Paper Type
ERF
Abstract
The rapid integration of Generative AI (GenAI) into higher education has created an urgent need to investigate GenAI literacy over time. By synthesizing the Unified Theory of Acceptance and Use of Technology (UTAUT), the Expectation-Confirmation Model (ECM), and Social Cognitive Theory (SCT), the GenAI Literacy-Continuance Model (GAILC) is proposed. This model explores the links between a student’s know-how, the perceived quality of AI outputs, and their subsequent ethical awareness and academic performance. A pilot study was conducted using a pre/post-survey design in the first-year Introduction to Personal Computing course. Students completed six hands-on modules, ranging from introduction to AI, prompt engineering, and AI-driven research to agentic systems and ethics. Preliminary results indicate gains in students’ self-efficacy and technical interaction skills. However, these early results suggest that critical evaluation remains a significant problem. The pilot highlighted substantial attrition between phases, providing a clear roadmap for refining data collection in future iterations.
Paper Number
1228
Recommended Citation
Koohikamali, Mehrdad and Zhang, Sonya, "Longitudinal Transformation of Generative AI Literacy in Higher Education" (2026). AMCIS 2026 Proceedings. 2.
https://aisel.aisnet.org/amcis2026/sig_ed/sig_ed/2
Longitudinal Transformation of Generative AI Literacy in Higher Education
The rapid integration of Generative AI (GenAI) into higher education has created an urgent need to investigate GenAI literacy over time. By synthesizing the Unified Theory of Acceptance and Use of Technology (UTAUT), the Expectation-Confirmation Model (ECM), and Social Cognitive Theory (SCT), the GenAI Literacy-Continuance Model (GAILC) is proposed. This model explores the links between a student’s know-how, the perceived quality of AI outputs, and their subsequent ethical awareness and academic performance. A pilot study was conducted using a pre/post-survey design in the first-year Introduction to Personal Computing course. Students completed six hands-on modules, ranging from introduction to AI, prompt engineering, and AI-driven research to agentic systems and ethics. Preliminary results indicate gains in students’ self-efficacy and technical interaction skills. However, these early results suggest that critical evaluation remains a significant problem. The pilot highlighted substantial attrition between phases, providing a clear roadmap for refining data collection in future iterations.
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