Large language models and GenAI in education: Insights from Nigerian in-service teachers through a hybrid ANN-PLS-SEM approach DOI Creative Commons
Musa Adekunle Ayanwale, Owolabi Paul Adelana, Nurudeen Babatunde Bamiro

et al.

F1000Research, Journal Year: 2025, Volume and Issue: 14, P. 258 - 258

Published: March 4, 2025

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, administrative efficiency. However, integrating tools into resource-constrained settings such as Nigeria presents significant challenges, including inadequate infrastructure, digital inequities, teacher readiness. Despite the growing research on AI adoption, limited studies focus developing regions, leaving a critical gap understanding how educators perceive adopt technologies. Methods We adopted hybrid approach, combining Partial Least Squares Structural Equation Modelling (PLS-SEM) Neural Networks (ANN) uncover both linear nonlinear dynamics influencing behavioral intention (BI) 260 Nigerian in-service teachers regarding after participating structured training. Key predictors examined include Perceived Ease Use (PEU), Usefulness (PUC), Attitude Towards (ATC), Your Colleagues (YCC), Technology Anxiety (TA), Teachers’ Trust (TTC), Privacy Issues (PIU). Results Our PLS-SEM results highlight PUC, TA, YCC, PEU, that order importance, predictors, explaining 15.8% variance BI. Complementing these, ANN analysis identified ATC, PUC most factors, demonstrating substantial predictive accuracy with an RMSE 0.87. This suggests while drives PEU positive attitudes are foundational fostering engagement Conclusion need targeted professional development initiatives teachers’ competencies, reduce technology-related anxiety, build trust ChatGPT. study actionable insights policymakers educational stakeholders, emphasizing importance inclusive ethical ecosystem. aim empower support AI-driven transformation resource-limited environments by addressing contextual barriers.

Language: Английский

Students' behavioural intention to use content generative AI for learning and research: A UTAUT theoretical perspective DOI Creative Commons
Mohammed Nasiru Yakubu,

N. Gnanamalar David,

Naima Hafiz Abubakar

et al.

Education and Information Technologies, Journal Year: 2025, Volume and Issue: unknown

Published: March 22, 2025

Language: Английский

Citations

0
A cross-country analysis of self-determination and continuance use intention of AI tools in business education: Does instructor support matter? DOI Creative Commons
Egena Ode, Rabake Nana,

Irene O. Boro

et al.

Computers and Education Artificial Intelligence, Journal Year: 2025, Volume and Issue: unknown, P. 100402 - 100402

Published: April 1, 2025

Language: Английский

Citations

0
Generative artificial intelligence in pedagogical practices: a systematic review of empirical studies (2022–2024) DOI Creative Commons
Wang Xiaoyu, Zamzami Zainuddin,

Chin Hai Leng

et al.

Cogent Education, Journal Year: 2025, Volume and Issue: 12(1)

Published: April 7, 2025

Language: Английский

Citations

0
Leapfrogging Effect Hypothesis: Generative Ai as a Permanent Scaffold in Higher Education DOI
Alexander M. Sidorkin

Published: Jan. 1, 2025

Language: Английский

Citations

0
Large language models and GenAI in education: Insights from Nigerian in-service teachers through a hybrid ANN-PLS-SEM approach DOI Creative Commons
Musa Adekunle Ayanwale, Owolabi Paul Adelana, Nurudeen Babatunde Bamiro

et al.

F1000Research, Journal Year: 2025, Volume and Issue: 14, P. 258 - 258

Published: March 4, 2025

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, administrative efficiency. However, integrating tools into resource-constrained settings such as Nigeria presents significant challenges, including inadequate infrastructure, digital inequities, teacher readiness. Despite the growing research on AI adoption, limited studies focus developing regions, leaving a critical gap understanding how educators perceive adopt technologies. Methods We adopted hybrid approach, combining Partial Least Squares Structural Equation Modelling (PLS-SEM) Neural Networks (ANN) uncover both linear nonlinear dynamics influencing behavioral intention (BI) 260 Nigerian in-service teachers regarding after participating structured training. Key predictors examined include Perceived Ease Use (PEU), Usefulness (PUC), Attitude Towards (ATC), Your Colleagues (YCC), Technology Anxiety (TA), Teachers’ Trust (TTC), Privacy Issues (PIU). Results Our PLS-SEM results highlight PUC, TA, YCC, PEU, that order importance, predictors, explaining 15.8% variance BI. Complementing these, ANN analysis identified ATC, PUC most factors, demonstrating substantial predictive accuracy with an RMSE 0.87. This suggests while drives PEU positive attitudes are foundational fostering engagement Conclusion need targeted professional development initiatives teachers’ competencies, reduce technology-related anxiety, build trust ChatGPT. study actionable insights policymakers educational stakeholders, emphasizing importance inclusive ethical ecosystem. aim empower support AI-driven transformation resource-limited environments by addressing contextual barriers.

Language: Английский

Citations

0