GenAI4ELab/papercli-papers huggingface dataset:
Can LLMs Reliably Simulate Human Learner Actions? A Simulation Authoring Framework for Open-Ended Learning Environments
Authors: Amogh Mannekote; Adam Davies; Jina Kang; Kristy Elizabeth Boyer
Venue: AAAI 2025 (main)

Abstract: Simulating learner actions helps stress-test open-ended interactive learning environments and prototype new adaptations before deployment. While recent studies show the promise of using large language models (LLMs) for simulating human behavior, such approaches have not gone beyond rudimentary proof-of-concept stages due to key limitations. First, LLMs are highly sensitive to minor prompt variations, raising doubts about their ability to generalize to new scenarios without extensive prompt engineering. Moreover, apparently successful outcomes can often be unreliable, either because domain experts unintentionally guide LLMs to produce expected results, leading to self-fulfilling prophecies; or because the LLM has encountered highly similar scenarios in its training data, meaning that models may not be simulating behavior so much as regurgitating memorized content. To address these challenges, we propose Hyp-Mix, a simulation authoring framework that allows experts to develop and evaluate simulations by combining testable hypotheses about learner behavior. Testing this framework in a physics learning environment, we found that GPT-4 Turbo maintains calibrated behavior even as the underlying learner model changes, providing the first evidence that LLMs can be used to simulate realistic behaviors in open-ended interactive learning environments, a necessary prerequisite for useful LLM behavioral simulation.
Comparing Artificial Intelligence Curricula in Canadian and US Universities
Authors: Rose Niousha; Lexie Jingruo Guo; Rick Kaifeng Li; Narges Norouzi; Lisa Zhang
Venue: AAAI 2025 (main)

Abstract: Artificial Intelligence (AI) has impacted the world tremendously in the last decade, causing an increased demand for accessible AI education globally. Students benefit from studying AI earlier in the curriculum; however, AI courses can require a range of prerequisites, which can be structured differently in various educational contexts. In this paper, we study the curriculum structure of AI, Machine Learning (ML), and Data Science (DS) courses in Canadian Universities and compare it with that of US Research-1 institutions. There are many similarities between AI, ML, and DS courses in Canada and the US. For example, DS courses tend to be more accessible earlier in the CS curriculum compared to AI and ML. However, there are key differences between the two countries, with Canadian AI, ML, and DS courses generally being a part of a longer prerequisites chain, and Canadian CS departments offering fewer DS courses. Still, both Canadian and US institutions find innovative ways to introduce AI earlier in the curriculum, including via interdisciplinary courses and specialized courses with few prerequisites. This study corroborates earlier work in recognizing diversity in curricular frameworks in North America and recommends curricular revisions and early academic advising to ensure access to AI courses.
Using Case Studies to Teach Responsible AI to Industry Practitioners
Authors: Julia Stoyanovich; Rodrigo Kreis de Paula; Armanda Lewis; Chloe Zheng
Venue: AAAI 2025 (main)

Abstract: Responsible AI (RAI) encompasses the science and practice of ensuring that AI design, development, and use are socially sustainable--—maximizing the benefits of technology while mitigating its risks. Industry practitioners play a crucial role in achieving the objectives of RAI, yet there is a persistent a shortage of consolidated educational resources and effective methods for teaching RAI to practitioners.

In this paper, we present a stakeholder-first educational approach using interactive case studies to foster organizational and practitioner-level engagement and enhance learning about RAI. We detail our partnership with Meta, a global technology company, to co-develop and deliver RAI workshops to a diverse company audience. Assessment results show that participants found the workshops engaging and reported an improved understanding of RAI principles, along with increased motivation to apply them in their work.
We Are AI: Taking Control of Technology
Authors: Julia Stoyanovich; Armanda Lewis; Eric Corbett; Lucius  E.J. Bynum; Lucas Rosenblatt; Falaah Arif Khan
Venue: AAAI 2025 (main)

Abstract: Responsible AI (RAI) is the science and practice of ensuring the design, development, use, and oversight of AI are socially sustainable---benefiting diverse stakeholders while controlling the risks. Achieving this goal requires active engagement and participation from the broader public. This paper introduces "We are AI: Taking Control of Technology," a public education course that brings the topics of AI and RAI to the general audience in a peer-learning setting.  

