A Large Language Model-Based System for Socratic Inquiry: Fostering Deep Learning and Memory Consolidation

This paper introduces a novel Large Language Model (LLM)-based system designed to enhance learning effect through Socratic inquiry, thereby fostering deep understanding and longterm knowledge consolidation. Recognizing the challenges of implementing Socratic pedagogy and memory reinforcement principles (as highlighted by the Ebbinghaus forgetting curve) in traditional settings, this research explores a new framework that integrates the power of LLMs with established pedagogical approaches grounded in constructivist learning theory and cognitive load theory. This system, which includes carefully designed AIdriven questioning techniques informed by the Socratic method, dynamic note management, and memory reinforcement strategies guided by the testing effect, was evaluated through a quasiexperimental classroom intervention. The study revealed that students using the LLM-based framework demonstrated significantly better understanding of complex topics, and were also more engaged with the learning process, when compared to students using traditional methods. These results highlight the potential for LLMs to transform educational practices by creating new, more effective and personalized learning pathways that reduce cognitive load and facilitate long-term retention. This research offers practical insights for educators and researchers seeking to leverage AI to promote meaningful learning experiences and to help students internalize knowledge more effectively.