The rapid advancement of artificial intelligence (AI) has intensified the need for understanding, using, and critically evaluating AI systems. This has contributed to an increase in initiatives integrating AI into education, primarily focusing on technical competence rather than reflective understanding of its use. As the deep learning algorithms that power generative AI continuously improve their performance, so too must the deep learning of the human learner.
Reflection has long been recognized as integral to deep learning as a process through which learners can make sense of experiences, connect theory to practice, and develop informed perspectives within a discipline. This proceeding presents an argument for the generation of a reflective framework for AI education to support students in critically examining their metacognitive processes as they are exposed to the fast, immediate, and seemingly accurate AI outputs during their learning processes. Grounded in research on reflective practice, the framework aims to support students to move beyond surface-level reliance and trust of AI outputs toward critical engagement with AI technologies. In doing so, it seeks to support students to learn and use metacognitive strategies for ethical and responsible participation in an AI-driven world.
http://orcid.org/https://0000-0003-1770-7363
The Ohio State University
[biography]
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