We evaluated the capability of a generative pre-trained transformer (GPT-4) to automatically generate high-quality learning objectives (LOs) in the context of a practically oriented university course on Artificial Intelligence. Discussions of opportunities (e.g., content generation, explanation) and risks (e.g., cheating) of this emerging technology in education have intensified, but to date there has not been a study of the models' capabilities in supporting the course design and authoring of LOs. LOs articulate the knowledge and skills learners are intended to acquire by engaging with a course. To be effective, LOs must focus on what students are intended to achieve, focus on specific cognitive processes, and be measurable. Thus, authoring high-quality LOs is a challenging and time consuming (i.e., expensive) effort. We evaluated 127 LOs that were automatically generated based on a carefully crafted prompt (detailed guidelines on high-quality LOs authoring) submitted to GPT-4 for conceptual modules and projects of an AI Practitioner course. We analyzed the generated LOs if they follow certain best practices such as beginning with action verbs from Bloom's taxonomy in regards to the level of sophistication intended. Our analysis showed that the generated LOs are sensible, properly expressed (e.g., starting with an action verb), and that they largely operate at the appropriate level of Bloom's taxonomy, respecting the different nature of the conceptual modules (lower levels) and projects (higher levels). Our results can be leveraged by instructors and curricular designers wishing to take advantage of the state-of-the-art generative models to support their curricular and course design efforts.

翻译：我们评估了生成式预训练变换模型（GPT-4）在面向实践的人工智能大学课程中自动生成高质量学习目标的能力。当前，关于这一新兴技术在教育领域中机遇（如内容生成、解释说明）与风险（如学术作弊）的讨论日益激烈，但迄今尚未有研究探讨该模型在课程设计及学习目标编写中的支撑作用。学习目标阐明了学习者通过参与课程应掌握的知识与技能，其有效性要求必须聚焦学生预期达成的成果、明确认知过程且具备可测性。因此，编写高质量学习目标是一项具有挑战性且耗时（即成本高昂）的工作。我们针对某人工智能实践课程的概念模块与项目，向GPT-4提交精心设计的提示词（含高质量学习目标编写的详细指南），自动生成了127条学习目标并开展评估。通过分析生成的学习目标是否遵循最佳实践（例如采用布鲁姆分类法中对应预期认知层级的动作动词开头），结果显示：生成的学习目标合理通顺、表述规范（如以动作动词开头），且基本契合布鲁姆分类法的适切层级，能准确区分概念模块（较低层级）与项目（较高层级）的不同特性。本研究结论可为希望能借助前沿生成模型优化课程与教学设计的教育工作者及课程设计者提供实践参考。