Artificial Intelligence - Driven Digital Literacy Programmes for the Enhancement of Adult Learning in Delta State, Nigeria
Abstract This study examined AI-driven digital literacy programmes for the enhancement of adult learning in Delta State. AI-driven digital programmes such as adaptive or personalised learning (EnGen), digital literacy integration, and practical applications are designed to equip the adult learners with the skills and competencies needed to understand, evaluate and use AI technologies effectively. The study aimed at assessing AI-driven digital literacy programmes for the enhancement of adult learning in Central Senatorial District of Delta State. Three (3) research questions and three (3) hypotheses guided the study and tested at 0.05 levels. A descriptive survey research design was adopted. The population of the study were 224 adult learners’ facilitators and the sample size is 140 which were randomly selected using a multi-stage sampling technique. The instrument of data collection is researcher self-structured questionnaire titled AI-Driven Digital Literacy Programmes for Adult Learning Questionnaire (AIDDLPALQ). The instrument was validated using face and content validity, while the reliability of the instrument was ascertained using Pearson Product Moment Correlation Coefficients which yielded a reliability index of 0.70. Data collected were analyzed with mean, standard deviation, independent t-test, and one-sample t-test. Findings revealed that digital tools were accessible to urban and rural learners; and facilitators lacked adequate training and support for AI-driven programmes. Based on findings, it was recommended that government and educational stakeholders should train and re-retain facilitators to enhance their capacity to integrate AI-driven teaching methodologies effectively in the study area.
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-06-01
- DOI
- https://doi.org/10.5281/zenodo.17073121
- Primary Topic
- Geochemistry and Geologic Mapping
- Type
- article
- Field-Weighted Citation Impact
- 0.00