# RAG-KT: Cross-platform Explainable Knowledge Tracing with Multi-view Fusion Retrieval Generation

arXiv: https://arxiv.org/abs/2604.10960 · HTML: https://mlsanity.com/paper/2604.10960/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 36 references that are in the map.
- First posted on arXiv 13 Apr 2026.

## Builds on (most cited first, 19 of 19)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [Large Language Models for Education: A Survey and Outlook](https://mlsanity.com/paper/2403.18105/) (2024)
- [Context-Aware Attentive Knowledge Tracing](https://mlsanity.com/paper/2007.12324/) (2020)
- [Dynamic Key-Value Memory Networks for Knowledge Tracing](https://mlsanity.com/paper/1611.08108/) (2016)
- [Instructions and Guide for Diagnostic Questions: The NeurIPS 2020 Education Challenge](https://mlsanity.com/paper/2007.12061/) (2020)
- [Addressing Two Problems in Deep Knowledge Tracing via Prediction-Consistent Regularization](https://mlsanity.com/paper/1806.02180/) (2018)
- [Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory](https://mlsanity.com/paper/1904.11738/) (2019)
- [MiMo-Embodied: X-Embodied Foundation Model Technical Report](https://mlsanity.com/paper/2511.16518/) (2025)
- [Improving Knowledge Tracing via Pre-training Question Embeddings](https://mlsanity.com/paper/2012.05031/) (2020)
- [A Survey of Knowledge Tracing: Models, Variants, and Applications](https://mlsanity.com/paper/2105.15106/) (2021)
- [Enhancing Deep Knowledge Tracing with Auxiliary Tasks](https://mlsanity.com/paper/2302.07942/) (2023)
- [Towards Modeling Learner Performance with Large Language Models](https://mlsanity.com/paper/2403.14661/) (2024)
- [CLST: Cold-Start Mitigation in Knowledge Tracing by Aligning a Generative Language Model as a Students' Knowledge Tracer](https://mlsanity.com/paper/2406.10296/) (2024)
- [Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations](https://mlsanity.com/paper/2112.11209/) (2021)
- [DyGKT: Dynamic Graph Learning for Knowledge Tracing](https://mlsanity.com/paper/2407.20824/) (2024)
- [LLM-KT: Aligning Large Language Models with Knowledge Tracing using a Plug-and-Play Instruction](https://mlsanity.com/paper/2502.02945/) (2025)
- [Cold Start Problem: An Experimental Study of Knowledge Tracing Models with New Students](https://mlsanity.com/paper/2505.21517/) (2025)
- [HISE-KT: Synergizing Heterogeneous Information Networks and LLMs for Explainable Knowledge Tracing with Meta-Path Optimization](https://mlsanity.com/paper/2511.15191/) (2025)

## Built on by (most cited first, 1 of 1)

- [Modeling Student Sensemaking with LLMs and Knowledge-Graph-Guided Inference](https://mlsanity.com/paper/2609.31046/) (2026)

## Abstract (arXiv)

Knowledge Tracing (KT) infers a student's knowledge state from past interactions to predict future performance. Conventional Deep Learning (DL)-based KT models are typically tied to platform-specific identifiers and latent representations, making them hard to transfer and interpret. Large Language Model (LLM)-based methods can be either ungrounded under prompting or overly domain-dependent under fine-tuning. In addition, most existing KT methods are developed and evaluated under a same-distribution assumption. In real deployments, educational data often arise from heterogeneous platforms with substantial distribution shift, which often degrades generalization. To this end, we propose RAG-KT, a retrieval-augmented paradigm that frames cross-platform KT as reliable context constrained inference with LLMs. It builds a unified multi-source structured context with cross-source alignment via Question Group abstractions and retrieves complementary rich and reliable context for each prediction, enabling grounded prediction and interpretable diagnosis. Experiments on three public KT benchmarks demonstrate consistent gains in accuracy and robustness, including strong performance under cross-platform conditions.