The model of ethical AI in preparation for teaching (MPET): a practical guide for ethical AI adoption

Abstract

As artificial intelligence (AI) becomes increasingly embedded in teaching preparation, educators need practical ways of translating broad ethical principles into decisions about particular tools and uses. The Model of Ethical AI in Preparation for Teaching (MPET) was developed for this purpose, converting considerations relating to privacy and data protection, fairness and inclusivity, transparency, pedagogical alignment, and learner experience into a structured screening process. This article revises and evaluates MPET following a three-month pilot involving 97 education professionals across six world regions, of whom 63 completed a post-intervention questionnaire. The evaluation comprised three five-point items concerning usability, perceived support for informed ethical decision-making, and timeliness, together with open-ended questions about AI use, concerns, and participant reflections. Descriptive analysis showed that 77.8% of respondents rated MPET positively for ease of use and 77.8% rated it positively for timely ethical evaluation, while 66.7% rated it positively for supporting informed ethical decisions. Qualitative feedback highlighted the need for greater explanatory depth, recognition of contextual variation, and more guidance for complex ethical questions. The findings therefore provide preliminary evidence of perceived usefulness and usability rather than evidence that MPET improves the accuracy, quality, or consistency of ethical decisions. The revised model positions the five core criteria alongside cross-cutting human oversight, teacher agency, accountability, contestability, and sustainability, and introduces jurisdictional and capability-sensitive escalation for cases requiring institutional or specialist review. MPET is consequently presented as a structured entry point into ethical deliberation rather than a mechanism for certifying that an AI tool is ethical.