Empowering University Educators to Support Generative AI-enabled Learning: Proposing a Competency Framework
Yingying Cha, Yun Dai, Ziyan Lin, Ang Liu, Cher Ping Lim
Abstract
University educators, including engineering educators and other domain-specific educators in higher education, are challenged by the changing expectations on teaching and assessment methods in the era of generative artificial intelligence (GenAI). To address these challenges, it is critical to offer professional training and development programs to university teachers as institutional efforts to empower them with the knowledge and skills required to navigate the complex landscape of higher education. Against this background, this position paper proposes a teacher competency framework that includes three intersecting components: self-empowerment competency, professional and pedagogical competency, and empowerment competency. This framework provides a comprehensive training model that transforms university teachers from domain-specific experts to well-rounded educators, towards the ultimate goal of empowering students. Implications of the framework for education policies, teacher education, and professional development programs are discussed.