'AI Should Help Them Learn, Not Learn for Them': University Staff Perspectives on the Role of Generative AI in Education

Synthesis: Cross-sectional survey of 76 academic staff at an Australian university (conducted August 2023–June 2024) exploring educators' perspectives, attitudes, and experiences with generative AI in learning and teaching. While staff saw clear productivity gains — streamlined administration, curriculum and Assessment design, and student-centred activities — they voiced substantial concerns about academic integrity, ethical implications, the erosion of core skills such as critical thinking and Creativity, and widening equity gaps in student access and digital proficiency. A recurring refrain — that AI "should help them learn, not learn for them" — points to an urgent need for governance frameworks, structured professional training, interdisciplinary collaboration, and the active inclusion of student perspectives. Published in the Journal of University Teaching and Learning Practice, DOI https://doi.org/10.53761/d5n2yh02. (CC BY-ND license — see log.)

Key Findings

Opportunity without integration. Most staff (61.8%) had used GenAI at work and rated the experience positively, yet few used it frequently or had woven it into teaching and curriculum design (only 40.4% had integrated it on an occasional or regular basis). The dominant perceived benefit was enhanced productivity and efficiency, especially for brainstorming, background research, and content summarisation.
Academic integrity is the central fear. By far the most common concern was students using AI to plagiarise or cheat on assessments, with some staff seeing any attempt to stay on top of it as futile given the speed of technological change.
AI as an impediment to genuine learning. Roughly a third saw GenAI as a threat to learning quality, worried students would use it as a "shortcut" and miss the cognitive work needed to build writing, referencing, critical analysis, and creative skills. Staff flagged inaccuracy and bias (hallucinations) and students' poor AI Literacy as enabling uncritical overreliance — captured in the study's title sentiment that AI should "help them learn, not learn for them."
Workload and labour concerns. Some staff reported that GenAI increased their workload (e.g., marking AI-generated essays) and feared that university adoption would be used to replace human functions such as marking, feedback, and student services rather than genuinely assist them.
A clear skills and support gap. While most staff wanted training and supported embedding AI Literacy into the curriculum, over three-quarters said adequate resources were not provided, and the majority reported no clear institutional guidelines on ethical use. Only one respondent rated their university as above average for AI utilisation compared to peers.
"A tool, not a solution." Across open-ended responses, staff broadly agreed that GenAI should support rather than replace human educators, preserving teacher–student relationships, disciplinary expertise, and human oversight, while managing routine administrative "shovel work."

Study Design & Method

The study used an exploratory cross-sectional design with an online survey (hosted on RedCap) built on Kelly et al.'s (2023) Generative Artificial Intelligence Survey, trialled with colleagues before launch. Academic staff from ten schools at an Australian university were recruited through Associate Deans of Learning and Teaching via email, Teams, and campus posters. The instrument contained up to 55 items across six sections — demographics, digital and AI literacy, training and university adoption, prompt engineering, Assessment development, and academic integrity, ethics, and equity — with branching logic, closed (multiple-choice and Likert) and open-ended items. After listwise deletion, 76 of 94 initial responses were analysed. Descriptive statistics were computed in SPSS 28; because of the small sample and low cell counts, no inferential statistics were run. Open-ended responses were analysed using an inductive thematic approach (Braun & Clarke), with codes grouped into broad themes and saturation judged when repeated review yielded no new codes.

Implications for AI in Education

Governance and policy before scale. Institutions need clear, usable frameworks and transparent policies on permitted AI use for both students and staff; the absence of such guidance was a recurring barrier reported by respondents.
Professional development is non-negotiable. Targeted training must pair technical skill with pedagogical strategy — most staff wanted it, most lacked it, and few felt confident teaching students skills such as prompting, pointing to a significant skills gap.
Redesign assessment around integrity. Rather than detection-only approaches, staff called for Assessment redesign that emphasises higher-order thinking and developing students' evaluative judgement, protecting the human, dialogic role of Feedback.
Equity must be deliberate. Given socioeconomically diverse cohorts, ensuring equitable access to GenAI tools and structured student training was regarded as essential to avoid widening existing digital and educational disparities.
Human oversight endures. The dominant view that AI is "a tool, not a solution" reinforces the need for human-in-the-loop design and the continued centrality of educators, teacher–student relationships, and human judgment in Higher Ed.

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Citation

Enright, H., Horvath, D., Petrovic, K., & Šarkić, B. (2026). "AI should help them learn, not learn for them": University staff perspectives on the role of Generative AI in education. Journal of University Teaching and Learning Practice, 23(7). https://doi.org/10.53761/d5n2yh02 (CC BY-ND).