AI evidence extraction

Identifying discourses of generative AI in higher education

Chahna Gonsalves, Oguz A. Acar · Edward Elgar Publishing eBooks · 2025

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

8/10

Relevance

0/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.4337/9781035326020.00012

Methodology & findings

Study design

Semi-structured interviews and discourse analysis of academic staff at a UK Russell Group university's business school

Main result

The study identified eight key thematic discourses through which academic staff perceive generative AI in higher education. The findings reveal that "generative AI is seen as both beneficial and challenging, demanding a shift in pedagogy to enhance critical thinking and problem-solving skills." These themes range from viewing AI as "a double-edged sword" to "a digital tutor," demonstrating the complexity and ambivalence of educator perspectives toward technology integration.

Research paradigm

Interpretive/Hermeneutic - qualitative phenomenological inquiry into educator perceptions and discourse

Author conclusions

The authors conclude that "this research offers insights for responsibly and effectively incorporating AI in academic settings, aiming for a synergy between technological advancements and core educational values." They emphasize "the need for balanced AI integration, ongoing professional development, and ethical guidelines" as essential for responsible AI implementation in higher education.

Risk of bias

Single institution sampling (UK Russell Group university); Limited to business school staff perspective; Small sample size (8 themes from interviews); Potential selection bias in staff participation; Selection bias from single institution (Russell Group university); Limited sample size and geographic scope; Potential social desirability bias in interview responses; Single disciplinary context (business school); Selection bias: Participants limited to single institution (UK Russell Group university business school); Potential respondent bias: Self-selected academic staff may hold particular views about AI; Geographic bias: Single UK institution may not represent broader international or sectoral perspectives; Discipline bias: Business school focus may not generalize to other academic disciplines

Open questions raised

The chapter suggests future research should focus on implementation strategies for balanced AI integration, development of ethical guidelines, and investigation of professional development needs for academic staff.
The research implicitly identifies the need for further investigation into: responsible and effective AI incorporation in academic settings, balanced integration strategies, professional development frameworks for educators, and ethical guidelines for generative AI use in higher education.
The paper implicitly identifies gaps regarding the need for evidence-based guidance on AI integration, professional development frameworks for educators, and ethical frameworks specific to higher education contexts, though these are not explicitly articulated as future research directions in the abstract.

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