Learning With AI: A Structured Approach to Student-AI Collaboration

The rapid integration of generative artificial intelligence (AI) into higher education has created an urgent need for instructional models that move beyond tool use toward intentional, pedagogically grounded practice. This study presents a structured assignment design that positions AI as a scaffolded collaborator within undergraduate educational psychology courses. Across a multi-part, semester-long sequence, students engage in concept explanation, self-directed inquiry, AI-supported knowledge construction, and reflective documentation of their learning process. Findings from recurring student artifacts indicate progressive gains in conceptual understanding, integration of research sources, and transfer of knowledge to personally meaningful contexts. Students demonstrate increased capacity for self-regulated learning through iterative prompting, evaluation of AI-generated content, and systematic verification of information. Reflective components further reveal the development of metacognitive awareness and a calibrated, critical stance toward AI, shifting from initial over-trust or skepticism to intentional and responsible use. This work contributes a replicable model for integrating generative AI into higher education that foregrounds epistemic agency, metacognition, and ethical AI literacy. By framing AI as a form of adaptive scaffolding within inquiry-based learning, the assignment supports deeper engagement with disciplinary content while preparing students to navigate AI-rich knowledge environments with autonomy and critical judgment.