Faster, Cheaper, More Accurate: Specialised Knowledge Tracing Models Outperform LLMs

[2603.02830] Faster, Cheaper, More Accurate: Specialised Knowledge Tracing Models Outperform LLMs Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computation and Language arXiv:2603.02830 (cs) [Submitted on 3 Mar 2026 ( v1 ), last revised 15 Aug 2026 (this version, v2)] Title: Faster, Cheaper, More Accurate: Specialised Knowledge Tracing Models Outperform LLMs Authors: Prarthana Bhattacharyya , Joshua Mitton , Ralph Abboud , Simon Woodhead View a PDF of the paper titled Faster, Cheaper, More Accurate: Specialised Knowledge Tracing Models Outperform LLMs, by Prarthana Bhattacharyya and 2 other authors View PDF HTML (experimental) Abstract: 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 to KT models on this domain-specific task? In this paper, we compare multiple LLMs and KT models across predictive performance, deployment cost, and inference speed to answer the above questions. We show that KT models outperform LLMs with respect to accuracy and F1 scores on this domain-specific task. Further, we demonstrate that LLMs are orders of magnitude slower than KT models and cost orders of magnitude more to deploy. This highlights the importance of domain-specific models for education prediction tasks and the fact that current closed source LLMs should not be used as a universal solution for all tasks. Comments: Published as Poster at EDM 2026. Link: this https URL . 7 pages, 6 figures. Prarthana Bhattacharyya and Joshua Mitton contributed equally to this work Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2603.02830 [cs.CL] (or arXiv:2603.02830v2 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2603.02830 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Prarthana Bhattacharyya [ view email ] [v1] Tue, 3 Mar 2026 10:25:52 UTC (112 KB) [v2] Sat, 15 Aug 2026 13:13:46 UTC (112 KB) Full-text links: Access Paper: View a PDF of the paper titled Faster, Cheaper, More Accurate: Specialised Knowledge Tracing Models Outperform LLMs, by Prarthana Bhattacharyya and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2026-03 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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