Generative artificial intelligence (AI) systems have performed well at many biomedical tasks, but few studies have assessed their performance directly compared to students in higher-education courses. We compared student knowledge-assessment scores with prompting of 6 large-language model (LLM) systems as they would be used by typical students in a large online introductory course in biomedical and health informatics that is taken by graduate, continuing education, and medical students. The state-of-the-art LLM systems were prompted to answer multiple-choice questions (MCQs) and final exam questions. We compared the scores for 139 students (30 graduate students, 85 continuing education students, and 24 medical students) to the LLM systems. All of the LLMs scored between the 50^th and 75^th percentiles of students for MCQ and final exam questions. The performance of LLMs raises questions about student assessment in higher education, especially in courses that are knowledge-based and online.

生成人工智能（AI）系统在许多生物医学任务中表现良好，但很少有研究直接与高等教育课程的学生进行比较来评估其表现。我们将学生的知识评估分数与 6 个大语言模型 ( LLM ) 系统的提示进行了比较，因为典型学生将在研究生、继续教育和医学生学习的生物医学和健康信息学大型在线入门课程中使用这些系统。最先进的法学LLM系统被提示回答多项选择题（MCQ）和期末考试问题。我们将 139 名学生（30 名研究生、85 名继续教育学生和 24 名医学生）的分数与LLM系统进行了比较。所有LLMs的 MCQ 和期末考试问题得分都在学生的 50 ^%到⁷⁵ % 之间。 LLMs的表现引发了有关高等教育中学生评估的问题，特别是在基于知识的在线课程中。