Key points are not available for this paper at this time.
Big data and artificial intelligence (AI) dominate educational research discussions. Yet, limited attention is given to how these technologies effectively support personalized learning in higher education. Recognizing that user perceptions influence adoption, this study explores university staff experiences with AI and learning analytics in the Republic of Cyprus, a context rarely examined. Employing an explanatory sequential mixed methods design, data were collected via forty-five questionnaires and two focus groups, gathering the underrepresented voices of all those involved in designing and implementing technology-enhanced learning. Findings indicate that staff view personalization as adapting instruction to learner needs, guided by student-centered pedagogical aims. Using performance indicators and behavioral patterns, learning analytics provides engagement insights, while AI supports task automation and teaching material design. This allows staff to maintain deeper learner interaction and support, promoting transparency, learner engagement and autonomy. However, inadequate tools, limited institutional support, training gaps, and resistance to change hinder implementation. These barriers emphasize the need for capacity building, infrastructure, and professional development. The study’s key contribution is a two-tiered roadmap combining strategic actions and learning design to guide integration of AI and learning analytics in teaching. Despite sampling limitations, these findings offer valuable insights for advancing personalized learning.
Konstantinidou et al. (Mon,) studied this question.