Knowledge tracing models have enabled a range of intelligent tutoring systems to provide feedback to students. However, existing methods for knowledge tracing in learning sciences are predominantly reliant on statistical data and instructor-defined knowledge components, making it challenging to integrate AI-generated educational content with traditional established methods. We propose a method for automatically extracting knowledge components from educational content using instruction-tuned large multimodal models. We validate this approach by comprehensively evaluating it against knowledge tracing benchmarks in five domains. Our results indicate that the automatically extracted knowledge components can effectively replace human-tagged labels, offering a promising direction for enhancing intelligent tutoring systems in limited-data scenarios, achieving more explainable assessments in educational settings, and laying the groundwork for automated assessment.

翻译：知识追踪模型已使一系列智能辅导系统能够为学生提供反馈。然而，学习科学中现有的知识追踪方法主要依赖于统计数据与教师定义的知识组件，这使得将人工智能生成的教育内容与传统成熟方法相整合面临挑战。我们提出了一种利用指令微调的大型多模态模型从教育内容中自动提取知识组件的方法。我们通过在五个领域的知识追踪基准上对其进行全面评估来验证该方法的有效性。结果表明，自动提取的知识组件能够有效替代人工标注的标签，为在有限数据场景下增强智能辅导系统、在教育环境中实现更具可解释性的评估以及为自动化评估奠定基础提供了有前景的方向。