用AI提升个性化学习效果，让教育更智能高效。
Future-Proofing Programmers: Optimal Knowledge Tracing for AI-Assisted Personalized Education
- 融合贝叶斯知识追踪与信号处理，动态建模学生学习状态。
- 大学实验中显著提升学习成效，优于传统教学工具。
- 适合教育科技开发者与教师，推动智能化教学落地。
学习如何学习正成为一门科学，得益于知识追踪、信号处理与生成式AI的融合，能够建模学生的学习状态并优化教育方案。我们提出CoTutor，一种基于AI的模型，通过引入信号处理技术增强贝叶斯知识追踪，提升学生进展建模能力，并提供自适应反馈与策略。作为AI协作者部署时，CoTutor结合生成式AI与自适应学习技术，在大学试点中展现出可测量的学习成果提升，优于传统教育工具。结果表明其在AI驱动个性化、可扩展性方面具有潜力，也为教育科技中的隐私与伦理问题提供了未来方向。受理查德·哈明关于计算机辅助‘学习如何学习’愿景启发，CoTutor运用凸优化与信号处理，自动化并规模化学习分析，同时保留教学判断权于人类，确保AI助力知识追踪，帮助学习者发现新洞见。
原文摘要 · Abstract (English)
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.
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