As China continues to advance the Healthy China strategy and a tiered healthcare delivery system, general practice education is becoming relevant not only to students in GP-targeted training programs, but to all clinical medical students. A newly published expert consensus in the Chinese Journal of Medical Education Research focuses on general practice teaching for clinical medical students who are not enrolled in GP-oriented programs. It addresses common challenges such as unclear training objectives, weak practical teaching, and inconsistent faculty and course evaluation systems. The consensus recommends a staged pathway of early exposure, theoretical learning, and practice-based training, calls for Introduction to General Practice to be offered as a required course, and proposes a comprehensive assessment framework covering knowledge, skills, and professional values. It aims to provide a reference for medical schools seeking to standardize general practice education at the institutional education stage.

Artificial intelligence (AI) is increasingly embedded in classroom practice in Hong Kong, following the Education Bureau's release of the Blueprint for Digital Education Development in Primary and Secondary Schools and its commitment to supporting innovative technology in schools. The latest research from The Education University of Hong Kong (EdUHK) underscores that AI's educational value lies not in computational speed, but in its integration with rigorous, evidence-based and student-centred frameworks for assessment. These findings provide timely guidance for Hong Kong's digital transformation in education.

Artificial intelligence is quickly moving into medical education. Hospitals and medical schools are exploring how AI can help generate cases, prepare teaching materials, design assessments, and support more personalized learning. But for clinical teachers, being open to AI does not necessarily mean using it in day-to-day teaching.

A new study published in the Chinese Journal of Medical Education Research highlights that gap. Researchers from Peking University First Hospital conducted a questionnaire survey among clinical teachers at a university-affiliated hospital, distributing 359 questionnaires and collecting 335 valid responses and found that teachers reported relatively high willingness to adopt AI-assisted teaching, but much lower levels of actual use. The finding points to a practical challenge for medical education: AI tools may be widely discussed and increasingly accessible, but their integration into clinical teaching still depends on trust, support, and usable implementation pathways.

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.

A new study from the University of Copenhagen suggests that eating disorders are not necessarily more common among families with a high level of education – but that young people from these families are more likely to be identified, assessed and treated.