Generative AI is moving into many corners of medical education. One of the most tempting uses is also one of the most time-consuming parts of teaching: assessing student work. For short-answer questions with clear reference answers, large language models have already shown some ability to score responses. But reflective writing is different. It has no single correct answer. It asks students to draw on coursework, clinical exposure, personal experience, and professional values. Can AI still judge such work reliably? A case study published in the Chinese Journal of Medical Education Research offers a cautious answer. Researchers from West China Second University Hospital, Sichuan University; the National Research Center for Educational Materials; and West China Medical Center examined how reasoning-capable large language models performed when assessing reflective writing assignments by medical students. They found that the models could generate relatively complete comments, but still showed clear limitations in scoring consistency, scoring stability, alignment with the grading rubric, and judgments related to academic integrity. The authors conclude that current reasoning-capable large language models are not yet effective tools for independently assessing medical students’ reflective writing. A more realistic path, they argue, is not substitution, but human–AI collaboration.

Fair Chance Hiring policies help reduce barriers to employment for people with criminal records. As companies rethink DEI initiatives, new Michigan State University research shows the way they talk about these policies can have a big impact on how job seekers respond .

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