Workshop courses designed to foster creativity are gaining popularity. However, even experienced faculty teams find it challenging to realize a holistic evaluation that accommodates diverse perspectives. Adequate deliberation is essential to integrate varied assessments, but faculty often lack the time for such exchanges. Deriving an average score without discussion undermines the purpose of a holistic evaluation. Therefore, this paper explores the use of a Large Language Model (LLM) as a facilitator to integrate diverse faculty assessments. Scenario-based experiments were conducted to determine if the LLM could integrate diverse evaluations and explain the underlying pedagogical theories to faculty. The results were noteworthy, showing that the LLM can effectively facilitate faculty discussions. Additionally, the LLM demonstrated the capability to create evaluation criteria by generalizing a single scenario-based experiment, leveraging its already acquired pedagogical domain knowledge.

翻译：旨在培养创造力的工作坊课程日益普及。然而，即使是经验丰富的教师团队，要实现容纳多元视角的综合评价也颇具挑战。充分的审议对于整合多样化评估至关重要，但教师往往缺乏进行此类交流的时间。未经讨论直接计算平均分会削弱综合评价的初衷。因此，本文探索使用大语言模型作为协调者来整合教师的不同评估。通过开展基于情境的实验，研究大语言模型能否整合多样化评价并向教师阐释其背后的教学理论。结果值得关注：大语言模型能有效促进教师讨论。此外，该模型展现出通过泛化单次情境实验来创建评价标准的能力，这得益于其已具备的教学领域知识。