Published in PLOS One on 2025-12-19, this study is presented as an attributed evidence briefing. The excerpts below are the original researchers’ words, reproduced under their Creative Commons Attribution licence. Health Signal has selected the abstract and, where needed, opening background paragraphs; the complete methods, results, limitations and references remain in the linked paper.
Original authors: Musa Adekunle Ayanwale, Christian Basil Omeh, Folasade Mardiyya Oyeniran, Catherine Chiugo Kanu
Source: PLOS One • DOI 10.1371/journal.pone.0335747
Read the complete primary study ↗ · Creative Commons Attribution licence
From the researchers
Abstract
As artificial intelligence (AI) reshapes educational landscapes, ensuring ethical alignment and institutional responsiveness is essential particularly in skill-intensive sectors such as Technical and Vocational Education and Training (TVET). In this study, we examined the predictive roles of Ethical Principles Guiding AI adoption (EPG), Compliance with AI Ethical Guidelines (CAEG), and Perceived AI Adoption Outcomes (PAAT), while testing the moderating effect of Institutional Policy Support (IPS). Grounded in Rest’s Four Component Model of ethical behavior, we adopted a cross-sectional, quantitative design and surveyed 400 TVET educators across Nigeria using a rigorously validated instrument. Our analysis through Partial Least Squares Structural Equation Modeling (PLS-SEM) revealed that Ethical Principles Guiding AI adoption significantly influenced both Compliance with AI Ethical Guidelines and Perceived AI Adoption Outcomes. Moreover, CAEG partially mediated the relationship between EPG and PAAT, highlighting compliance as a vital conduit for ethical adoption. However, IPS did not significantly moderate the EPG–CAEG relationship, indicating that personal ethical orientation may remain influential even in the absence of institutional frameworks. Our model demonstrated strong explanatory and predictive validity, particularly in institutions without formal AI policies underscoring the compensatory role of ethical leadership. We contribute novel empirical insights to the emerging scholarship on AI ethics in education by integrating ethical compliance and policy dynamics into a unified framework contextualized within African TVET systems. Our findings emphasize the importance of empowering educators with ethical competencies and call for urgent institutional action in policy formulation, infrastructure investment, and professional development to support responsible AI integration in education.
1. Introduction
Artificial Intelligence (AI) is transforming all sectors of the global economy, including education, with far-reaching implications for how teaching, learning, and workforce development are conceptualized. Within this landscape, the Technical and Vocational Education and Training (TVET) sector is particularly significant, as it is strategically positioned at the forefront of this technological evolution. As AI continues to reshape industries such as banking, transportation, healthcare, and manufacturing, its integration into education, especially TVET, is both timely and inevitable [1,2,3]. These scholars contend that educational institutions should equip students with the requisite competencies to function effectively in a knowledge-driven, innovation-oriented global economy. TVET educators play a pivotal role in this transition. They are tasked with preparing students not only through theoretical instruction but also through the practical application of skills essential for success in AI-mediated work environments. However, the rapid diffusion of AI tools, including generative systems like Copilot, ChatGPT, and Bard, into educational settings has raised pressing concerns regarding potential misuse and regulatory shortcomings [4,5]. In many TVET institutions, the absence or inadequacy of formal policies and ethical guidelines leaves educators without clear direction, heightening the risk of irresponsible or uninformed AI adoption. Ethical principles such as transparency, fairness, and accountability must be foundational to AI integration, ensuring that its use fosters empowerment rather than exclusion [6].
Reading this evidence
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© 2025 Musa Adekunle Ayanwale, Christian Basil Omeh, Folasade Mardiyya Oyeniran, Catherine Chiugo Kanu. Excerpts reproduced under CC BY. Selection, formatting and contextual notes by Health Signal. The researchers are the authors of the source study, not members of Health Signal’s staff.
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