arXiv:2609.01591v1 Announce Type: new
Abstract: AI tutors are most useful when they adapt to each student's strengths, weaknesses, and preferred guidance, but evidence about which guidance works for...
By Ke Yang, Chenglong Wang, Michel Galley, Chandan Singh, Jeevana Priya Inala, ChengXiang Zhai, Jianfeng Gao
arXiv:2507.12674v3 Announce Type: replace-cross
Abstract: Evaluating Artificial Intelligence (AI) tutor feedback before deployment requires anticipating student engagement, typically assessed through...
By Rose Niousha, Mihran Miroyan, Abigail O'Neill, Joseph E. Gonzalez, Gireeja Ranade, John DeNero, Narges Norouzi
arXiv:2608. 11259v1 Announce Type: cross Abstract: Many AI tutors leverage large language models (LLMs) today.
By Tushar Udeshi, Anna Khazenzon, Kabir Khan, Nick Breen, RJ Corwin, Chris DiGiano, Kodi Weatherholtz, Marek Zaluski
The paper introduces Disengagement-Aware Student Simulators (DAS2), a protocol that models five learner-engagement states—engaged, gaming, wheel-spinning, off-task, and mixed—to evaluate AI tutor performance before deployment. Using annotated tutoring sessions from ASSISTments09, DAS2’s rule-based labels matched human consensus in 81% of cases, and conditioning simulations on intended states narrowed the correctness-rate gap between simulated and authentic sessions for gaming and wheel-spinning behaviors. The study also compares five AI tutors across these states, finding stable relative rankings but state-specific performance differences, and notes that automated evaluation does not fully align with human judgment.
By Xianghui Meng, Jionghao Lin
arXiv:2609.28470v1 Announce Type: new
Abstract: Artificial intelligence offers an unprecedented opportunity to augment human capabilities, yet progress at the frontier has focused primarily on advanc...
By Curtis Northcutt, Inaara Hasmani, Kevin Feng, Trevor Khangi, Andreas Plesner, Jonas Mueller
MIT Schwarzman College of Computing launched a pilot program that hosted a weeklong summer workshop for higher education faculty. The workshop focused on exploring how AI and machine learning materials can be adapted for use in their classrooms across various disciplines.
By Amanda Diehl | MIT Schwarzman College of Computing