LECTOR: LLM-Enhanced Concept-based Test-Oriented Repetition for Adaptive Spaced Learning

TL;DR

LECTOR is an innovative adaptive scheduling algorithm that uses large language models to improve spaced repetition for language learning, effectively reducing semantic confusion and enhancing success rates.

Contribution

This paper introduces LECTOR, a novel LLM-enhanced algorithm that improves test-oriented spaced repetition by addressing semantic interference and personalizing learning schedules.

Findings

LECTOR achieves a 90.2% success rate, outperforming baseline algorithms.

It effectively reduces errors caused by semantic confusion.

Demonstrates computational efficiency in adaptive learning scenarios.

Abstract

Spaced repetition systems are fundamental to efficient learning and memory retention, but existing algorithms often struggle with semantic interference and personalized adaptation. We present LECTOR (\textbf{L}LM-\textbf{E}nhanced \textbf{C}oncept-based \textbf{T}est-\textbf{O}riented \textbf{R}epetition), a novel adaptive scheduling algorithm specifically designed for test-oriented learning scenarios, particularly language examinations where success rate is paramount. LECTOR leverages large language models for semantic analysis while incorporating personalized learning profiles, addressing the critical challenge of semantic confusion in vocabulary learning by utilizing LLM-powered semantic similarity assessment and integrating it with established spaced repetition principles. Our comprehensive evaluation against six baseline algorithms (SSP-MMC, SM2, HLR, FSRS, ANKI, THRESHOLD) across 100 simulated learners over 100 days demonstrates significant improvements: LECTOR achieves a 90.2\% success rate compared to 88.4\% for the best baseline (SSP-MMC), representing a 2.0\% relative improvement. The algorithm shows particular strength in handling semantically similar concepts, reducing confusion-induced errors while maintaining computational efficiency. Our results establish LECTOR as a promising direction for intelligent tutoring systems and adaptive learning platforms.