# Future-Proofing Programmers: Optimal Knowledge Tracing for AI-Assisted Personalized Education

arXiv: https://arxiv.org/abs/2509.23996 · HTML: https://mlsanity.com/paper/2509.23996/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 28 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [Knowledge Tracing: A Survey](https://mlsanity.com/paper/2201.06953/) (2022)
- [SAINT+: Integrating Temporal Features for EdNet Correctness Prediction](https://mlsanity.com/paper/2010.12042/) (2020)
- [qDKT: Question-centric Deep Knowledge Tracing](https://mlsanity.com/paper/2005.12442/) (2020)
- [Personalized Education in the AI Era: What to Expect Next?](https://mlsanity.com/paper/2101.10074/) (2021)
- [Copilot for Xcode: Exploring AI-Assisted Programming by Prompting Cloud-based Large Language Models](https://mlsanity.com/paper/2307.14349/) (2023)
- [Aligning Crowd-sourced Human Feedback for Reinforcement Learning on Code Generation by Large Language Models](https://mlsanity.com/paper/2503.15129/) (2025)
- [From Code Generation to Software Testing: AI Copilot with Context-Based RAG](https://mlsanity.com/paper/2504.01866/) (2025)

## Built on by (most cited first, 1 of 1)

- [Nemobot Games: Crafting Strategic AI Gaming Agents for Interactive Learning with Large Language Models](https://mlsanity.com/paper/2604.21896/) (2026)

## Abstract (arXiv)

Learning to learn is becoming a science, driven by the convergence of knowledge tracing, signal processing, and generative AI to model student learning states and optimize education. We propose CoTutor, an AI-driven model that enhances Bayesian Knowledge Tracing with signal processing techniques to improve student progress modeling and deliver adaptive feedback and strategies. Deployed as an AI copilot, CoTutor combines generative AI with adaptive learning technology. In university trials, it has demonstrated measurable improvements in learning outcomes while outperforming conventional educational tools. Our results highlight its potential for AI-driven personalization, scalability, and future opportunities for advancing privacy and ethical considerations in educational technology. Inspired by Richard Hamming's vision of computer-aided 'learning to learn,' CoTutor applies convex optimization and signal processing to automate and scale up learning analytics, while reserving pedagogical judgment for humans, ensuring AI facilitates the process of knowledge tracing while enabling learners to uncover new insights.