Assessment of Artificial Intelligence (AI) Proficiency and its Demographic Dynamics among Open Distance Learning (ODL) Students in Nigeria
Keywords:
Artificial Intelligence Proficiency; AI Literacy; AI self-efficacy; AI self-competency; Demographic Factors in AI Education; Nigerian ODL Students' AI ProficiencyAbstract
This study assessed AI proficiency vis-à-vis AI literacy, self-efficacy, and self-competency, and its demographic dynamics among ODL students in Nigeria. Social Cognitive Theory served as the conceptual foundation for the study. The study adopted a quantitative approach, and the participants of the study consisted of students chosen from a purposively selected ODL institution (University of Ibadan Distance Learning Centre). The rationale is because they have similar characteristics to other ODL institutions in the country. 301 students selected using a convenience sampling technique participated in the study. A structured questionnaire consisting of demographic information and 29 items adapted to measure AI Proficiency indicators served as the data collection instrument for the study. The items were anchored on a 4 point Likert scale from not at all =1, to a great extent =4. Participation in the study was through an online survey. The data generated from the study were analysed using descriptive statistics (frequency counts, percentages, mean, and standard deviation) and inferential statistics (multiple linear regression analysis, Pearson correlation, T-test, ANOVA, and MANOVA). Results of the study revealed that while the majority of ODL students exhibited high AI literacy, slightly above half of them had low AI self-efficacy. However, most ODL students reported a high level of AI self-competence. Similarly, the study found AI literacy and AI self-efficacy jointly predict ODL students’ AI self-competency. However, AI self-efficacy is the prominent factor. Further, the result revealed that males exhibited significantly higher AI literacy than females. Moreover, the study established that ODL students’ AI proficiency is shaped by intersectional demographic factors. Combined factors (age and programme level; marital status and employment status; and employment status and programme level) influence the AI proficiency of ODL students more than single demographics. It is recommended, amongst others, that ODL institutions and policymakers implement targeted interventions based on the identified factors to prepare ODL students for AI-driven learning in the country.
https://doi.org/10.26803/ijlter.24.6.12
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