Exploring the Impact of AI on Teaching and Learning in Teacher Education
In this project, we explore the phenomena of the emergence of the use of artificial intelligence in teaching and learning in teacher education. It investigates the educational implications of emerging technologies on the way students learn and how teacher-education institutions teach and develop new pedagogies for future digital technologies. Recent technological advancements and the increasing speed of adopting new technologies in higher teacher education are explored in order to predict the future nature of higher teacher education in a world where artificial intelligence is part of the fabric of our universities. We pinpoint some challenges for institutions of higher teacher education and student learning in the adoption of these technologies for teaching, learning, student support, and administration and explore further directions for research. The rise of AI makes it impossible to ignore a serious debate about its future role of teaching and learning in higher education and what type of choices universities will make in regard to this issue. The current use of technological solutions such as ‘learning management systems’ or effective or inspiring teacher behaviours for teaching and promoting student engagement and self-directed learning: corporate ventures or institutions of higher education? The rise of tech lords and the quasi-monopoly of few tech giants also come with questions regarding the importance of privacy and the possibility of an expert system that can provide feedback to teachers and learners regarding teaching and learning in future. These issues deserve special attention as universities should include this set of risks when thinking about a sustainable future. In this study, we intend to apply cutting edge AI techniques to video data of K-12 classes, and derive valuable information for educational purposes. After completing this project, we expect to compare algorithms to evaluate effective and inspiring teaching in teachers that exerts differential and gradual influences on student learning in the classroom. This study will extend our knowledge gaps by conducting secondary data analysis in local and overseas research projects (i.e., MET, TEGO, and ICALT3). Regarding practical implications for school inspection, teacher professional development and school improvement, the proposed project can provide valuable insights on machine learning on teaching quality evaluation based on videotaped lessons.
Commencement Date :
Completion Date :
Chief Investigator(s) :
Dr KO, Yue On James 高裕安 [APCLC,EPL]
secondary data analysis
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