ArticleF1000Research2025
Large language models and GenAI in education: Insights from Nigerian in-service teachers through a hybrid ANN-PLS-SEM approach.
Article in F1000Research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Quantifying teachers' readiness for artificial intelligence adoption in education: a mathematical modeling perspective.Scientific reports · 2025Article
- Large language models and GenAI in education: Insights from Nigerian in-service teachers through a hybrid ANN-PLS-SEM approach.F1000Research · 2025Article
- Promoting teaching innovation among university teachers through AI literacy from the perspective of planned behavior: the moderating effects of three perceived supports.Frontiers in psychology · 2025Article
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Authors and funding
6 authors.
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Abstract
Background: The rapid integration of Artificial Intelligence (AI) in education offers transformative opportunities to enhance teaching and learning. Among these innovations, Large Language Models (LLMs) like ChatGPT hold immense potential for instructional design, personalized learning, and administrative efficiency. However, integrating these tools into resource-constrained settings such as Nigeria presents significant challenges, including inadequate infrastructure, digital inequities, and teacher readiness. Despite the growing research on AI adoption, limited studies focus on developing regions, leaving a critical gap in understanding how educators perceive and adopt these technologies. Methods: We adopted a hybrid approach, combining Partial Least Squares Structural Equation Modelling (PLS-SEM) and Artificial Neural Networks (ANN) to uncover both linear and nonlinear dynamics influencing behavioral intention (BI) of 260 Nigerian in-service teachers regarding ChatGPT after participating in structured training. Key predictors examined include Perceived Ease of Use (PEU), Perceived Usefulness (PUC), Attitude Towards ChatGPT (ATC), Your Colleagues and Your Use of ChatGPT (YCC), Technology Anxiety (TA), Teachers' Trust in ChatGPT (TTC), and Privacy Issues (PIU). Results: Our PLS-SEM results highlight PUC, TA, YCC, and PEU, in that order of importance, as significant predictors, explaining 15.8% of the variance in BI. Complementing these, ANN analysis identified PEU, ATC, and PUC as the most critical factors, demonstrating substantial predictive accuracy with an RMSE of 0.87. This suggests that while PUC drives adoption, PEU and positive attitudes are foundational in fostering teacher engagement with AI technologies. Conclusion: Our results highlight the need for targeted professional development initiatives to enhance teachers' digital competencies, reduce technology-related anxiety, and build trust in AI tools like ChatGPT. Our study offers actionable insights for policymakers and educational stakeholders, emphasizing the importance of fostering an inclusive and ethical AI ecosystem. We aim to empower teachers and support AI-driven educational transformation in resource-limited environments by addressing contextual barriers.
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