Based Classroom Response System for Real-Time Analysis of Undergraduate Students’ Natural Language Explanations
Authors: Jordan Esiason; Priyanka Khare; Claire Aguiar; Dan Carpenter; Wookhee Min; Seung Lee; Gamze Ozogul; Xiaoying Zheng; James Lester
Venue: AAAI 2026 (main)

Abstract: Effective classroom teaching requires instructors to be
responsive to their students, such as by pivoting their
lectures in real-time to address common misconceptions that
their students may have developed. Classroom response
systems such as multiple-choice "clicker" systems are one
method by which instructors can gauge their students’
understanding during classroom lectures, but open-ended
questions that prompt students to engage in
self-explanation are better suited to promoting critical
thinking. Additionally, analyzing students’ natural
language responses typically requires time-consuming manual
analysis, which makes it challenging to implement in a
classroom setting. To address this challenge, we present an
LLM-driven method for automatically assessing students'
responses and generating an aggregated summary of LLM-based
evaluations for their self-explanations during
undergraduate classroom lectures. Our approach extracts
relevant knowledge components for a given question, tags
students’ responses according to whether they correctly
address each knowledge component, and generates class-level
summaries that highlight common misconceptions and gaps in
knowledge to support instructors in pivoting their lectures
in real time. We evaluate the system’s effectiveness at
these tagging and summarization tasks on data from an
undergraduate computer science course, using quantitative
and qualitative metrics such as relevance, sufficiency,
hallucination rate, and alignment with instructional goals
and desired feedback format gathered through instructor
interviews. Results suggest that the explanation-based
classroom response system can accurately analyze students’
natural language explanations.
Generalizable and Efficient Automated Scoring with a Knowledge-Distilled Multi-Task Mixture-of-Experts
Authors: Luyang Fang; Tao Wang; Ping Ma; Xiaoming Zhai
Venue: AAAI 2026 (main)

Abstract: Automated scoring of written constructed responses typically relies on separate models per task, straining computational resources, storage, and maintenance in real-world education settings. We propose UniMoE-Guided, a knowledge-distilled multi-task Mixture-of-Experts (MoE) approach that transfers expertise from multiple task-specific large models (teachers) into a single compact, deployable model (student). The student combines (i) a shared encoder for cross-task representations, (ii) a gated MoE block that balances shared and task-specific processing, and (iii) lightweight task heads. Trained with both ground-truth labels and teacher guidance, the student matches strong task-specific models while being far more efficient to train, store, and deploy. Beyond efficiency, the MoE layer improves transfer and generalization: experts develop reusable skills that boost cross-task performance and enable rapid adaptation to new tasks with minimal additions and tuning. On nine NGSS-aligned science-reasoning tasks (seven for training/evaluation and two held out for adaptation), UniMoE-Guided attains performance comparable to per-task models while using 6x less storage than maintaining separate students, and 87x less than the 20B-parameter teacher. The method offers a practical path toward scalable, reliable, and resource-efficient automated scoring for classroom and large-scale assessment systems.
A Dialogue-Based Learning Analytics Framework for Collaborative Game-Based Learning
Authors: Yeo Jin Kim; Daeun Hong; Tianshu Wang; Wookhee Min; Snigdha Chaturvedi; Cindy E. Hmelo-Silver; James Lester
Venue: AAAI 2026 (main)

Abstract: In computer-supported collaborative learning environments, analyzing student dialogue is essential for understanding collaborative problem-solving behaviors and supporting effective learning. Prior work often treats all dialogue interactions uniformly, failing to capture how specific dialogue interaction differentially impact learning experiences and outcomes. To address this limitation, we introduce a dialogue-based learning analytics framework that integrates weighted temporal clustering of dialogue with large language model-based interpretation. Our framework identifies student interaction patterns most predictive of group learning gains and uses these insights to enable early prediction of learning outcomes and generate pedagogically meaningful interpretations. We evaluate our framework on collaborative dialogue from middle school students engaged in a collaborative game-based learning environment. Our results show that our framework achieves 83.1% accuracy in learning outcome prediction. In addition, expert evaluations and case studies demonstrate that the identified weighted dialogue patterns reflect key collaborative problem-solving behaviors recognized as important in collaborative learning. By surfacing high-impact interaction patterns and enabling prioritized interpretation generation, our framework provides a promising approach for accurately analyzing students’ collaborative dialogue.
“Debate Guru”: Honing Public Speaking Skills Among Secondary School Students with AI Tutoring Systems
Authors: Zachary Nadan; Nathan Goldberg; Roozbeh Aliabadi; Shuhan Li
Venue: AAAI 2026 (main)

