Prompt Engineering for Curriculum Design
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Abstract
Large Language Models (LLMs) have recently demonstrated successes in a broad range of areas that require language understanding and generation. The design of course curricula is a key part of the educational process, as it helps students understand expectations and learning goals, and teachers to maintain consistency when conducting the course. Thus, there would be benefits if educators could leverage the capabilities of LLMs. The first benefit is the potential for improving course content, which would lead to better learning outcomes for students. Secondly, educators can save time by having an LLM assist in creating course material or assessments. However, many methods of using LLMs in specific applications fine-tune LLMs, which requires task-specific data as well as technical knowledge to perform the fine-tuning. In this work we use Prompt Engineering, a set of methods that aims to improve generated responses from LLMs by altering the input prompt to the LLM. Often, however, advice for designing prompts is very broad, such as "be concise". We utilize prompt patterns to create and propose three reusable, specific, and customizable prompts that can be used to assist in the design of a course syllabus, lesson material, and assessment questions. The use of prompt patterns additionally aims to produce reliable and consistent results from LLMs.