Enhancing the quality of educators’ AI competency: a mixed-methods study of professional and institutional factors

The rise of artificial intelligence (AI) in education requires educators to move beyond basic literacy toward deeper and more practical competencies. However, limited research has examined how professional and institutional factors jointly influence educators’ ability to adopt AI effectively. Using a sequential mixed-methods approach, this study provides a context-sensitive refinement of AI competency by clarifying its relationship with adjacent constructs such as technological literacy and creativity. Qualitative data were collected through 37 semi-structured interviews and two focus groups, followed by a survey completed by 140 respondents. Structural Equation Modeling-Partial Least Squares (SEM-PLS) was employed to test and validate the proposed model. Results indicate that professional development, digital resources, technological literacy, creativity, and collaboration are significant predictors of AI competency, with creativity and technological literacy emerging as the strongest predictors. The model demonstrated strong explanatory power, emphasizing that effective AI competency extends beyond technical proficiency and requires creative thinking, digital fluency, and supportive institutional environments. The study offers practical implications for universities seeking to strengthen faculty readiness for AI-integrated teaching and learning.