Adaptive Learning Systems, Intelligent Tutoring Systems and Students' Learning in Universities in North-Central Nigeria

This study investigated the relationship between Artificial intelligence-based adaptive learning systems, intelligent tutoring systems and students’ learning in universities in North-central Nigeria. The study was guided by two (2) research objectives, questions, and corresponding hypotheses. The study was anchored on the Technology Acceptance Model (TAM) and adopted a correlational research design. The population comprised 10,787 students across nine universities in North-Central, from which a sample size of 399 was selected using a stratified random sampling. Data was collected using a validated questionnaire and analysed through Pearson’s Product-Moment Correlation, and Regression analysis was used to test the formulated hypotheses at a 0.05 level of significance. The finding revealed that both the adaptive learning systems and the intelligent tutoring systems have a weak positive relationship with students’ learning (r=0.055) and were not statistically significant. Consequently, both hypotheses were accepted. The result indicated that although AI technologies have the potential to enhance personalised and interactive learning, their current utilisation in universities within the North-central zone of Nigeria has not significantly influenced students’ learning outcomes. This weak relationship suggests low acceptance and utilisation of AI tools, which aligns with TAM constructs of perceived usefulness and perceived ease of use. The study concludes that the limited impact of these technologies may be linked to low perceived usefulness and ease of use, as well as infrastructural and institutional challenges affecting their adoption. It is recommended that universities should improve technological infrastructure, enhance digital literacy and provide adequate support systems to facilitate the effective integration of AI tools in teaching and learning