Retrieval Interruption Framework: AI-Assisted Cognition and Retrieval-Dependent Learning in Higher Education

Generative artificial intelligence is increasingly used in higher education to provide explanations, solutions, feedback, and organizational support. Although current debates emphasize academic integrity, productivity, and instructional innovation, less attention has been given to how the timing and form of AI assistance may affect learning after that assistance is removed. This conceptual paper develops the Retrieval Interruption Framework (RIF) through a targeted narrative synthesis of research on retrieval practice, productive failure, scaffolding, cognitive load, cognitive offloading, metacognitive monitoring, the expertise reversal effect, the assistance dilemma, and AI-assisted learning. RIF is proposed as an integrative, AI-specific framework rather than a distinct theory of cognition. It distinguishes retrieval-preserving assistance from retrieval-displacing assistance. Retrieval-preserving assistance supports learners after they have attempted task-relevant recall, self-explanation, problem representation, or solution generation. Retrieval-displacing assistance supplies the targeted explanation, solution, or reasoning structure before an initial learner response. The framework predicts that retrieval-displacing assistance may improve immediate performance while weakening delayed unsupported recall, explanation quality, transfer, or metacognitive calibration, particularly in conceptually demanding tasks and among learners with limited prior knowledge. However, early AI guidance may remain productive when it reduces extraneous cognitive load, promotes active processing, fades over time, and is followed by independent performance. RIF reframes AI integration as a sequencing and instructional-design problem. Future research should compare specific forms of AI-first and learner-first assistance using immediate performance measures and delayed unsupported learning outcomes.