Lecturers' perceptions of the benefits and risks of generative Artificial Intelligence [AI] in teaching, learning and assessment: A case of selected universities in Rwanda

Abstract

This study investigated lecturers' perceptions of the benefits and risks of Generative Artificial Intelligence (GenAI) in teaching, learning, and assessment in selected universities in Rwanda. The study was grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) developed by Venkatesh et al., which identifies performance expectancy, effort expectancy, social influence, and facilitating conditions as the main determinants of technology acceptance and use. UTAUT guided the examination of how lecturers' perceptions of GenAI's usefulness, institutional environment, and potential consequences shape their willingness to integrate it into their practice. The study employed an exploratory qualitative research design. The population comprised university lecturers from five purposively selected universities, including two public and three private institutions. A sample of 40 lecturers, eight from each university, was purposively selected based on their awareness, experience, or exposure to GenAI tools. Data were collected through semi-structured interviews and analysed thematically using Braun and Clarke's six-phase framework. The findings revealed that lecturers perceived GenAI as both beneficial and risky. Benefits included reduced lesson preparation time, generation of instructional materials and assessment items, alternative explanations, enhanced creativity, personalized learning, and improved feedback. However, lecturers identified risks related to academic integrity, student overdependence, reduced independent learning, assessment validity, inaccurate information, fabricated references, privacy, unequal access, and limited institutional preparedness. The study recommended that universities should develop clear and adaptable GenAI policies, provide continuous AI-literacy training for lecturers and students, redesign assessments to emphasize critical thinking, practical application, reflection, and independent work, strengthen verification and contextualization of AI-generated content, and improve digital infrastructure to promote equitable access.