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Yun Wang, Zhaojun Ding, Xuansheng Wu, Siyue Sun, Ninghao Liu, Xiaoming Zhai

Automated scoring plays a crucial role in education by reducing the reliance on human raters and offering scalable and immediate evaluation of student work. While large language models (LLMs) have shown strong potential in this task, their use as end-to-end raters faces challenges such as low accuracy, prompt sensitivity, limited interpretability, and rubric misalignment, which hinder practical implementation. To address the limitations, we propose AutoSCORE, a multi-agent LLM framework enhancing automated scoring via rubric-aligned Structured COmponent REcognition. With two agents, AutoSCORE first extracts rubric-relevant components from student responses and encodes them into a structured representation (i.e., Scoring Rubric Component Extraction Agent), which is then used to assign final scores (i.e., Scoring Agent). This design ensures that model reasoning follows a human-like grading process, enhancing interpretability and robustness. We evaluate AutoSCORE on four benchmark datasets from the ASAP benchmark, using both proprietary and open-source LLMs (GPT-4o, LLaMA-3.1-8B, LLaMA-3.1-70B). Across diverse tasks and rubrics, AutoSCORE predominantly improves scoring accuracy, human-machine agreement (QWK, correlations), and reduces error metrics (MAE, RMSE) compared to single-agent baselines, with particularly strong benefits on complex, multidimensional rubrics, and especially large relative gains on smaller LLMs. These results demonstrate that structured component recognition combined with multi-agent design offers a scalable, reliable, and interpretable solution for automated scoring.

Yingqi Wang, Xiaohang Luo

As generative AI rapidly enters higher education, its cognitive, motivational, and social impacts across disciplines remain underexplored. This qualitative study examines disciplinary epistemologies and digital literacy on AI-assisted academic reading among EFL Chinese students. Guided by Cognitive Load Theory and Self-Determination Theory, participants were 46 university students across Biglan's disciplinary dimensions. We analyzed 46 questionnaires and 32 interviews. Students in soft and applied fields more often report AI reducing intrinsic load, supporting deeper semantic elaboration. In pure and hard fields, students tend to use AI as an interactive tool for questioning, but multi-contextual examples are more likely to introduce extraneous load. By contrast, terminology glossing and decomposition of complex sentences are more often applied in soft and applied fields. Excessive reliance is associated with cognitive offloading and an illusory sense of mastery, shaped by digital literacy and metacognitive awareness. Socially, AI sometimes displaces routine exchanges, but when integrated into group contexts, it facilitates collaboration. The study elaborates applications of CLT and SDT by showing how disciplinary and individual factors shape AI’s cognitive and motivational roles. Practically, it proposes discipline-sensitive design principles and metacognitive prompts, pointing to deployable interventions. Ethical approval and consent were obtained.

Ba-Thinh Tran-Le, Patrick Thomas, Nicholas M. Stiffler, Thuy Ngoc Nguyen

Many AI-based code assistants, particularly those powered by Large Language Models (LLMs), provide complete solutions, which can reduce active problem solving and limit incidental learning, the acquisition of knowledge as a byproduct of task engagement. Such learning requires active participation rather than passive acceptance of AI-generated answers, which might be incorrect. This study examines how incidental learning can be supported through guided interaction. We present LeetCoach, an LLM-assisted coding platform that applies a cognitive forcing strategy, prompting learners to reflect and take incremental steps instead of receiving full solutions. Using LeetCode-style questions, we conducted a pilot study with novice and advanced college programmers who completed tasks under assisted and unassisted conditions. Novices showed substantial post-test gains despite receiving AI guidance only during the intervention, suggesting that incidental exposure improved later performance. Advanced learners showed smaller gains. Across both groups, participants required fewer debugging attempts in the post-test compared to earlier stages, indicating improved debugging efficiency and algorithmic understanding. These findings provide early evidence that LLMs can be designed to promote indirect learning while shaping problem-solving strategies. This work offers a proof of concept for cognitively informed tutoring systems in computer science education and discusses implications for integrating LLMs to enhance both immediate outcomes and lasting skill development.

Van-Khanh Tran, Van-Khai Dang, Duc-Huy Nguyen

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.

Harshil Safi, Megha Bansal, Madhu Vadali, Barbara Bruno, Aditi Kothiyal

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.

Ryosuke Nakamoto, Brendan Flanagan, Kohei Nakamura, Hiroaki Ogata

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.

Zachary Nadan, Nathan Goldberg, Roozbeh Aliabadi, Shuhan Li

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.

Yeo Jin Kim, Daeun Hong, Tianshu Wang, Wookhee Min, Snigdha Chaturvedi, Cindy E. Hmelo-Silver, James Lester

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.

Luyang Fang, Tao Wang, Ping Ma, Xiaoming Zhai

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.

Jordan Esiason, Priyanka Khare, Claire Aguiar, Dan Carpenter, Wookhee Min, Seung Lee, Gamze Ozogul, Xiaoying Zheng, James Lester

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.

Sandipan Dhar, Srikanth Raj Chetupalli, Preeti Rao

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.

Jessica Vandenberg, Alex Goslen, Claire Aguiar, Wookhee Min, Veronica Cateté, Bradford Mott

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.

Jessica Vandenberg, Keisha Bailey, Claire Aguiar, Cecilia Xuning Zhang, Danny Schmidt, Treshonda Rutledge, Bradford Mott, Joseph P. Wilson

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.

Sandra Roach, Karis Boyd-Sinkler, Visrut Sudhakar, Shaundra Daily, Miroslav Pajic, Whitney McCoy

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.

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.