We outline the goals behind the course's development, discuss the multi-year iterative process that shaped its creation, and summarize its content. We also discuss two offerings of "We are AI" to an active and engaged group of librarians and professional staff at New York University, highlighting successes and areas for improvement. The course materials, including a multilingual comic book series by the same name, are publicly available and can be used independently. By sharing our experience in creating and teaching "We are AI", we aim to introduce these resources to the community of AI educators, researchers, and practitioners, supporting their public education efforts.
Towards an AI Course Based on Neural Networks
Authors: Michael Wollowski
Venue: AAAI 2025 (main)

Abstract: In a prior paper, we argued that Artificial Intelligence (AI) should be placed on a different foundation, one based on pattern recognition and feature learning rather than symbol manipulation and feature engineering. In this paper, we provide a proof of concept of an AI course that follows that proposed approach. Students study how these systems become so incredibly powerful through machine learning of features and through pattern matching. Students learn how those systems represent knowledge and they study their currently limited reasoning abilities. Students spend time discussing the accomplishments of current systems, positive as well as negative and they study the projected impact of anticipated systems. In this paper, we give a brief argument of why one would want to offer such a course. We present a detailed outline of the contents of such a course, together with learning materials and their proposed use. We summarize relevant anonymous student feedback and offer a subjective evaluation of the pilot course.
Developing Chatbots for Sustainability: Experiential Learning in an Undergraduate Business Course
Authors: Dailin Zheng; Yu Chen; Yee Kit Chan; Erica Lai; Leslie J. Albert
Venue: AAAI 2025 (main)

Abstract: This paper presents an experiential learning pedagogy that teaches undergraduate business management information systems students hands-on AI skills through the lens of sustainability. The learning modules aim to empower undergraduate business students to gain interest and confidence in AI knowledge, skills, and careers, to sharpen their higher order thinking abilities, and to help them gain a deeper understanding of sustainability issues. Students learn AI through developing chatbots that address pressing sustainability issues within their own communities. Results of the pilot study indicate that students have increased self-efficacy in AI, more positive attitudes towards AI learning and AI-related careers, enhanced sustainability awareness, and more confidence in their ability to innovate.
“AlphAI”: Teaching AI Algorithms to K12 by Training Learning Robots and Visualizing How It Works
Authors: Marie Absalon; Thomas Deneux
Venue: AAAI 2025 (main)

Abstract: Given the massive transformation of all areas of society by AI, it is becoming essential to integrate AI literacy into the various school curricula from an early age. However, teaching the basic concepts of AI and Machine Learning (e.g. training a model; artificial neural networks) at the K-12 level might seem too abstract, whereas teaching only how to use AI fails to really "open the black box".

To overcome these difficulties, we have developed AlphAI, a software resource designed to make the understanding of AI algorithms accessible and attractive to the general public and children as young as 8 years old. This is achieved by making AI very concrete, first by manipulating the learning of educational robots that users train for different behaviors, such as circuit racing, using either supervised or reinforcement learning; second by visualizing in real time in a graphical interface the details of AI algorithms (neural networks, k-nearest neighbors, Q-learning, etc). In addition, the use of the software is not limited to beginners, since it allows to write one's own AI in Python to control the robots.

In this paper, we present the basic principles of the software, its graphical interface, how to use it with various educational robots, and example activities with classes from Elementary school to University.

AlphAI software and robotic kits are commercially available from Learning Robots.
Designing Characters with AI: An Art & AI Learning Activity
Authors: Safinah Ali; Sara Jakubowicz; Ayat Abodayeh; Amaan Zubairi; Dalal Aldossary; Cynthia Breazeal
Venue: AAAI 2025 (main)

Abstract: The growing impact of AI on various fields, including art, highlights the importance of integrating AI learning into art education. This work investigates whether traditional art lessons can be adapted to meaningfully incorporate AI, focusing on its application to art-making practices. We adapted a character design activity to incorporate AI at different stages, such as using AI for creating references, getting feedback, visual design, animation, and personality design. We developed a character design learning activity which was supplemented by a code notebook and a front-end character design tool. 39 middle and high school students participated in this activity during two in-person Art and AI workshops. Analysis of creative outputs, knowledge surveys, and classroom discussions showed that students showed significant shifts in their understanding of AI as a creative collaborator, their art making practice, and their confidence with using AI tools. Learners demonstrated different creative styles while adopting AI into their character design. This approach demonstrates the potential for integrating AI into art lessons and offers a scalable framework for other non-CS subjects.
Understanding K-12 Teachers’ Needs for AI Education: A Survey-Based Study
Authors: Nazan Bautista; John Femiani; Daniela Inclezan
Venue: AAAI 2025 (main)

