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.

本论文探讨了在教学设计中利用大型语言模型（LLMs），尤其是GPT-4的全面应用。我们关注以缩小理论教育研究和实际实施之间的差距为目的，通过扩大基于证据的教学设计专业知识的规模。本文讨论了AI驱动内容生成的益处和局限性，强调人为监督保证教育材料质量的必要性。我们通过两个详细的案例研究阐释了这一工作，其中应用GPT-4创建了复杂的高阶评估和不同课程的主动学习组成部分。根据我们的经验，我们提供有效使用LLM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，创建量表，评分和生成干扰项。我们还分享了我们对未来的推荐系统的愿景，该推荐系统可以根据用户的独特教育背景，定制GPT-4从教育研究中提取教学设计原则，并创建个性化的、具备证据支持的策略。本研究有助于理解和最大限度地利用AI驱动的语言模型潜力，以增强教育成果。

利用大型语言模型扩展基于证据的教学设计专业知识