Competency Frameworks for Promoting Technology-Assisted Self-Regulated Learning Among Vocational High School Teachers

Technology that uses transformer topologies of machine learning techniques to create new content is known as generative artificial intelligence (genAI). It’s crucial to consider the background of generative artificial intelligence’s ascent in education. Through a literature review, expert interviews, expert review, and the Delphi technique, this study created a set of competency frameworks to support technology-assisted self-regulated learning for vocational high school teachers in Taiwan, given the recent advancements and prevalence of generative AI and its unavoidable use in the teaching and learning processes. The purpose of this study was to determine which aspects of self-regulated learning among vocational high school instructors take into account the use of technology-assisted environments. Qualitative research is conducted with two rounds of structured questionnaires by the Delphi Technique and the assistance of six industrial experts, six educational authority personnel, and six academic scholars specializing in technology-assisted learning to complete the questionnaires. The study identified 3 primary dimensions-Knowledge, Skills, and Attitudes, and 13 secondary dimensions-including 5 for Knowledge (24 competencies), 5 for Skills (18 competencies), and 4 for Attitudes (22 competencies), resulting in a total of 64 competency frameworks. This study provided a set of competency requirements that can serve as a reference guide for teachers’ self-assessment, curriculum planning, teaching goal-setting, and students’ self-regulation. It is anticipated that these discoveries will improve both the effectiveness of students’ learning and the quality of teachers’ instruction.