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Long Nguyen, Duc Nguyen, Quan Bui, Dung Phan, Dung Le, Khanh Nguyen, Quynh Vo, Khang Vo, Nam Duong, Anh Dinh 等

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.

Katherine S. Moore, Helen Zhang, Irene Lee

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.

Olli Hilke, Nicolas Pope, Juho Kahila, Henriikka Vartiainen, Teemu Roos, Tuomo Parkki, Matti Tedre

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.

Erfan Farhadi, Kenneth Fei, Yifan Jiang, Zhen Bai

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.

Jie Chao, Rebecca Ellis, Shiyan Jiang, Daria Smyslova, Qiuqing Li, Amato Nocera, Christy Byrd, Zhen Wu, Carolyn P. Rosé, Stephen Callahan 等

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.

Safinah Ali, Ayat Abodayeh, Vishesh Kumar, Cynthia Breazeal

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.

Zifan Xu, Kristen Procko, Michael Munje, Kristin Patterson, Lea Sabatini, Joydeep Biswas, Peter Stone

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.

Ruiwei Xiao, Xinying Hou, Ying-Jui Tseng, Hsuan Nieu, Guanze Liao, John Stamper, Kenneth R. Koedinger

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.

Alan Tsang

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.

Xiaoyi Tian, Yasitha Rajapaksha, Ally Limke, Clara DiMarco, Emily Bryans Dobar, Marnie Hill, Jamie Payton, Tiffany Barnes

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.

Phan Xuan Tan, Eiji Kamioka, Van Nguyen

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.

Martin Strobel

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.

Jennifer M. Reddig, Scott Moon, Kaitlyn Crutcher, Christopher J. MacLellan

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.

Manooshree Patel, Rayna Bhattacharyya, Thomas Lu, Arnav Mehta, Niels Voss, Narges Norouzi, Gireeja Ranade

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.

Firas Moosvi, Fraida Fund, Varada Kolhatkar, Meiying Qin, Thomas Price, Lisa Zhang

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.

Meenakshi Mittal, Rishi Khare, Mihran Miroyan, Chancharik Mitra, Narges Norouzi

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.

Erik Marx, Thiemo Leonhardt, Nadine Bergner

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.

Khushi Malik, Amber Richardson, Tingting Zhu, Lisa Zhang

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.

Zifeng Liu, Hai Li, Jie Chao, Wanli Xing

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.

Maria Kasinidou, Styliani Kleanthous, Jahna Otterbacher, Evgenia Christoforou

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