Abstract: AI-supported tools have entered K-12 classrooms in recent years to reshape student learning and skill-building. We are particularly interested in AI’s application in literacy subjects, such as English, where students are expected to hone their critical thinking and public speaking skills through AI interactions. This report details the pilot implementation of Debate Guru, an AI-enhanced debate education platform, across two secondary schools with varying instructional contexts. Over the course of a summer school course, educators integrated Debate Guru in one of two ways: 1) by using Debate Guru’s complete curriculum; or 2) by combining platform resources with their own instruction such as a literary text. The pilot was implemented with approximately 50 students across Grade 8-11. Findings suggest significant increases in students’ confidence, argumentative reasoning, and engagement. Teachers reported high usability and strong pedagogical value, while students responded positively to interactive features and AI feedback integration. The pilot demonstrated preliminary usefulness and scalability of AI tutoring systems in debate classes. Future iterations can benefit from longitudinal studies, technical refinements, and school capacity-building to ensure greater generalizability of results and deeper knowledge acquisition.
Explain-from-Stroke: Capturing Invisible Learning Processes Through Handwriting Dynamics Analysis
Authors: Ryosuke Nakamoto; Brendan Flanagan; Kohei Nakamura; Hiroaki Ogata
Venue: AAAI 2026 (main)

Abstract: Educational assessment requires understanding student problem-solving processes, not just final answers. Current AI-driven analytics focus on static outcomes, missing valuable insights from temporal dynamics. Explain-from-Stroke is a practical framework that captures invisible learning processes by integrating handwriting dynamics with vision-language models. The system extracts temporal features such as writing speed, pauses, and revisions, providing additional context for generating meaningful insights into hidden aspects of student reasoning. Using real classroom data from a Japanese secondary school, the model shows an 18.2% improvement in cognitive depth analysis compared with static approaches. This work provides educators with an accessible method to analyze learning processes using standard tablet technology.
Thinking Through the Hands: An Exploratory Study of Hand Movements to Assess Students Problem-Solving in Mechanistic Reasoning Tasks
Authors: Harshil Safi; Megha Bansal; Madhu Vadali; Barbara Bruno; Aditi Kothiyal
Venue: AAAI 2026 (main)

Abstract: Theories of embodied learning emphasize that learning processes are grounded in bodily actions and interactions with the environment, suggesting that movements play a fundamental role in problem solving, decision making, and learning. This perspective holds particular relevance for making-based learning settings, where patterns of movement and spatial engagement can reveal strategic expertise. Prior research has examined distinctions between students who learned and did not learn, but manual coding of actions presents scalability and real-time application challenges. To address this gap, we develop a computer vision–based analysis pipeline for automated detection and characterization of hand movements during complex assembly tasks. In an exploratory study, we apply this approach to video data of students engaged in the assembly of a differential gearbox, quantifying metrics such as amount and speed of movement. Results indicate that learners show fewer right-hand movements than novices and exhibit reduced movement speed, with a progressive decline in speed as the task unfolds. Non-learners, by contrast, display more uneven hand movement speed. These findings, while preliminary, highlight measurable differences in actions of learners and non-learners, and therefore have potential implications for learning support. Specifically, the ability to computationally distinguish movement profiles can inform the design of adaptive learning interventions, providing real-time performance assessment and targeted feedback for making-based learning.
SAGE: A Compositional Multi-Agent LLM Framework with Pedagogical Reasoning for Structured Collaborative Problem Solving
Authors: Van-Khanh Tran; Van-Khai Dang; Duc-Huy Nguyen
Venue: AAAI 2026 (main)

Abstract: While AI can simulate virtual classrooms, effective collaborative learning requires both dynamic interaction and a well-structured pedagogical plan. To address this, we introduce SAGE (Scaffolded Agent-Guided Education), a novel, compositional two-phase framework. First, a planning module automatically generates an optimized pedagogical scenario using a dedicated team of agents. Second, this scenario is used to configure a conversation module, where autonomous agents engage a student in a structured, real-time dialogue. This approach ensures that dynamic, multi-agent interactions are grounded in a pedagogically sound foundation. We evaluate SAGE through simulation and a study with real students. Results show improved performance against a next-speaker prediction baseline (achieving a 72.13% win rate) and demonstrate effective group dynamics. Specifically, our study with students reveals high role adherence from AI agents, a balanced progression between task-oriented and socio-emotional interactions, and a clear scaffolding effect where instructional support fades as learner autonomy increases. Our findings highlight the significant potential of synergizing automated instructional design with autonomous conversational execution for collaborative learning.