Date
Publisher
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.
What is the application?
Who age?
Why use AI?
