# A Training-Free Large Reasoning Model-based Knowledge Tracing Framework for Unified Prediction and Prescription

arXiv: https://arxiv.org/abs/2601.01708 · HTML: https://mlsanity.com/paper/2601.01708/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning.
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 5 Jan 2026.

## Placed in

- LLM reasoning / [Test-time compute and efficient thinking](https://mlsanity.com/lr/test-time-compute-and-efficient-thinking/)

## Builds on (most cited first, 8 of 8)

- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [OpenAI o1 System Card](https://mlsanity.com/paper/2412.16720/) (2024)
- [Language Model Can Do Knowledge Tracing: Simple but Effective Method to Integrate Language Model and Knowledge Tracing Task](https://mlsanity.com/paper/2406.02893/) (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)
- [DBE-KT22: A Knowledge Tracing Dataset Based on Online Student Evaluation](https://mlsanity.com/paper/2208.12651/) (2022)
- [A Systematic Review of Knowledge Tracing and Large Language Models in Education: Opportunities, Issues, and Future Research](https://mlsanity.com/paper/2412.09248/) (2024)
- [Not All Options Are Created Equal: Textual Option Weighting for Token-Efficient LLM-Based Knowledge Tracing](https://mlsanity.com/paper/2410.12872/) (2024)
- [FlatFormer: A Flat Transformer Knowledge Tracing Model Based on Cognitive Bias Injection](https://mlsanity.com/paper/2512.06629/) (2025)

## Built on by (most cited first, 3 of 3)

- [MERIT: Memory-Enhanced Retrieval for Interpretable Knowledge Tracing](https://mlsanity.com/paper/2603.22289/) (2026)
- [Hey Chat, Can You Teach Me? Structuring Socratic Dialogue for Human Learning in the Wild](https://mlsanity.com/paper/2606.11744/) (2026)
- [Can a System-One LLM Perform Knowledge Tracing When Few or No Learners Are Logged?](https://mlsanity.com/paper/2610.11135/) (2026)

## Abstract (arXiv)

Knowledge Tracing (KT) aims to estimate a learner's evolving mastery based on interaction histories. Recent studies have explored Large Language Models (LLMs) for KT via autoregressive nature, but such approaches typically require fine-tuning and exhibit unstable or near-random performance. Moreover, prior KT systems primarily focus on prediction and rely on multi-stage pipelines for feedback and recommendation, resulting in increased system complexity and resources. To address this gap, we propose Thinking-KT, a training-free KT framework that incorporates Test-Time Scaling (TTS), enabling even small LLMs to achieve competitive KT performance. Moreover, in this framework, a small LLM can jointly perform KT prediction, personalized feedback generation, and learning recommendation in a unified output without degrading prediction accuracy. Beyond performance, we present the systematic analysis of reasoning traces in KT. Our results demonstrate that TTS is a critical yet underexplored factor in LLM-based KT, and that small LLMs can serve as unified ITS engines.