{"title":"人工智能批判性教学法：教师专用量表的设计和心理验证，以增强课堂上的批判性思维","authors":"Ali Alqarni","doi":"10.1111/jcal.70039","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Critical thinking is essential in modern education, and artificial intelligence (AI) offers new possibilities for enhancing it. However, the lack of validated tools to assess teachers' AI-integrated pedagogical skills remains a challenge.</p>\n </section>\n \n <section>\n \n <h3> Objectives</h3>\n \n <p>The current study aimed to develop and validate the Artificial Intelligence-Critical Pedagogy Scale (AICPS) to measure teachers' ability to use AI in fostering critical thinking.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>This study was conducted in Saudi Arabia and consisted of two phases. Phase 1 involved item development through a literature review and semi-structured interviews with 17 secondary school teachers, leading to an initial pool of 100 items. After expert reviews and a pilot study, the scale was refined to 47 items. Phase 2 evaluated the psychometric properties of the scale through exploratory factor analysis (EFA), confirmatory factor analysis (CFA), exploratory graph analysis (EGA), reliability assessments and measurement invariance testing across gender. The final sample included 800 secondary school teachers.</p>\n </section>\n \n <section>\n \n <h3> Results and Conclusions</h3>\n \n <p>EFA confirmed a four-factor structure with 39 items. The four factors were Competence in Creating Critical Thinking-Oriented Learning Environments (CCCTOLE), Ability to Provide Dynamic and Innovative Feedback (APDIF), Understanding and Interaction with Emerging Learning Technologies (UIELT) and Creativity in Designing Transformative Learning Activities (CDTLA). CFA demonstrated a good model fit (<i>χ</i><sup>2</sup>/df = 3.24, RMSEA = 0.075, CFI = 0.916 and TLI = 0.909). EGA further supported the four-factor structure. Internal consistency was excellent, with Cronbach's alpha and McDonald's omega above 0.70 for all subscales. Measurement invariance testing confirmed that the scale functions equivalently across gender groups. The AICPS is a reliable and valid tool for assessing teachers' AI-based critical pedagogy skills. Its demonstrated gender invariance suggests its applicability across diverse educational contexts. This scale can guide future teacher training and policy decisions in AI-driven education.</p>\n </section>\n </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"41 3","pages":""},"PeriodicalIF":5.1000,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial Intelligence-Critical Pedagogic: Design and Psychologic Validation of a Teacher-Specific Scale for Enhancing Critical Thinking in Classrooms\",\"authors\":\"Ali Alqarni\",\"doi\":\"10.1111/jcal.70039\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Critical thinking is essential in modern education, and artificial intelligence (AI) offers new possibilities for enhancing it. However, the lack of validated tools to assess teachers' AI-integrated pedagogical skills remains a challenge.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Objectives</h3>\\n \\n <p>The current study aimed to develop and validate the Artificial Intelligence-Critical Pedagogy Scale (AICPS) to measure teachers' ability to use AI in fostering critical thinking.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>This study was conducted in Saudi Arabia and consisted of two phases. Phase 1 involved item development through a literature review and semi-structured interviews with 17 secondary school teachers, leading to an initial pool of 100 items. After expert reviews and a pilot study, the scale was refined to 47 items. Phase 2 evaluated the psychometric properties of the scale through exploratory factor analysis (EFA), confirmatory factor analysis (CFA), exploratory graph analysis (EGA), reliability assessments and measurement invariance testing across gender. The final sample included 800 secondary school teachers.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results and Conclusions</h3>\\n \\n <p>EFA confirmed a four-factor structure with 39 items. The four factors were Competence in Creating Critical Thinking-Oriented Learning Environments (CCCTOLE), Ability to Provide Dynamic and Innovative Feedback (APDIF), Understanding and Interaction with Emerging Learning Technologies (UIELT) and Creativity in Designing Transformative Learning Activities (CDTLA). CFA demonstrated a good model fit (<i>χ</i><sup>2</sup>/df = 3.24, RMSEA = 0.075, CFI = 0.916 and TLI = 0.909). EGA further supported the four-factor structure. Internal consistency was excellent, with Cronbach's alpha and McDonald's omega above 0.70 for all subscales. Measurement invariance testing confirmed that the scale functions equivalently across gender groups. The AICPS is a reliable and valid tool for assessing teachers' AI-based critical pedagogy skills. Its demonstrated gender invariance suggests its applicability across diverse educational contexts. This scale can guide future teacher training and policy decisions in AI-driven education.</p>\\n </section>\\n </div>\",\"PeriodicalId\":48071,\"journal\":{\"name\":\"Journal of Computer Assisted Learning\",\"volume\":\"41 3\",\"pages\":\"\"},\"PeriodicalIF\":5.1000,\"publicationDate\":\"2025-04-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computer Assisted Learning\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/jcal.70039\",\"RegionNum\":2,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computer Assisted Learning","FirstCategoryId":"95","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jcal.70039","RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
Artificial Intelligence-Critical Pedagogic: Design and Psychologic Validation of a Teacher-Specific Scale for Enhancing Critical Thinking in Classrooms
Background
Critical thinking is essential in modern education, and artificial intelligence (AI) offers new possibilities for enhancing it. However, the lack of validated tools to assess teachers' AI-integrated pedagogical skills remains a challenge.
