{"title":"字节与大脑：医学教育中人工智能反馈与人类导师反馈的比较研究。","authors":"Majid Ali, Ihab Harbieh, Khawaja Husnain Haider","doi":"10.1080/0142159X.2025.2519639","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Timely, high-quality feedback is vital in medical education but increasingly difficult due to rising student numbers and limited faculty. Artificial intelligence (AI) tools offer scalable solutions, yet limited research compares their effectiveness with traditional tutor feedback. This study examined the comparative effectiveness of AI-generated feedback versus human tutor feedback within the medical curriculum.</p><p><strong>Methods: </strong>Second-year medical students (n = 108) received two sets of feedback on a written assignment, one from their tutor and one unedited response from ChatGPT. Students assessed each feedback using a structured online questionnaire focused on key feedback quality criteria.</p><p><strong>Results: </strong>Eighty-five students (79%) completed the evaluation. Tutor feedback was rated significantly higher in clarity and understandability (p < 0.001), relevance (p < 0.001), actionability (p = 0.009), comprehensiveness (p = 0.001), accuracy and reliability (p = 0.003), and overall usefulness (p < 0.001). However, 62.3% of students indicated that both pieces of feedback complemented each other. Open-ended responses aligned with these quantitative findings. .</p><p><strong>Conclusion: </strong>Human tutors currently provide superior feedback in terms of clarity, relevance, and accuracy. Nonetheless, AI-generated feedback shows promise as a complementary tool. A hybrid feedback model integrating AI and human input could enhance the scalability and richness of feedback in medical education.</p>","PeriodicalId":18643,"journal":{"name":"Medical Teacher","volume":" ","pages":"1-11"},"PeriodicalIF":3.3000,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bytes versus brains: A comparative study of AI-generated feedback and human tutor feedback in medical education.\",\"authors\":\"Majid Ali, Ihab Harbieh, Khawaja Husnain Haider\",\"doi\":\"10.1080/0142159X.2025.2519639\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Timely, high-quality feedback is vital in medical education but increasingly difficult due to rising student numbers and limited faculty. Artificial intelligence (AI) tools offer scalable solutions, yet limited research compares their effectiveness with traditional tutor feedback. This study examined the comparative effectiveness of AI-generated feedback versus human tutor feedback within the medical curriculum.</p><p><strong>Methods: </strong>Second-year medical students (n = 108) received two sets of feedback on a written assignment, one from their tutor and one unedited response from ChatGPT. Students assessed each feedback using a structured online questionnaire focused on key feedback quality criteria.</p><p><strong>Results: </strong>Eighty-five students (79%) completed the evaluation. Tutor feedback was rated significantly higher in clarity and understandability (p < 0.001), relevance (p < 0.001), actionability (p = 0.009), comprehensiveness (p = 0.001), accuracy and reliability (p = 0.003), and overall usefulness (p < 0.001). However, 62.3% of students indicated that both pieces of feedback complemented each other. Open-ended responses aligned with these quantitative findings. .</p><p><strong>Conclusion: </strong>Human tutors currently provide superior feedback in terms of clarity, relevance, and accuracy. Nonetheless, AI-generated feedback shows promise as a complementary tool. A hybrid feedback model integrating AI and human input could enhance the scalability and richness of feedback in medical education.</p>\",\"PeriodicalId\":18643,\"journal\":{\"name\":\"Medical Teacher\",\"volume\":\" \",\"pages\":\"1-11\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2025-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Medical Teacher\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://doi.org/10.1080/0142159X.2025.2519639\",\"RegionNum\":2,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"EDUCATION, SCIENTIFIC DISCIPLINES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical Teacher","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1080/0142159X.2025.2519639","RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION, SCIENTIFIC DISCIPLINES","Score":null,"Total":0}
Bytes versus brains: A comparative study of AI-generated feedback and human tutor feedback in medical education.
Introduction: Timely, high-quality feedback is vital in medical education but increasingly difficult due to rising student numbers and limited faculty. Artificial intelligence (AI) tools offer scalable solutions, yet limited research compares their effectiveness with traditional tutor feedback. This study examined the comparative effectiveness of AI-generated feedback versus human tutor feedback within the medical curriculum.
Methods: Second-year medical students (n = 108) received two sets of feedback on a written assignment, one from their tutor and one unedited response from ChatGPT. Students assessed each feedback using a structured online questionnaire focused on key feedback quality criteria.
Results: Eighty-five students (79%) completed the evaluation. Tutor feedback was rated significantly higher in clarity and understandability (p < 0.001), relevance (p < 0.001), actionability (p = 0.009), comprehensiveness (p = 0.001), accuracy and reliability (p = 0.003), and overall usefulness (p < 0.001). However, 62.3% of students indicated that both pieces of feedback complemented each other. Open-ended responses aligned with these quantitative findings. .
Conclusion: Human tutors currently provide superior feedback in terms of clarity, relevance, and accuracy. Nonetheless, AI-generated feedback shows promise as a complementary tool. A hybrid feedback model integrating AI and human input could enhance the scalability and richness of feedback in medical education.
期刊介绍：
Medical Teacher provides accounts of new teaching methods, guidance on structuring courses and assessing achievement, and serves as a forum for communication between medical teachers and those involved in general education. In particular, the journal recognizes the problems teachers have in keeping up-to-date with the developments in educational methods that lead to more effective teaching and learning at a time when the content of the curriculum—from medical procedures to policy changes in health care provision—is also changing. The journal features reports of innovation and research in medical education, case studies, survey articles, practical guidelines, reviews of current literature and book reviews. All articles are peer reviewed.
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