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All Journal Journal of Education and Learning (EduLearn) Jurnal Multidisiplin Ibrahimy
Faith, Okose
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Religion Humanities Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Exploring Postgraduates’ Adoption of AI-Based Tools in Selected Higher Education Institutions in Delta State Daniel, Reuben; Iteku, Johnson Aborivwighre; Faith, Okose
Jurnal Multidisiplin Ibrahimy Vol. 3 No. 2 (2026): JUMMY - February
Publisher : LP2M Universitas Ibrahimy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jummy.v3i2.8464

Abstract

This study explores Delta State, Nigeria, postgraduates’ knowledge of and engagement with AI-based tools for language learning. This study sought to identify the levels of awareness and different facets of engagement, and the correlation of the two variables amongst the Population of 1000 postgraduates, 870 respondents were selected through stratified random sampling. Participants’ data were obtained through questionnaires, which were later analysed through thematic and descriptive analysis. The results of the study showed a mix of different levels of awareness of tools, with a notable knowledge of, and a lack of awareness of, functionalities like predictive analysis and automated grading. The results of the study indicated that AI-based tools for test preparation, translation, recommendations, and general skill improvement were used in moderate-to-high frequencies. The analysis demonstrated a greater level of awareness of AI-based tools and adoption. The study recommendations include integration of AI-based tools for additional training offered to students, and that curricula designed for Nigerian students be developed. The main conclusion of the study was that awareness is the main influencer. The research offers a postgraduate African context empirical contribution; specifically, the study shows how the components of technology acceptance, awareness, and perceived usefulness are interrelated. Limitations include self-reporting biases and the small geographic scope. Ascertaining the true effect of AI on students' language skills and overall academic achievement lends to future investigations the need for experimental and longitudinal designs.
Improving students’ achievement in chemistry: comparative effectiveness of conceptual modelling and conceptual blending Blessing, Edah Ngozi; Urhievwejire, Agboro-Eravwoke Ochuko; Ehighae, Iyayi Innocent; Oghenewede, Ovuworie Onoriode; Faith, Okose; Obro, Sunday
Journal of Education and Learning (EduLearn) Vol 20, No 2: May 2026
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/edulearn.v20i2.24341

Abstract

The study looked at how conceptual modelling (CM) and conceptual blending (CB) affected students’ achievement in chemistry. This study employed a quasi-experimental design. In this study, 274 seniors secondary (SSII) chemistry students from six coeducational secondary schools in the state were randomly selected using simple random sampling. The information was gathered using the chemistry achievement test (CAT). It was used for pre-test and post-test to evaluate students’ achievement in chemistry. All groups received these tests before and after a six-week course of therapy. The mean/average scores of the 3 groups on the pre-test and post-test were compared using analysis of covariance (ANCOVA) of the CAT data. The study discovered that CM, as opposed to CB, had a significantly bigger effect on students’ achievement in chemistry. Chemistry achievement was significantly higher with CB than with the lecture approach (LA). According to the study, when compared to the LA, CM and CB are more effective teaching strategies for raising students’ achievement in chemistry. Furthermore, it was discovered that CM was the most successful approach. It was suggested, among other things, that chemistry instructors incorporate CM into their chemical lessons.
Co-Authors Blessing, Edah Ngozi Daniel, Reuben Ehighae, Iyayi Innocent Iteku, Johnson Aborivwighre Obro, Sunday Oghenewede, Ovuworie Onoriode Urhievwejire, Agboro-Eravwoke Ochuko