Spaced Repetition and Retrieval Practice: Efficient Learning Mechanisms from a Cognitive Psychology Perspective and Their Empowerment by AI

Abstract

This paper explores two efficient learning strategies-spaced repetition and retrieval practice-from the perspective of cognitive psychology, and examines their integration with artificial intelligence (AI) technology. It first establishes the theoretical foundation by elaborating on the human memory processes (encoding, storage, retrieval), Ebbinghaus’s forgetting curve, the spacing effect, and the retrieval practice effect, clarifying how these strategies align with the brain’s memory mechanisms. The paper then analyzes the scientific principles of spaced repetition, including its reliance on memory consolidation theory and desirable difficulty theory, as well as the evolution of interval arrangement algorithms from fixed-interval models, e.g., Leitner System, to modern data-driven and deep learning-based systems, e.g., SSP-MMC, LSTM-HLR. For retrieval practice, it compares the cognitive differences between active retrieval and passive review, evaluates the effectiveness of different retrieval forms (free recall, fill-in-the-blank, multiple-choice), and discusses its application in teaching. Furthermore, the study emphasizes the synergistic effect of combining spaced repetition and retrieval practice (forming “spaced retrieval”). And how AI enhances these strategies-adaptive learning systems use large-scale memory data and machine learning to personalize review plans, optimize retrieval difficulty, and improve learning efficiency. Finally, the paper identifies practical challenges, e.g., initial cognitive load, personalized calibration, and proposes optimization strategies, while outlining future directions for AI-integrated learning systems. Empirical evidence throughout the paper confirms that these strategies significantly boost long-term knowledge retention compared to traditional learning methods.