通过大型语言模型扩展基于证据的教学设计专业知识
Scaling Evidence-based Instructional Design Expertise through Large Language Models
AI总结：
本研究探索利用GPT-4扩展基于证据的教学设计专业知识，通过案例展示其在高阶评估和主动学习组件创建中的应用，并提出了有效使用LLMs的最佳实践及未来推荐系统愿景。
摘要：
本文全面探索了在教学设计领域利用大型语言模型（LLMs），特别是GPT-4的方法。以扩展基于证据的教学设计专业知识为核心，我们的研究旨在弥合理论教育研究与实际应用之间的差距。我们讨论了AI驱动内容生成的优势与局限，强调了人类监督在确保教育材料质量方面的必要性。本工作通过两个详细的案例研究加以阐明，我们在其中应用GPT-4为不同课程创建了复杂的高阶评估和主动学习组件。基于我们的经验，提供了在教学设计任务中有效使用LLMs的最佳实践，如利用模板、微调、处理意外输出、实施LLM链、引用参考文献、评估输出、创建评分量规、评分以及生成干扰项。我们还分享了对未来推荐系统的愿景，其中定制的GPT-4将从教育研究中提取教学设计原则，并为用户独特的教育环境创建个性化、有证据支持的策略。我们的研究有助于理解和优化利用AI驱动的语言模型来提升教育成果的潜力。
英文摘要：
This paper presents a comprehensive exploration of leveraging Large Language Models (LLMs), specifically GPT-4, in the field of instructional design. With a focus on scaling evidence-based instructional design expertise, our research aims to bridge the gap between theoretical educational studies and practical implementation. We discuss the benefits and limitations of AI-driven content generation, emphasizing the necessity of human oversight in ensuring the quality of educational materials. This work is elucidated through two detailed case studies where we applied GPT-4 in creating complex higher-order assessments and active learning components for different courses. From our experiences, we provide best practices for effectively using LLMs in instructional design tasks, such as utilizing templates, fine-tuning, handling unexpected output, implementing LLM chains, citing references, evaluating output, creating rubrics, grading, and generating distractors. We also share our vision of a future recommendation system, where a customized GPT-4 extracts instructional design principles from educational studies and creates personalized, evidence-supported strategies for users' unique educational contexts. Our research contributes to understanding and optimally harnessing the potential of AI-driven language models in enhancing educational outcomes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
