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As generative artificial intelligence (AI) becomes a routine part of how we work, academics face more than a choice – there is an emerging moral responsibility to understand AI’s capacities, limitations and implications for fairness. Without that engagement, uneven academic familiarity with AI risks creating unfair advantages and disadvantages among students.

Many students in higher education routinely use AI tools to support time-pressured workloads, accessibility challenges or language needs. Research suggests such tools can democratise support and offer flexibility where it might otherwise be lacking (see Peterson, 2025). In contexts where traditional support might be rigid, scarce or unevenly distributed AI tools provide adaptable, more responsive forms of support. Yet institutional responses and educator preparedness remain inconsistent (Xiao et al., 2023). Where lecturers are AI-aware, students’ AI-assisted work may be scrutinised or regulated; yet where staff lack knowledge or confidence, use of AI may go undetected. The result: two students submitting work of similar quality may receive very different outcomes – not because of intellectual merit, but simply because of who marks their work.

‘Two students submitting work of similar quality may receive very different outcomes – not because of intellectual merit, but simply because of who marks their work.’

This variability undermines basic educational values of fairness and equity. It risks devaluing academic credentials when degree outcomes depend, in part, on the marking staff’s familiarity with AI rather than student learning or effort.

Generative AI tools pose real challenges to core academic principles. For example, a recent systematic review shows that AI’s ability to produce high-quality text threatens academic integrity (Bittle & El-Gayar, 2025) across higher education settings. Studies further demonstrate that even so-called ‘authentic assessments’ (tasks designed to be resistant to AI misuse) are not immune: AI-generated work can pass the scrutiny of experienced staff (see Kofinas et al., 2025).

When teaching staff do not keep pace with AI developments, misuse may remain invisible. This is not only about misconduct, because, over time, reliance on AI-generated work may erode students’ opportunities to develop critical thinking, academic writing and subject mastery. The educational value of assessments could degrade, and qualifications may come to reflect performance on AI-assisted tasks rather than genuine learning.

Moreover, the benefits of AI – including improved accessibility and support for diverse student needs – risk being unevenly realised across institutions and programmes. Students supported by knowledgeable instructors may develop critical AI literacy and benefit academically, while others may be disadvantaged through limited guidance, inconsistent expectations, and heightened risk of punitive integrity processes. For institutions committed to fairness and widening participation, such inequities directly challenge commitments to inclusive and equitable education.

For academics and institutions dedicated to equitable, quality education, engaging with AI is not optional. It is a professional and moral obligation. Key components of that commitment should include:

• ongoing professional development (UNESCO, 2024) to stay informed about AI advances, capabilities, limitations, risks and pedagogical application
• clear, transparent institutional policy and guidance (QAA, 2023) that defines acceptable AI use and prevents inconsistent enforcement
• assessment design that prioritises learning outcomes, develops evaluative judgment (Bearman et al., 2024) and integrates staged observations, oral, reflective or portfolio-based approaches (QAA, 2023) where appropriate
• ethical and inclusive deployment of AI as a support tool rather than a shortcut, with explicit teaching on responsible use (UNESCO, 2023)
• research, evaluation and shared practice across institutions to build evidence-informed approaches (UNESCO, 2024).

Communities of educational researchers, regardless of national context, are uniquely placed to guide responses to AI’s integration in higher education. By combining empirical study, reflective practice and ethical reasoning, researchers can help shape coherent institutional policies, informed pedagogy and fairness-oriented assessment practices. As educators committed to social justice, inclusion and academic integrity, we share a collective responsibility to lead this conversation. Ignoring it amounts to abdication of responsibility; engaging means protecting the value, credibility and equity of higher education.

Engaging with AI is not just a technical or administrative task – it is a moral duty, fundamental to the integrity of education.

References

Bearman, M., Tai, J., Dawson, P., Boud, D. and Ajjawi, R. (2024). Developing evaluative judgement for a time of generative artificial intelligence. Assessment and Evaluation in Higher Education, 49(6), 893–905.

Bittle, K., & El-Gayar, O. (2025). Generative AI and academic integrity in higher education: A systematic review and research agenda. Information, 16(4), 296.

Kofinas, A. K., Tsay, C. H.-H., & Pike, D. (2025). The impact of generative AI on academic integrity of authentic assessments within a higher education context. British Journal of Educational Technology, 56(6), 2522–2549.

Peterson, S. (2025). Addressing student use of generative AI in schools and universities through academic integrity reporting. Frontiers in Education, 10.

United Nations Educational, Scientific and Cultural Organization [UNESCO]. (2023). Guidance for generative AI in education and research.

United Nations Educational, Scientific and Cultural Organization [UNESCO]. (2024). AI competency framework for teachers.

Xiao, P., Chen, Y., & Bao, W. (2023). Waiting, banning, and embracing: An empirical analysis of adapting policies for generative AI in higher education. arXiv.