2026 ASEE Annual Conference & Exposition

A Student-Led Workshop for Building AI Literacy through Embodied AI and Robotics

Presented at CIT Technical Session 1: AI in Education and Learning Innovation

Generative artificial intelligence (genAI) has become an everyday study companion for university students, yet most experience it as a disembodied text interface, i.e., a chatbot. Embodied AI, where physical agents such as robots interact with humans and their environment through large language models (LLMs), offers a concrete way to expand AI literacy and curiosity about human–robot collaboration. This paper reports on a pilot in-person workshop designed and led by two undergraduate computer science students who utilized embodied AI by extending the capabilities of a PiCarX robotics kit and coupling its onboard rule-based Python control with real-time API calls to proprietary LLMs. This workshop engaged undergraduate student participants from across engineering disciplines and academic class standing to observe live demonstrations and discuss how LLMs can interpret sensor data, issue control decisions, and engage in natural-language reasoning about physical contexts.
We implemented an IRB approved post-workshop survey (N=30) to assess participants’ attitudes, engagement, and intentions to further explore embodied AI. The survey design was informed by prior instruments commonly used in AI education and human robot interaction research. The survey consisted of Likert style items probing general AI attitudes, perceptions of robots, intrinsic motivation, and technology acceptance. Open-ended questions were based on exploring participants’ curiosity, perceived accessibility, and ethical considerations..
Results indicate strong agreement that the workshop helped students move beyond viewing AI as a purely text-based tool, increased interest in embodied AI, and motivated concrete next steps such as projects, courses, or research involvement. Qualitative responses further suggest that students developed a more nuanced understanding of both the possibilities and limitations of LLM-driven robotic systems. Together, these findings demonstrate how student-led, low-cost demonstrations of embodied AI can support AI literacy and serve as an accessible bridge between abstract AI concepts and tangible engineering practice.

Authors

Prof. SAGNIK NATH http://orcid.org/0000-0002-5225-952X University of California, Santa Cruz [biography]

Sagnik Nath is an Assistant Teaching Professor (tenure track) in the Department of Computer Science and Engineering at the University of California, Santa Cruz. He earned his Ph.D. in Electrical Engineering from Rensselaer Polytechnic Institute in 2020 and his B.E. in Electronics and Communication Engineering from the Indian Institute of Engineering Science and Technology, Shibpur, in 2015. His teaching and research interests span computer architecture, VLSI design, AI literacy, and optimization techniques for small language models, with a particular focus on integrating generative AI into engineering education. He leads projects exploring how embodied AI, data science instruction, and large language models can enhance student engagement and broaden participation in computing.

Dr. Nath is actively involved in the computer science education community, having served multiple times as an Associate Program Chair for the SIGCSE Technical Symposium and as a panel reviewer for the National Science Foundation. He also reviews regularly for leading computer science and engineering education venues, including ITiCSE, CompEd, and ASEE.
Dr. Debarati Basu http://orcid.org/0000-0002-6205-8510 Embry–Riddle Aeronautical University, Daytona Beach [biography]

Dr. Debarati Basu is an Assistant Professor in the Engineering Fundamentals Department in the College of Engineering at the Embry-Riddle Aeronautical University, Daytona Beach campus. She earned her Ph.D. in Engineering Education from Virginia Tech. She received her bachelor's and masters in Computer Science and Engineering. Her research is at the intersection of Engineering Education (EE) and Computing Education Research (CER) for enhancing individualized experiences, specifically focused on educational technologies including GenAI (for learning, engagement, self-directed learning), student-centered curriculum and pedagogy, and support systems for student and faculty success. She primarily teaches freshmen computing students with active learning techniques and focuses on bringing EE and CER into practice.
Ruthwika Gajjala University of California, Santa Cruz
Rachit Verma University of California, Santa Cruz

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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Broadening Participation in Engineering and Engineering Technology
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