MERIT: Memory-Enhanced Retrieval for Interpretable Knowledge Tracing

TL;DR

MERIT is a training-free framework that enhances interpretability in knowledge tracing by combining frozen LLM reasoning with structured memory, enabling scalable, accurate, and transparent student performance prediction.

Contribution

MERIT introduces a novel, training-free approach that integrates LLMs with pedagogical memory for interpretable and scalable knowledge tracing.

Findings

Achieves state-of-the-art performance on real-world datasets.

Reduces computational costs compared to fine-tuned models.

Provides transparent reasoning through explicit Chain-of-Thought rationales.

Abstract

Knowledge Tracing (KT) models students' evolving knowledge states to predict future performance, serving as a foundation for personalized education. While traditional deep learning models achieve high accuracy, they often lack interpretability. Large Language Models (LLMs) offer strong reasoning capabilities but struggle with limited context windows and hallucinations. Furthermore, existing LLM-based methods typically require expensive fine-tuning, limiting scalability and adaptability to new data. We propose MERIT (Memory-Enhanced Retrieval for Interpretable Knowledge Tracing), a training-free framework combining frozen LLM reasoning with structured pedagogical memory. Rather than updating parameters, MERIT transforms raw interaction logs into an interpretable memory bank. The framework uses semantic denoising to categorize students into latent cognitive schemas and constructs a paradigm bank where representative error patterns are analyzed offline to generate explicit Chain-of-Thought (CoT) rationales. During inference, a hierarchical routing mechanism retrieves relevant contexts, while a logic-augmented module applies semantic constraints to calibrate predictions. By grounding the LLM in interpretable memory, MERIT achieves state-of-the-art performance on real-world datasets without gradient updates. This approach reduces computational costs and supports dynamic knowledge updates, improving the accessibility and transparency of educational diagnosis.