Yvonne Lindbäck, Karin Valeskog, Karin Schröder, Sofi Sonesson
{"title":"学习和评估任务的结构化发展,以防止生成人工智能滥用和提高物理治疗教育教师的人工智能素养。","authors":"Yvonne Lindbäck, Karin Valeskog, Karin Schröder, Sofi Sonesson","doi":"10.1177/23821205251378794","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>The rapid emergence of generative artificial intelligence (GAI) in higher education necessitates redesign of learning activities and assessments to uphold academic integrity and foster AI literacy. This article presents a structured approach to developing educational strategies that mitigate GAI misuse while enhancing students' understanding of GAI, with a focus on collaborative faculty engagement and curricular adaptation in physiotherapy education.</p><p><strong>Methods: </strong>Using the Quality Implementation Framework (QIF), we conducted a comprehensive review of all courses within a Swedish physiotherapy program employing a problem-based learning (PBL) model. Faculty-wide, time-bound development initiatives were implemented, including targeted AI literacy training. A student survey was conducted to assess GAI usage patterns and perceptions.</p><p><strong>Results: </strong>Assessment formats were adapted to emphasize clinical reasoning and critical thinking, reducing opportunities for GAI misuse. Standardized guidelines on acceptable GAI use were integrated across all courses. The survey results 2 months after implementation indicated diverse usage patterns: 13% of students reported daily use of GAI, while 24% had never used it. Additionally, 42% felt adequately informed about GAI. Faculty AI literacy and confidence improved through structured group work and feedback, supporting the integration of AI-related tasks into the curriculum.</p><p><strong>Conclusions: </strong>The systematic approach using QIF and PBL, expert support, faculty champions, problem-solving strategies, and feedback, enabled meaningful curricular changes within 4 months. The variability in student GAI use underscores the need for equitable AI literacy education. This approach not only reduced the risk of GAI misuse but also enhanced faculty preparedness, offering a scalable model for other health sciences programs.</p>","PeriodicalId":45121,"journal":{"name":"Journal of Medical Education and Curricular Development","volume":"12 ","pages":"23821205251378794"},"PeriodicalIF":1.6000,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12441287/pdf/","citationCount":"0","resultStr":"{\"title\":\"Structured Development of Learning and Assessment Tasks to Prevent Generative AI Misuse and Enhance AI Literacy in the Faculty in Physiotherapy Education.\",\"authors\":\"Yvonne Lindbäck, Karin Valeskog, Karin Schröder, Sofi Sonesson\",\"doi\":\"10.1177/23821205251378794\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>The rapid emergence of generative artificial intelligence (GAI) in higher education necessitates redesign of learning activities and assessments to uphold academic integrity and foster AI literacy. This article presents a structured approach to developing educational strategies that mitigate GAI misuse while enhancing students' understanding of GAI, with a focus on collaborative faculty engagement and curricular adaptation in physiotherapy education.</p><p><strong>Methods: </strong>Using the Quality Implementation Framework (QIF), we conducted a comprehensive review of all courses within a Swedish physiotherapy program employing a problem-based learning (PBL) model. Faculty-wide, time-bound development initiatives were implemented, including targeted AI literacy training. A student survey was conducted to assess GAI usage patterns and perceptions.</p><p><strong>Results: </strong>Assessment formats were adapted to emphasize clinical reasoning and critical thinking, reducing opportunities for GAI misuse. Standardized guidelines on acceptable GAI use were integrated across all courses. The survey results 2 months after implementation indicated diverse usage patterns: 13% of students reported daily use of GAI, while 24% had never used it. Additionally, 42% felt adequately informed about GAI. Faculty AI literacy and confidence improved through structured group work and feedback, supporting the integration of AI-related tasks into the curriculum.</p><p><strong>Conclusions: </strong>The systematic approach using QIF and PBL, expert support, faculty champions, problem-solving strategies, and feedback, enabled meaningful curricular changes within 4 months. The variability in student GAI use underscores the need for equitable AI literacy education. This approach not only reduced the risk of GAI misuse but also enhanced faculty preparedness, offering a scalable model for other health sciences programs.</p>\",\"PeriodicalId\":45121,\"journal\":{\"name\":\"Journal of Medical Education and Curricular Development\",\"volume\":\"12 \",\"pages\":\"23821205251378794\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2025-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12441287/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Medical Education and Curricular Development\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/23821205251378794\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"EDUCATION, SCIENTIFIC DISCIPLINES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Medical Education and Curricular Development","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/23821205251378794","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"EDUCATION, SCIENTIFIC DISCIPLINES","Score":null,"Total":0}
Structured Development of Learning and Assessment Tasks to Prevent Generative AI Misuse and Enhance AI Literacy in the Faculty in Physiotherapy Education.
Objective: The rapid emergence of generative artificial intelligence (GAI) in higher education necessitates redesign of learning activities and assessments to uphold academic integrity and foster AI literacy. This article presents a structured approach to developing educational strategies that mitigate GAI misuse while enhancing students' understanding of GAI, with a focus on collaborative faculty engagement and curricular adaptation in physiotherapy education.
Methods: Using the Quality Implementation Framework (QIF), we conducted a comprehensive review of all courses within a Swedish physiotherapy program employing a problem-based learning (PBL) model. Faculty-wide, time-bound development initiatives were implemented, including targeted AI literacy training. A student survey was conducted to assess GAI usage patterns and perceptions.
Results: Assessment formats were adapted to emphasize clinical reasoning and critical thinking, reducing opportunities for GAI misuse. Standardized guidelines on acceptable GAI use were integrated across all courses. The survey results 2 months after implementation indicated diverse usage patterns: 13% of students reported daily use of GAI, while 24% had never used it. Additionally, 42% felt adequately informed about GAI. Faculty AI literacy and confidence improved through structured group work and feedback, supporting the integration of AI-related tasks into the curriculum.
Conclusions: The systematic approach using QIF and PBL, expert support, faculty champions, problem-solving strategies, and feedback, enabled meaningful curricular changes within 4 months. The variability in student GAI use underscores the need for equitable AI literacy education. This approach not only reduced the risk of GAI misuse but also enhanced faculty preparedness, offering a scalable model for other health sciences programs.