Can a System-One LLM Perform Knowledge Tracing When Few or No Learners Are Logged?

The paper investigates whether an off‑the‑shelf System‑One large language model (LLM) can perform knowledge tracing (KT) when few or no learners are logged. On seven datasets, the model—named Jev—achieves a mean AUC of .706 without any target platform data, outperforming 28 deep KT models trained on eight learners and a System‑Two Thinking‑KT approach. Adding a few logged examples and a similar‑learner statistic (JevKT) further improves performance to .722, maintaining superiority over deep KT up to 64 learners and surpassing supervised KT until 128 learners.

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• llms
• fine-tuning