# Live Knowledge Tracing: Real-Time Adaptation using Tabular Foundation Models

arXiv: https://arxiv.org/abs/2602.06542 · HTML: https://mlsanity.com/paper/2602.06542/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 6 Feb 2026.

## Builds on (most cited first, 13 of 13)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Transformers learn in-context by gradient descent](https://mlsanity.com/paper/2212.07677/) (2022)
- [TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second](https://mlsanity.com/paper/2207.01848/) (2022)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [TabICL: A Tabular Foundation Model for In-Context Learning on Large Data](https://mlsanity.com/paper/2502.05564/) (2025)
- [Context-Aware Attentive Knowledge Tracing](https://mlsanity.com/paper/2007.12324/) (2020)
- [pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models](https://mlsanity.com/paper/2206.11460/) (2022)
- [Towards Modeling Learner Performance with Large Language Models](https://mlsanity.com/paper/2403.14661/) (2024)
- [Back to the Basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation](https://mlsanity.com/paper/1604.02336/) (2016)
- [Logistic Knowledge Tracing: A Constrained Framework for Learner Modeling](https://mlsanity.com/paper/2005.00869/) (2020)
- [nanoTabPFN: A Lightweight and Educational Reimplementation of TabPFN](https://mlsanity.com/paper/2511.03634/) (2025)
- [FairPFN: A Tabular Foundation Model for Causal Fairness](https://mlsanity.com/paper/2506.07049/) (2025)
- [Cold Start Problem: An Experimental Study of Knowledge Tracing Models with New Students](https://mlsanity.com/paper/2505.21517/) (2025)

## Abstract (arXiv)

Deep knowledge tracing models have achieved significant breakthroughs in modeling student learning trajectories. However, these architectures require substantial training time and are prone to overfitting on datasets with short sequences. In this paper, we explore a new paradigm for knowledge tracing by leveraging tabular foundation models (TFMs). Unlike traditional methods that require offline training on a fixed training set, our approach performs real-time ''live'' knowledge tracing in an online way via in-context learning. TFMs align testing sequences with relevant training sequences at inference time, therefore skipping the training step entirely. We demonstrate, using several datasets of increasing size, that our method achieves competitive predictive performance with up to 53x speedups on average, in a setting where student interactions are observed progressively over time.