Integrating Generative Artificial Intelligence into University Curricula: Student Insights

Synthesis: Rook, L., & Plumb, M. (2026), Journal of University Teaching and Learning Practice 23(2). A qualitative survey of 166 undergraduate business students in a final-year capstone unit at a regional Australian university, analyzed using Braun and Clarke's Reflexive Thematic Analysis. The study foregrounds student voice on the role of generative AI in academic learning and career readiness, finding strong support for integrating GenAI knowledge and application into higher education curricula. Students identified three priority areas for development: (1) understanding and optimising GenAI functionality, (2) exploring GenAI applications across contexts, and (3) navigating ethical and legal dimensions of GenAI. The authors argue that involving students in co-designing future-ready curricula advances AI literacy, digital capability, and employability.

Key Findings

Students strongly support curricular GenAI integration. 84% of respondents emphasised the value of understanding how to use GenAI tools effectively in university units, viewing such engagement as essential for contemporary learning — "university is intended to output students with the most up to date skills and knowledge applicable to their field" (P97). Students framed GenAI as a driver of career transformation, comparing it to the advent of computers or the internet and arguing that failing to learn it risks falling behind.

Students overwhelmingly see GenAI literacy as essential for employability. 85% of respondents emphasised its importance for future careers, with two themes emerging: GenAI as a catalyst for productivity and efficiency in professional contexts (automation, streamlined workflows) and as an enabler of specialised, evolving workplace tasks (e.g., marketing content, HR tasks like resume analysis and interview scripts).

Priority area 1: Understanding and optimising GenAI functionality. Students wanted a deeper grasp of how GenAI systems work, effective prompting ("how to prompt AI … to get a better response," P53), the technical foundations of generative AI (architectures, algorithms, training data), and how to evaluate the accuracy and "truthfulness" of GenAI outputs.

Priority area 2: Exploring GenAI applications across contexts. Students sought knowledge of applications spanning academic settings and real-world industries (healthcare, finance, marketing), the range of available tools beyond ChatGPT, and integration into workplace software, toolkits, and workflow systems.

Priority area 3: Navigating ethical and legal dimensions of GenAI. Students explicitly requested learning about the ethical and legal implications of GenAI, including "the ethical frameworks and guidelines governing the development and use of AI technologies" (P164), workplace regulations, and how to "ethically use them to avoid copyright/incorrect claims" (P134).

Students value co-design and scaffolded, discipline-specific guidance. Practitioner notes stress scaffolding experiences that build prompting, critical evaluation, and understanding of how GenAI systems function, embedding ethical and legal considerations for responsible engagement, and bridging students' informal, personal use of GenAI to its formal academic and professional applications.

Study Design & Method

Sample & context. Anonymous qualitative survey (Qualtrics) administered in 2024 to a final-year undergraduate capstone unit (222 enrolled) in the business school of a regional Australian university, covering HRM, finance, marketing, international business, economics, management, public relations, law, and accounting. Open-ended responses from 166 students (of 222) were analyzed; the university had not yet implemented specific GenAI teaching policies.

Purposive sampling & ethics. Purposive sampling was used to select participants able to illuminate GenAI perceptions; ethics approval from University of Wollongong (Approval No. 2023/286). To preserve anonymity, the first author (also unit coordinator) had no access to identifiable data; the second author de-identified all responses prior to analysis.

Three research questions. RQ1 (value of using GenAI tools in university studies), RQ2 (value of GenAI literacy for future careers), and RQ3 (further GenAI knowledge/skills students want) — each matched to an open-ended survey item.

Analysis. Braun and Clarke's Reflexive Thematic Analysis (RTA), inductive and involving both semantic and latent meanings. NVivo supported coding, but decisions were interpretive and reflexive; authors independently coded then negotiated shared understanding, reviewed themes against the full dataset, and maintained researcher reflexivity throughout.

Implications for AI in Education

This study is a distinctive empirical anchor showing that AI literacy in a professional-discipline context is best built through student-centred curriculum design that privileges learner voice. By foregrounding business students' priorities, it challenges educator-led and policy-driven accounts that often exclude those most directly affected by GenAI integration, and it argues for curricula guided by educational values rather than market or technological imperatives. The finding that students already use GenAI informally (only 7% learned about it from their university, per cited cross-institutional work) underscores a disconnect between institutional offerings and student needs. For higher education more broadly, the study's three priority areas — functionality, cross-context application, and ethical/legal navigation — offer a practical, student-validated scaffold for embedding GenAI literacy, while its emphasis on co-design connects to wider calls for engaged, scaffolded, and discipline-specific integration that balances technical fluency with critical and ethical reasoning. Notably, the study is strongly relevant to a future business education concept page in this wiki (not yet created; left as plain text).

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Citation

Rook, L., & Plumb, M. (2026). Integrating generative artificial intelligence into university curricula: Student insights. Journal of University Teaching and Learning Practice, 23(2). https://doi.org/10.53761/jjzzd330