BUILDING AI READINESS AMONG UNIVERSITY TEACHERS: EFFECTS OF GENERATIVE AI PROFESSIONAL DEVELOPMENT ON ADOPTION INTENTION

Abstract

The rapid emergence of generative artificial intelligence (GenAI) has created new opportunities and challenges for higher education institutions worldwide. While GenAI tools have demonstrated potential to enhance teaching, learning, assessment, and academic support, their successful integration depends largely on educators' ability to understand, evaluate, and responsibly apply these technologies. Existing technology adoption research has primarily focused on perceived usefulness and ease of use, providing limited explanation of the knowledge, confidence, ethical awareness, and professional preparation required for effective AI integration. Therefore, this study proposes an integrated AI readiness framework to examine how generative AI professional development influences university teachers' AI literacy, AI self-efficacy, ethical AI awareness, adoption intention, and classroom AI integration. Drawing upon Social Cognitive Theory, the Technology Acceptance Model, the Unified Theory of Acceptance and Use of Technology, and emerging AI literacy frameworks, this study adopts an explanatory sequential mixed-methods design. The quantitative phase will employ structural equation modeling to test the hypothesized relationships among AI professional development, AI literacy, AI self-efficacy, ethical AI awareness, AI readiness, adoption intention, and classroom AI integration. The qualitative phase will use semi-structured interviews to explore teachers' experiences, challenges, and institutional factors influencing responsible AI adoption. The proposed framework extends traditional technology acceptance perspectives by positioning AI readiness as a central mechanism through which teacher competencies are transformed into adoption behavior. The study contributes to the AI-in-education literature by providing a comprehensive model that integrates technological knowledge, psychological confidence, ethical understanding, and institutional learning support. The findings are expected to provide practical guidance for universities seeking to design effective AI professional development initiatives and develop sustainable strategies for responsible GenAI integration in higher education.