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 optimizing 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 emphasized 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 emphasized 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 specialized, evolving workplace tasks (e.g., marketing content, HR tasks like resume analysis and interview scripts).

• Priority area 1: Understanding and optimizing 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.

What this means for practice

• Instructors. Build GenAI literacy into assessment around the three areas students themselves prioritized: how the tools work and how to prompt them, how they apply across academic and industry contexts, and their ethical and legal dimensions such as copyright and disclosure.
• Instructors. Bridge students' informal personal use into formal academic and professional practice by Scaffolding prompting and critical evaluation rather than assuming competence from familiarity.
• Designers. Co-design the curriculum with students: 84% wanted GenAI taught in their units and 85% saw it as essential for employability, yet only 7% had learned about it from their university.
• Administrators. Support discipline-specific integration guided by educational values rather than market or technological pressure, and close the gap between institutional offerings and the informal GenAI use students already bring.

Limitations

• A cross-sectional survey of 166 of 222 students in a single final-year business capstone unit at one regional Australian university, capturing perceptions at one point in time.
• Only student perspectives were collected, with no educators or industry professionals, so alignment between curricular interventions and workplace expectations could not be evaluated.
• The authors acknowledge that the framing of the survey questions may have reflected a positive orientation toward GenAI and influenced responses.
• Coding was inductive reflexive thematic analysis of open-ended responses, so the three priority areas are interpretive rather than measured against an independent criterion.

Citation

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