Examining teacher and learner perceptions on the ethical challenges of generative AI (gen AI) in transforming teaching and learning practices : a mixed methods study

This thesis examines the perceptions of teachers and learners regarding the ethical challenges posed by Generative Artificial Intelligence (GenAI) in shaping teaching and learning practices within the United Kingdom’s Further Education (FE) sector. Although ethical debates surrounding AI in education increasingly address issues such as algorithmic bias, data governance, academic integrity, and educational equity, these discussions are predominantly framed through a Higher Education (HE) lens. As a result, the ethical implications of GenAI within FE remain underexplored. This study addresses this gap by examining FE as a distinct educational context characterised by diverse learner populations, vocational pathways, and specific institutional constraints. Adopting a mixed-methods research design, the study integrates quantitative data from 314 learner questionnaires with qualitative evidence from three teacher focus groups (FG1: 11 participants; FG2: 5 participants; FG3: 6 participants). The findings indicate that learners generally express cautious optimism regarding the use of GenAI, particularly in relation to learning support. At the same time, teachers articulate more sustained concerns about governance, assessment integrity, and institutional readiness. Together, these perspectives highlight the need for clearer, context-sensitive ethical guidance within the FE sector. The study is grounded in an interdisciplinary literature review informed by the Ethically Aligned Design (EAD, 2017) framework, alongside utilitarian, deontological, and justice-oriented ethical theories. It makes three key contributions. Empirically, it provides a sector-specific account of GenAI ethics in FE by foregrounding the perspectives of teachers and learners that are often underrepresented in HE-dominated discourse (Ataí Bey, Sylwander & Livingstone, 2025). Theoretically, it demonstrates how ethical principles can be operationalised within FE through the Ethical GenAI Integration Framework (EGIF), which translates abstract values into contextually relevant governance strategies. Practically, the study introduces the EGIF as a framework designed to support participatory, scalable, and justice-informed approaches to GenAI adoption within FE. Overall, the findings suggest that compliance-oriented governance approaches are insufficient for addressing the ethical complexities of GenAI in FE contexts, where institutional diversity and structural inequalities shape both risks and opportunities. By situating ethical considerations within the lived experiences of teachers and learners, the study argues that ethical reflection should inform, rather than follow, technological adoption, supporting GenAI integration that is aligned with educational values, equity, and learner dignity.

Grantor dc:publisher

Oxford Brookes University

Author dc:creator

• Kwari, Tendai

Contributors dc:contributor

• Alexander, Patrick
• Davis, Mary

Statement dc:rights

• All rights reserved

Language dc:language

en

DOI dc:identifier

https://doi.org/10.24384/tz1j-qw71

OAI identifier oai:identifier

tle:9ab3c47a-78ae-47cb-b779-ba9e496a4825:d6bd9758-527a-46cd-bfe2-c433766e8fca:1