Personalized AI Practice Replicates Learning Rate Regularity at Scale
Recent research demonstrated that students exhibit consistent learning rates across diverse educational contexts. We test these findings using a dataset of 1.8 million (366k post-filtering) student interactions from the digital platform Campus AI providing further evidence to the observation of regularity in learning rate among students. Unlike prior work requiring manual cognitive modeling, Campus AI automatically generates Knowledge Components (KCs) and corresponding exercises, both of which are validated by human experts. This one-to-many mapping facilitates the application of Additive Factors Models to measure learning parameters without complex cognitive modeling. Using mixed-effects...
| Authors | Jocelyn Beauchesne, Christine Maroti, Jeshua Bratman et al. |
|---|---|
| Primary category | cs.CY |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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