Exploring University Students’ AI Literacy through Reflective Journals

Abstract

University students increasingly use generative artificial intelligence (GenAI) for academic work, yet frequent access can coexist with uncritical reliance, inaccurate output, and uncertainty about authorship and responsible use. Although AI-literacy scales identify broad dimensions of competence, fewer qualitative studies have examined how non-Science, Technology, Engineering and Mathematics students describe AI literacy after a credit-bearing course. This qualitative study explored how university students conceptualized AI literacy and AI-supported learning through end-of-course reflective journals. Using voluntary response sampling, 20 students enrolled in a regular three?credit general education course at a Korean national university consented to the study of their journals. Two findings encompassing five subthemes were identified: (1) AI literacy as critical and ethical academic competence, including competence beyond technical skill, verification and epistemic responsibility, and ethical authorship; and (2) learner?controlled AI-supported learning, including personalized learning support and human-AI collaboration. Participants described AI literacy as involving inquiry, evaluation, transparency, responsibility, and retention of human judgment. Because the study used retrospective self-reports from one course and did not triangulate journals with interviews, observations, or performance measures, the findings represent participants’ perceptions rather than measured competence or causal course effects.

https://doi.org/10.26803/ijlter.25.9.4