From leadership research to district action: Ethical compliance and institutional policy support for artificial intelligence integration in African TVET Education: A structural equation modeling approach

AI-assisted editorial implementation guide. Proposed district actions are separate from the original authors’ findings and recommendations. Independent peer review of this guide is not asserted.

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Evidence and transfer

Education-related source: verify which participants, institutions and outcomes were actually studied before district adoption. Source publication type: research-article. License and identity checks do not establish study quality or causality.

Original authors’ abstract, reproduced under the source license:

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.

Study methods

In this study, we employed a quantitative, cross-sectional survey design to investigate the relationships among Ethical Principles Guiding AI Adoption (EPG), Compliance with AI Ethical Guidelines (CAEG), and Perceived AI Adoption Outcomes in TVET (PAAT), while examining Institutional Policy Support (IPS) as a moderating variable. This design enabled us to collect standardized data from a diverse sample of educators and analyze both direct and indirect effects among the study constructs. The research was conducted in Nigeria, where the integration of AI into education is gaining momentum but is often impeded by inadequate institutional frameworks and gaps in ethical policies. From the Research Ethics Committee of the universities of Nigeria, written permission was obtained to conduct the study (2023/2024 academic session with ref no of UNN/FE/EC/2024/011). We focused on public TVET institutions, including universities and vocational training centers, due to their central role in equipping students with skills for the AI-driven workforce. Using a purposive sampling technique, we collected 400 valid responses from educators, with 57.8% identifying as male and 63.7% holding doctoral qualifications. Notably, 78% of respondents reported that their institutions lacked formal AI policy support, further justifying the need to examine how ethics and institutional mechanisms influence AI adoption behavior in the TVET sector.

Limitations

This study provides valuable contributions, it is not without limitations. First, the data were collected using self-reported measures, which are subject to social desirability bias. TVET educators may have over-reported their ethical compliance or AI readiness, particularly given the growing awareness around responsible AI use. In future research, observational or behavioral data, such as audit logs of actual AI tool usage, could provide more objective insights into compliance behavior. Second, although our sample was diverse, it was limited to TVET institutions within a specific national context. The findings may not fully generalize to other educational systems or cultural settings, particularly those with more developed AI infrastructures or different ethical policy mandates. Future studies should consider cross-country comparisons to explore how cultural, regulatory, and infrastructural variations influence the dynamics of AI ethics and adoption in education. Third, while we tested the moderating role of institutional policy support, we relied on a binary categorization (support available vs. not available). This may not fully capture the richness and variability of institutional AI policy environments. Future research could utilize a multi-dimensional scale to measure the quality, clarity, and enforcement strength of institutional support. Lastly, our study focused exclusively on educators. Yet, AI integration in education is a multi-stakeholder process involving administrators, students, and policy actors. Future research should expand the scope to include these groups, offering a more holistic understanding of the ethical ecosystem surrounding AI adoption in TVET.

A 90-day district inquiry

Days 1–15 · Appraise. Read the source methods, recruitment, comparator, outcomes and limitations. Record how the study setting differs from your district and which claim the design can support.
Days 16–30 · Design. Select one recurring district decision. Publish the decision owner, evidence requirements, consultation process and appeal route; test the process on a bounded low-risk decision. Agree participation, privacy protections, baseline and stopping rules before starting.
Days 31–60 · Observe. Collect the pre-agreed measures, keep a log of implementation changes and invite confidential voluntary feedback. Preserve existing support for staff and learners.
Days 61–90 · Decide. Track decision time, unresolved objections, documented reasons, appeals and access differences. Review whether the process changes who can participate and who carries the burden. Make a dated decision to stop, revise or extend the inquiry.

Board decision brief

Present the local problem, source setting, evidence limits, intervention, responsible owner, total cost, measured changes and uncertainty. Separate staff perceptions, implementation measures and student learning outcomes.

Attribution and reuse

Musa Adekunle Ayanwale; Christian Basil Omeh; Folasade Mardiyya Oyeniran; Catherine Chiugo Kanu. Ethical compliance and institutional policy support for artificial intelligence integration in African TVET Education: A structural equation modeling approach. PLOS One (2025-12-19). https://doi.org/10.1371/journal.pone.0335747

Prepared 2026-11-07. Source authorship and DOI are preserved. The abstract and source passages are attributed extracts; this editorial guide is offered under the same CC BY 4.0 license.