LECTURERS’ DIGITAL LITERACY SKILLS AND APPLICATION OF ARTIFICIAL INTELLIGENCE AS PREDICTORS OF ONLINE LEARNING OUTCOMES AMONG UNDERGRADUATES IN FEDERAL UNIVERSITIES IN SOUTH-EAST, NIGERIA

Abstract

This study investigated lecturers’ digital literacy skills and application of artificial intelligence as predictors of online learning outcomes among undergraduates in federal universities in South-East, Nigeria. Three specific purposes; three research questions, and three hypotheses guided the study. Correlational research design was adopted for the study. The population of the study comprised all the 4,240 teaching staff (2,778 in Nnamdi Azikiwe University, Awka; UNIZIK and 1,462 in Alex Ekwueme Federal University, Ndufu- Alike, Ikwo; AE-FUNAI). The sample size for the study was 424 teaching staff drawn through multi-stage sampling procedure. Structured instruments “Lecturers’ Digital Literacy Skills Scale (PDLSS) developed by the researchers; Artificial Intelligence in Education Questionnaire (AIEQ) developed by Jane Smith in 2022; and Online Learning Outcomes Rating Scale (OLORS) developed by the researchers” were used for data collection. The instruments were face validated by three experts in Faculty of Education; two lecturers in Educational Management and Policy and one lecturer in Measurement and Evaluation Unit, Department of Educational Foundations Nnamdi Azikiwe University, Awka. Internal consistencies co-efficient of 0.86, 0.91, and 0.83 were obtained for PDLSS, AIEQ, and OLORS respectively using Cronbach Alpha statistical method. The researchers administered the instrument to the respondents with the help of six research assistants. Out of the 424 copies of the instruments administered, 407 copies (95.99%) were retrieved duly completed, and used for data analysis. Simple linear regression statistics and multiple linear regressions were used for data analysis. The multiple linear regressions were used to determine the joint predictive value of the independent variables and the dependent variable of the study. The p-value was used to determine the significance of the prediction for all hypotheses. All analyses were carried out using Statistical Package for Social Science (SPSS) Version 25. The study findings revealed that lecturers' digital skills is a very strong and significant predictor of online learning outcomes among undergraduates in federal universities in South-East Nigeria. The findings also revealed that lecturers' digital skills are very strong and significant predictor of online learning outcomes among undergraduates in federal universities in South-East Nigeria. Furthermore, the findings revealed that artificial intelligence is a very strong and significant predictor of online learning outcomes among undergraduates in federal universities in South-East Nigeria with (R = 0.836; predictive value of 0.639 [63.9%]); and F(1/407) = 463.719; & p-value of 0.001). Thus, it was concluded that lecturers' digital skills and the integration of artificial intelligence are critical factors in predicting online learning outcomes among undergraduates in federal universities in South-East, Nigeria. Based on the findings, the study recommended among others that concluded that lecturers' digital skills and the integration of artificial intelligence are critical factors in predicting online learning outcomes among undergraduates in federal universities in South-East, Nigeria. Lecturers should engage in ongoing training and professional development programs focused on enhancing digital skills and the effective integration of artificial intelligence tools in teaching.