Abstract: With the rapid rise of AI technologies such as ChatGPT, understanding and integrating AI into K-12 education has become increasingly important. However, teachers often lack the AI literacy necessary to navigate these tools, which can lead to the perpetuation of misconceptions and biases in the classroom. This study seeks to identify K-12 teachers’ self-identified needs regarding AI education and compare them with existing research on professional development (PD) for AI integration. We surveyed 34 K-12 teachers to assess their knowledge of AI, identify areas where they require further support, and evaluate the relevance of current PD offerings. Our findings reveal a significant disconnect between the top-down assumptions of expert-driven PD initiatives and the practical needs articulated by teachers. Key themes emerged, including a diverse range of AI understanding among educators, a strong preference for hands-on, practical training, and a demand for ongoing institutional support. Additionally, teachers expressed a desire for collaborative learning environments to share strategies and experiences related to AI. This study underscores the importance of tailoring PD programs to address the unique contexts and challenges faced by educators, advocating for a more personalized approach that fosters confidence and competence in AI integration. By aligning PD offerings with teachers’ needs, we aim to enhance their ability to effectively utilize AI tools in the classroom, ultimately enriching the educational experience for students.
Advancing Research on Equitable AI Education Through a Focus on Implementation: Insights from a Middle School Computer Vision Module Beta-Test
Authors: Christina A. Bosch; Mary Cate Gustafson-Quiett; Samar Abu Hegly; Sarah Wharton; John Masla; Lydia Guterman; Calvin Macatantan; Eric Klopfer; Hal Abelson; Cynthia Breazeal
Venue: AAAI 2025 (main)

Abstract: Part of a university initiative supporting responsible AI for social empowerment and education, the project-based RAICA (Responsible AI for Computational Action) curriculum supports middle/high school learners and novice AI literacy teachers use AI creatively for good. This paper offers a rare example of design-based implementation research (DBIR) in AI education across widely varied contexts, provides fine grain implementation data that contributes to a foundation for evaluating effectiveness and expanding access. We present a novel approach to analyzing fidelity of implementation data from RAICA’s computer vision module beta-test. Twelve educators working with ~282 students across nine pilot sites in four countries used a bespoke fidelity of implementation data collection tool (pre-made comment prompts in a Google Docs version of the teacher guide) to provide 236 qualitative responses about AI literacy and responsible design activities, plus 111 ordinal ratings of embedded teacher supports. Analyses revealed that while the curriculum was generally implemented as designed, educators frequently made modifications. Although most changes produced practical insights for improved curriculum design, others helped the design team anticipate and prevent changes that could obscure learning objectives and hinder outcomes. We discuss the pedagogical, design, and research implications of these findings for effective AI teaching/learning in diverse settings.
Smart Motor: A Low-Cost Hardware and Software Toolkit for Introducing Supervised Machine Learning to Elementary School Students
Authors: Tanushree Burman; Milan Dahal; Geling Xu; Chris Rogers; Jennifer Cross; Jivko Sinapov
Venue: AAAI 2025 (main)

Abstract: With the rise of Artificial Intelligence (AI) systems in society, our children have routine interactions with these technologies. It has become increasingly important for them to understand how these technologies are trained, what their limitations are and how they work. To introduce children to AI and Machine Learning (ML) concepts, recent efforts introduce tools that integrate ML concepts with physical computing and robotics. However, some of these tools cannot be easily integrated into building projects and the high price of robotics kits can be a limiting factor to many schools. We address these limitations by offering a low-cost hardware and software toolkit that we call the Smart Motor to introduce supervised machine learning to elementary school students. Our Smart Motor uses the nearest neighbor algorithm and utilizes visualizations to highlight the underlying decision-making of the model. We conducted a one week long study using Smart Motors with 9- to 12- year old students and measured their learning through observation, questioning and examining what they built. We found that students were able to integrate the Smart Motors into their building projects but some students struggled with understanding how the underlying model functioned. In this paper we discuss these findings and insights for future directions for the Smart Motor.
Empowering Educators in AI: Insights from Co-Designing an AI Microcredential with and for K-12 Educators
Authors: Nicole M. Hutchins; Shan Zhang; Joanne R. Barrett; Maya Israel
Venue: AAAI 2025 (main)

Abstract: This paper examines the co-design process for a foundational AI microcredential course targeting K-12 teachers' knowledge, agency, and effectiveness in integrating AI into their classrooms. We collaborated with six K-12 teachers and instructional coaches to ensure the course's relevance and practicality. Using conjecture mapping and memoing, we systematically captured and analyzed insights from the collaborative process. These methods helped us pinpoint essential themes and requirements for effective professional development (PD) that meets the unique challenges and opportunities of teaching about and using AI in K-12 classrooms. Themes included concerns about in-class monitoring for unethical impacts of AI integration and the desire for empowerment in evaluating and selecting AI tools that they can best leverage to meet state and national standards. Educator requirements centered on the creation of quick, easily accessible, and asynchronous learning activities. In addition, educators requested just-in-time AI integration resources and learning opportunities that can be leveraged throughout the year, rather than being limited to PD sessions. This study contributes to AI education by providing a framework for designing teacher professional development programs that are responsive to the evolving educational landscape and the specific needs of K-12 teachers.
“From Unseen Needs to Classroom Solutions”: Exploring AI Literacy Challenges & Opportunities with Project-Based Learning Toolkit in K-12 Education
Authors: Hanqi Li; Ruiwei Xiao; Hsuan Nieu; Ying-Jui Tseng; Guanze Liao
Venue: AAAI 2025 (main)

