A CROSS-SECTIONAL QUANTITATIVE STUDY INVESTIGATING LECTURERS’ READINESS AND CHALLENGES IN INTEGRATING AI-DRIVEN TEACHING TOOLS AT MANGOSUTHU UNIVERSITY OF TECHNOLOGY (MUT). - Mbanjwa, Sibonelo Thanda (Student's Journal of Health Research Africa, 2025)
Background Artificial Intelligence (AI) is increasingly transforming teaching and learning in higher education. However, lecturers’ readiness to adopt AI-driven tools and the challenges they face remain under-researched in historically disadvantaged institutions such as Mangosuthu University of Technology (MUT). This study examines lecturers’ preparedness, perceptions, and institutional barriers to integrating AI technologies into teaching practices. Methods This cross-sectional quantitative study involved 50 lecturers and 10 students from the Faculties of Natural Sciences, Engineering, and Management Sciences at Mangosuthu University of Technology (MUT). Lecturers were selected using purposive sampling to ensure variation in teaching experience and familiarity with digital technologies, while students were selected using stratified random sampling to ensure faculty-level representation. Results The findings show that 62% of lecturers felt unprepared to integrate AI into their teaching, mainly due to inadequate training and limited institutional support. Only 30% reported moderate readiness, while 8% felt fully equipped. The most common challenges included lack of infrastructure (70%), absence of AI-specific training (68%), resistance to change (40%), and time constraints (55%). Despite these barriers, 85% acknowledged AI’s potential to enhance student engagement and support personalized learning. Socio-demographic analysis revealed that 56% of participants were male and 44% female, with an age range between 30 and 59 years. Most lecturers (60%) had over 10 years of teaching experience, yet only 40% had prior exposure to educational technology, and few had used AI-specific tools. Conclusion While MUT lecturers recognize the potential benefits of AI, most are not adequately prepared for its integration. Key institutional barriers must be addressed to ensure successful adoption. Recommendations MUT should implement targeted AI training, strengthen technological infrastructure, and create support systems to guide AI adoption. Future research should explore student perceptions to ensure balanced, inclusive integration strategies.