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Ethical Generative AI Integration in English for Academic Purposes within Higher Education: A Mixed-Methods Study

◐ medium · 2026-08-20 · generative-ai, language-learning, ethics, higher-ed, academic-integrity, teacher-role

Synthesis: Alharbi et al. (2026) respond to the fact that roughly 60% of higher education institutions report faculty using Generative AI tools in teaching while fewer than 15% have implemented formal ethical guidelines. Using a convergent mixed-methods design guided by Mezirow's Transformative Learning Theory, the authors designed, implemented, and evaluated an eight-week GenAI-integrated professional development program at a multicultural Global South institution, engaging 97 higher education educators in quantitative testing and 15 in qualitative interviews. Quantitative results showed significant gains in ethical awareness (d = 0.93) and digital andragogical competence with high inter-rater reliability; qualitative findings confirmed persistent dilemmas such as plagiarism and student overreliance on AI, and surfaced an unexpected barrier — reconciling institutional policy gaps with personal ethical values. The study addresses gaps in Global South evidence and field-tested professional development models, offering recommendations on AI policy literacy, peer-led communities of practice, and shared AI resources.

Key Findings

  • Large gains in ethical awareness and reasoning: ethical awareness scores (Scenario Rubric) rose from a pre-test mean of 3.21 to 4.05 post-program, t(96) = 15.12, p < .001, with a large effect size (Cohen's d = 0.93); Artefact Rubric scores rose from 2.94/5 to 4.21/5 with high inter-rater reliability (κ = 0.87).
  • Plagiarism and student overreliance persist as core dilemmas: educators cited difficulty navigating the absence of institutional Generative AI policies (15 mentions) and uncertainty over acceptable AI use (12 mentions) as primary challenges, confirming that Academic Integrity concerns shift but do not disappear.
  • An unexpected barrier emerged — reconciling policy gaps with personal values: beyond technical dilemmas, participants struggled to align institutional policy gaps with their own ethical values, a finding that complicates simple compliance-based approaches to Ethics.
  • Policy familiarity jumped dramatically: affirmative responses on knowledge of institutional AI policy rose from 27% to 79% (p < .001, McNemar's test), and higher policy awareness was associated with stronger Alignment with Policy scores in artefacts (r = .36, p < .05).
  • Engagement amplifies gains: higher engagement (an index of attendance, task completion, and reflective participation) correlated with higher post-test scores and rubric performance (r = .41, p < .01 for plagiarism/authorship scenarios), and highly engaged participants positioned themselves as "change agents" in their departments.
  • Educators proposed institutional strategies: participants recommended establishing AI ethics committees (12 mentions), embedding AI policy literacy into professional development (11 mentions), and creating centralized repositories of vetted Generative AI tools (10 mentions), plus ongoing rather than one-off training.

Study Design & Method

This is an empirical mixed-methods study using a convergent design within a pragmatist paradigm, integrating quantitative evidence of change with qualitative accounts of experience. The quantitative strand used stratified random sampling to recruit 97 higher education educators in an English for Academic Purposes (EAP) unit at a multicultural Global South institution (staff from the Gulf, South Asia, Central Asia, and Africa); the qualitative strand used purposeful maximum variation sampling to select 15 participants for semi-structured interviews. Original instruments included an adapted 24-item Likert survey (Cronbach's α = 0.81–0.87), a Scenario Rubric, an Artefact Rubric (inter-rater reliability κ = 0.85), and an engagement index. The eight-week professional development program was explicitly aligned with Mezirow's Transformative Learning Theory, sequenced from disorienting dilemmas (weeks 1–2) through reflective discourse (3–4), communicative learning and peer review (5–6), to perspective transformation and reintegration (7–8). Qualitative data were analyzed via Braun and Clarke's (2006) six-phase thematic analysis (intercoder κ ≈ 0.85), with integration occurring during the interpretation phase through triangulation. Rigour was ensured via member checking, thick description, audit trails, and reflexive journaling; the single-institutional setting limits statistical generalizability but supports analytical generalizability to comparable Global South contexts.

Implications for AI in Education

The study positions educators as ethical decision-makers rather than passive adopters of emerging technology, arguing that effective professional development must pair technical Generative AI skills with structured ethical reasoning. For higher education, the finding that policy familiarity and engagement drive both competence and institutional literacy implies that institutions should embed AI policy literacy across all training, establish ethics committees, and build centralized resources rather than relying on one-off compliance sessions. For English for Academic Purposes and other multilingual contexts, the work underscores the need for context-sensitive frameworks that address the ethical tensions of authorship, transparency, and equity amid policy ambiguity — and that surface the value-conflict between institutional rules and personal ethical values. The adaptable eight-week model offers a field-tested template for professional development that balances Academic Integrity with innovation, and its attention to Global South contexts helps counter the "epistemic parochialism" common in Generative AI research.

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Citation

Alharbi, W., Hamid, S., Abbas, S., & Salieva, Z. (2026). Ethical Generative AI integration in English for Academic Purposes within higher education: A mixed-methods study. Journal of University Teaching and Learning Practice, 23(5). (CC BY-ND 4.0.)