Objectives
The current study aimed to develop and validate the Artificial Intelligence-Critical Pedagogy Scale (AICPS) to measure teachers' ability to use AI in fostering critical thinking.
Methods
This study was conducted in Saudi Arabia and consisted of two phases. Phase 1 involved item development through a literature review and semi-structured interviews with 17 secondary school teachers, leading to an initial pool of 100 items. After expert reviews and a pilot study, the scale was refined to 47 items. Phase 2 evaluated the psychometric properties of the scale through exploratory factor analysis (EFA), confirmatory factor analysis (CFA), exploratory graph analysis (EGA), reliability assessments and measurement invariance testing across gender. The final sample included 800 secondary school teachers.
Results and Conclusions
EFA confirmed a four-factor structure with 39 items. The four factors were Competence in Creating Critical Thinking-Oriented Learning Environments (CCCTOLE), Ability to Provide Dynamic and Innovative Feedback (APDIF), Understanding and Interaction with Emerging Learning Technologies (UIELT) and Creativity in Designing Transformative Learning Activities (CDTLA). CFA demonstrated a good model fit (χ2/df = 3.24, RMSEA = 0.075, CFI = 0.916 and TLI = 0.909). EGA further supported the four-factor structure. Internal consistency was excellent, with Cronbach's alpha and McDonald's omega above 0.70 for all subscales. Measurement invariance testing confirmed that the scale functions equivalently across gender groups. The AICPS is a reliable and valid tool for assessing teachers' AI-based critical pedagogy skills. Its demonstrated gender invariance suggests its applicability across diverse educational contexts. This scale can guide future teacher training and policy decisions in AI-driven education.
期刊介绍：
The Journal of Computer Assisted Learning is an international peer-reviewed journal which covers the whole range of uses of information and communication technology to support learning and knowledge exchange. It aims to provide a medium for communication among researchers as well as a channel linking researchers, practitioners, and policy makers. JCAL is also a rich source of material for master and PhD students in areas such as educational psychology, the learning sciences, instructional technology, instructional design, collaborative learning, intelligent learning systems, learning analytics, open, distance and networked learning, and educational evaluation and assessment. This is the case for formal (e.g., schools), non-formal (e.g., workplace learning) and informal learning (e.g., museums and libraries) situations and environments. Volumes often include one Special Issue which these provides readers with a broad and in-depth perspective on a specific topic. First published in 1985, JCAL continues to have the aim of making the outcomes of contemporary research and experience accessible. During this period there have been major technological advances offering new opportunities and approaches in the use of a wide range of technologies to support learning and knowledge transfer more generally. There is currently much emphasis on the use of network functionality and the challenges its appropriate uses pose to teachers/tutors working with students locally and at a distance. JCAL welcomes: -Empirical reports, single studies or programmatic series of studies on the use of computers and information technologies in learning and assessment -Critical and original meta-reviews of literature on the use of computers for learning -Empirical studies on the design and development of innovative technology-based systems for learning -Conceptual articles on issues relating to the Aims and Scope
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