Faster, Cheaper, More Accurate: Specialised Knowledge Tracing Models Outperform LLMs
Predicting future student responses to questions is particularly valuable for educational learning platforms where it enables effective interventions. One of the key approaches to do this has been through the use of knowledge tracing (KT) models. These are small, domain-specific, temporal models trained on student question-response data. KT models are optimised for high accuracy on specific educational domains and have fast inference and scalable deployments. The rise of Large Language Models (LLMs) motivates us to ask the following questions: (1) How well can LLMs perform at predicting students' future responses to questions? (2) Are LLMs scalable for this domain? (3) How do LLMs compare...
| Authors | Prarthana Bhattacharyya, Joshua Mitton, Ralph Abboud et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 March 2026 |
| Last updated at source | 15 August 2026 |
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