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2026 ASEE Annual Conference & Exposition

Teaching the Intuition Behind Artificial Intelligence: Understanding Before Automation

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

Before generative AI reshaped the educational landscape, a course was designed around a simple premise: understand AI before you use it. This paper examines that intuition-first approach through Foundations of Data Science and Machine Learning, originally developed and taught in 2020 and offered again in 2025 using the same conceptual curriculum. Students engaged with foundational AI concepts by hand, without code or libraries. Using a concept-focused written assessment and final project reports from the 2025 offering, we analyze how students reason about model assumptions, evaluation tradeoffs, and failure modes. Results show consistent evidence of conceptual reasoning, including the ability
to articulate why models fail and what probabilistic systems cannot know. These forms of reasoning align with the critical thinking that generative AI literacy requires, despite generative AI systems never being explicitly taught. This case also illustrates how institutional constraints on AI coursework can narrow interdisciplinary access while conceptual depth remains intact. The findings suggest that intuition-first instruction provides a durable foundation for AI literacy that remains relevant as tools evolve.

Authors

Dr. Tracy Anne Hammond http://orcid.org/https://0000-0001-7272-0507 Texas A&M University [biography]

Dr. Tracy Anne Hammond is a Professor of Computer Science and Engineering at Texas A&M University and Director of the Sketch Recognition Lab. She earned her Ph.D. in Electrical Engineering and Computer Science from the MIT Computer Science and Artificial Intelligence Laboratory at Massachusetts Institute of Technology, along with additional degrees from Columbia University in computer science, mathematics, applied physics, and anthropology.

Her research focuses on artificial intelligence, sketch recognition, intelligent user interfaces, haptics, and engineering education, with applications in education, health, accessibility, and related domains. She contributes to research, teaching, and professional service in AI and engineering education, with an emphasis on responsible and inclusive design.
Jobin Varughese http://orcid.org/https://0009-0002-9322-4179 Texas A&M University [biography]

Jobin Varughese is a doctoral student in the College of Engineering at Texas A&M University, where he also serves as Senior Educational Data Analyst at the Center for Teaching Excellence. He holds an M.S. in Management Information Systems with an emphasis in Data Science from Oklahoma State University and a B.S. in Electronics and Communication Engineering from Christ University. His research draws on learning analytics, AI in education, and human-computer interaction to observe, build, and study systems that learn how people learn. In this work, he partners closely with faculty, instructional staff, and students — believing that the most meaningful scholarship emerges not from research alone, but from collaborations with the people closest to the learning experience.

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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