Lecturers’ Knowledge and Application of Artificial Intelligence-Driven Assessment Systems for Enhancing University Assessment Practices among Education Lecturers in Kwara State University, Malete

Abstract

The growing integration of artificial intelligence (AI) in education necessitates that lecturers implement innovative assessment methods to maintain accuracy, efficiency, and academic integrity. The study investigated education lecturers’ knowledge and application of AI-driven assessment systems in the university. A descriptive survey research design was adopted. The population comprised 71 education lecturers, while a sample of 65 lecturers was selected using the census sampling technique. Data were collected using the Lecturers’ Knowledge and Utilisation of AI-Driven Assessment Systems Questionnaire (LKAUAIQ) adapted from Saadu (2026). The instrument was validated by experts in Early Childhood and Primary Education as well as Measurement and Evaluation and yielded a Cronbach’s Alpha reliability coefficient of 0.87. Data were analysed using mean and standard deviation for research questions, while t-test and ANOVA were used to test hypotheses at a 0.05 level of significance. Findings revealed that lecturers possess a high level of knowledge of AI-driven assessment systems (Mean = 2.60) but apply them to a low extent (Mean = 2.27). It was also found that there was no significant difference in knowledge based on years of teaching experience (F=0.347, p=0.708 >0.0), while a significant difference existed in application based on experience (F=6.78, p=0.002 <0.05). The study concludes that knowledge of AI does not necessarily translate to effective application in assessment practices. Recommendation made that universities strengthen practical AI training, provide ICT support, and implement targeted capacity-building programmes to enhance effective utilisation of AI-driven assessment systems among lecturers.