Abstract: As artificial intelligence (AI) becomes increasingly central to various fields, there is a growing need to equip K-12 students with AI literacy skills that extend beyond computer science. This paper explores the integration of a Project-Based Learning (PBL) AI toolkit into diverse subject areas, aimed at helping educators teach AI concepts more effectively. Through interviews and co-design sessions with K-12 teachers, we examined current AI literacy levels and how teachers adapt AI tools like the AI Art Lab, AI Music Studio, and AI Chatbot into their course designs. While teachers appreciated the potential of AI tools to foster creativity and critical thinking, they also expressed concerns about the accuracy, trustworthiness, and ethical implications of AI-generated content. Our findings reveal the challenges teachers face, including limited resources, varying student and instructor skill levels, and the need for scalable, adaptable AI tools. This research contributes insights that can inform the development of AI curricula tailored to diverse educational contexts.
Shaping AI Interest in Rural Middle Schools with Unplugged Learning: Gender Differences and Teacher Insights
Authors: Hansol Lim; Danielle Boulden; Jessica Vandenberg; Veronica Cateté; Wookhee Min; Bradford Mott
Venue: AAAI 2025 (main)

Abstract: Adoption of artificial intelligence (AI) is at an inflection point. With daily use of AI escalating due to widely available software tools, educators, researchers, and policymakers must adapt swiftly to changing educational needs. While think tanks and Big Tech companies often promote the notion that AI serves as a powerful tool for democratizing access to knowledge and opportunities, our work in rural communities underscores the disparity in access to AI education and related opportunities. In this paper, we report on our experience introducing foundational AI concepts to rural middle school students using an unplugged game-based learning activity. By providing engaging learning experiences to rural populations, we hope to broaden interest in and understanding of AI technologies. To this end, we conducted a classroom study in which two middle school teachers implemented our unplugged AI learning activity with their students. Analyzing survey data from 60 of the participating students, we explore the impact of the activity on their interest in AI, their conceptual understanding, and examine potential gender differences. Additionally, we share insights from the teachers who participated in our professional development sessions in preparation for the classroom implementations.
A Versatile Low-Cost Kit for Teaching Novice Learners AI Using Robotics Components and a No-Code Development Playground
Authors: Anssi Lin; Anssi Salonen; Nicolas Pope; Henriikka Vartiainen; Matti Tedre
Venue: AAAI 2025 (main)

Abstract: In the fast-growing field of K–12 AI education, there is an
urgent need for accessible, hands-on tools that introduce AI
concepts and workflows to novice learners. In recent years,
a variety of AI education tools have been introduced, ranging from coding environments to physical kits and robots. To
provide an alternative to existing AI education tools, this paper presents a low-cost robotics kit (<50€)  designed to teach
modern ML concepts through a no-code approach. The kit is
grounded in maker pedagogy and designed for easy customizability to different materials commonly found in classrooms,
like cardboard, wood, metal, and plastic builder kits without
the need for specialized tools. For programming the robot’s
actions, the kit features an all-in-one development studio that
is compatible with most phone, laptop, and tablet platforms
and can operate with or without an Internet connection, making it applicable to a wide range of educational contexts, including ICT4D.
Fostering Epistemic Insights into AI Ethics through a Constructionist Pedagogy: An Interdisciplinary Approach to AI Literacy
Authors: Ziyan Lin; Yun Dai
Venue: AAAI 2025 (main)

Abstract: There is a growing consensus on the importance of AI ethics in K-12 education, yet effective teaching remains a challenge. AI ethics requires an interdisciplinary understanding of computer science, philosophy, and the humanities, alongside epistemic insights into how AI systems acquire, process, and apply knowledge differently from humans. To address this challenge, this study presents the design, development, and implementation of three theory-informed activities aimed at fostering epistemic insight and ethical understanding of AI among upper primary school students (ages 10-12). Grounded in constructionism, our pedagogical design leverages hands-on experimentation with guided reflection to concretize complex AI concepts. Students examine rule-based, data-driven, and generative AI systems, employing mathematical reasoning to represent AI decision-making processes and reflect on ethical issues such as fairness, bias, and transparency. The interdisciplinary, constructionist approach encourages learners to discern how AI knowledge construction differs from human cognition, thereby enhancing their ethical reasoning. The findings show that students not only developed a foundational understanding of ethical principles but also gained epistemic insight into AI’s relationship with human knowledge and values. This article provides a practical, theory-informed framework and interdisciplinary teaching resources to advance K-12 AI ethics education and support educators in fostering AI literacy.