Teaching AI Literacy as a Moving Target: Reflections from an Undergraduate AI Course

Artificial intelligence is now part of the academic, professional, and civic landscapes that students are entering, yet teaching AI at the undergraduate level remains a moving target. This presentation offers a reflection on the experiences of teaching CITA 315 Artificial Intelligence; a broad AI literacy course designed for students with varied technical backgrounds. Rather than presenting a finished model or a set of definitive answers, the session will describe the course as a work in progress, an attempt to help students make sense of AI concepts, tools, promises, risks, and ethical questions during a period of rapid change.

The presentation will reflect on the course’s structure, major topics, classroom activities, assessments, and student responses. CITA 315 introduced students to foundational AI concepts while engaging with generative AI, prompt engineering, AI output evaluation, deepfakes, AI companions, cybersecurity, and workplace applications. The course also examined ethical issues such as bias, trustworthiness, privacy, labor impacts, and environmental concerns. A central lesson from the course was that AI literacy requires more than exposure to new tools. Students needed repeated opportunities to question outputs, examine assumptions, compare claims against evidence, and consider how technical systems affect real people and institutions.

The session will also consider the challenges of teaching a course in a field that changes faster than a traditional syllabus cycle. These challenges included keeping materials current, balancing excitement with skepticism, designing assessments that could not be easily reduced to AI-generated responses, and helping students engage critically. Overall, this presentation will share the experiences of teaching CITA 315 as a regional campus case study in AI literacy education, emphasizing practical lessons learned, unresolved questions, and the ongoing need for flexible, human-centered approaches to teaching AI.

Speaker Biography

Dr. Grace Begany is Associate Professor and Associate Chair in the Department of Applied Career and Digital Technologies at SUNY Cobleskill, where she has taught since 2015. Her courses span digital ethics, artificial intelligence, data analysis, web development, project management, databases, programming, information privacy, digital literacy, and food & technology. Before entering academia, she spent nearly two decades in industry, leading large-scale online and mobile development projects as well as digital analytics initiatives for international, mid-size, and start-up organizations.

Using multi-method techniques, Dr. Begany’s interdisciplinary research examines how people engage with digital technologies. Her work has appeared in journals and conferences including Telematics and Informatics, Government Information Quarterly, Information Polity, Journal of the American Medical Informatics Association, Interacting with Computers, Journal of Music, Technology & Education, and Proceedings of the Association for Information Science & Technology.

She holds a PhD from the University at Albany, an MA from New York University, and a BA from Mount Holyoke College.