2026 ASEE Annual Conference & Exposition

Designing an AI4ALL Course for Establishing AI Literacy as a Baseline Skill for All University Graduates

Presented at DSAI-Session 2: AI Literacy and Course Design for All Disciplines

Artificial Intelligence (AI) is rapidly transforming nearly every sector of society, from healthcare and education to business and the arts. Recent advances in generative AI have further accelerated this shift, enabling students from all disciplines to interact directly with powerful AI tools. As a result, graduates are increasingly expected not only to use AI, but to exercise sound judgment about its capabilities, limitations, and implications. However, most universities lack accessible, credit-bearing AI education for non-Computer Science (non-CS) students, and existing efforts often emphasize tool use or conceptual exposure without explicitly supporting students’ ability to reason critically, act responsibly, and maintain agency when working with AI.

To address this challenge, we present AI4ALL (AI for All), a course designed to establish AI literacy as a foundational competency for all undergraduate students, with particular emphasis on cultivating an AI mindset—the capacity to make informed, reflective, and ethical decisions when engaging with AI systems. The course adopts a project-based learning approach to situate AI use within open-ended, discipline-relevant problem contexts, where students must actively evaluate, justify, and take ownership of AI-informed decisions. The course design is informed by a systematic requirement analysis that integrates two complementary sources of evidence: (1) a review of existing AI literacy resources and educational research, and (2) a multi-stakeholder survey encompassing students, faculty, community members, and industry professionals.

The outcome of this study is a set of design requirements and a prototype project-based course structure that foregrounds conceptual understanding, reflective judgment, and responsible AI use over narrow technical proficiency. We argue that explicitly centering AI mindset within project-based introductory AI education is essential for preparing students to engage thoughtfully with AI in academic, professional, and civic contexts, and we offer AI4ALL as a transferable model for institutions seeking to integrate AI literacy into the undergraduate curriculum.

Authors

Zheng Song University of Michigan - Dearborn [biography]

Dr. Song received his second Ph.D. in Computer Science, with a focus on distributed systems and software engineering, from Virginia Tech in 2020, and his first Ph.D., focused on wireless networking and mobile computing, from Beijing University of Posts and Telecommunications in 2015. He worked as a software engineer at Sina for one year after completing his master’s degree at China Agricultural University in 2009. He received the Distinguished Paper Award at IEEE ICDCS 2024, the NSF CRII Award in 2021, and the Best Paper Award at IEEE EDGE 2019.
Jack Wagner University of Michigan - Dearborn [biography]

Jack Wagner is a Computer and Information Science / CIS Mathematics student at the University of Michigan-Dearborn, concentrating his studies in Artificial Intelligence. His work spans natural language processing, machine learning, and edge computing, with his current research work focusing on AI education and the evaluation of large language models. He will continue his studies by pursuing a Master of Science (Sc.M.) in Computer Science at Brown University, with long-term interests in advancing trustworthy and reliable AI through research and academia.
Chen Wang University of Michigan-Dearborn [biography]

Miss Wang is an Instructional Technologist at UM-Dearborn. She holds a master’s degree in Learning Sciences and Technologies from the University of Pennsylvania and has several conference publications in the field of AI in education.
Mir Abidi University of Michigan - Dearborn [biography]

Mir Abidi is a Data Science student at the University of Michigan-Dearborn with hands-on experience designing and deploying AI systems in the clinical research industry. His work focuses on building intelligent tools that streamline healthcare operations, including AI-powered platforms for patient recruitment, screening, and study management.
Traven Yarbro University of Michigan - Dearborn
Sabyasachi Dutta University of Michigan - Dearborn [biography]

Dr. Sabyasachi Dutta received his B.Sc., M.Sc., and Ph.D. degrees in mathematics from the University of Calcutta, Kolkata, India. From 2017 to 2018, he was a Visiting Scientist with the Indian Statistical Institute, Kolkata. In 2018, he received a Prestigious Fellowship from the National Institute of Information and Communications Technology (NICT), Japan, and subsequently spent one year, from 2018 to 2019, at Kyushu University, Fukuoka, Japan, as a Guest Researcher. He worked as a Postdoctoral Fellow at University of
Calgary, Canada. His research interests include information theoretic cryptography and in building quantum-safe cryptographic protocols. He is currently associated with University of Michigan - Dearborn as Lecturer (Full time) in the Department of Computer and Information Science.
Dr. Bruce R Maxim http://orcid.org/0000-0002-0979-7787 University of Michigan - Dearborn [biography]

Bruce R. Maxim has worked as a software engineer, project manager, professor, author, and consultant for more than forty years. His research interests include software engineering, human computer interaction, game design, social media, artificial intelligence. Dr. Maxim’s professional experience include managing research information systems at a medical school, directing instructional computing activities for a medical campus, and working as a statistical programmer in a department of epidemiology. He served as the chief technology officer for a game development company.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026

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