Generating a Student-Informed Teaching and Learning Conceptual Framework for GenAI in Business Schools: A Case Study

Synthesis: Drummond and Dale (2026) present a case study from a UK business school that introduced generative AI (GenAI) into the first-year undergraduate curriculum through newly implemented AI-focused learning activities. A mixed-methods survey of 149 students found high engagement and recognition of GenAI skills as necessary for future careers, with particular interest in the real-world business applications of AI, ethical considerations, and future technological advances. A significant student concern was using GenAI in academic work without unintentionally committing academic misconduct. Drawing on student feedback and emerging literature, the authors propose a new conceptual framework — extending Ng et al.'s (2021) AI literacy model — for integrating GenAI education throughout business degree programs, offering a scaffolded approach to activities and Assessment across all undergraduate levels. The paper contributes a student-informed model for applied GenAI learning in business education and adds to the literature on AI literacy.

Key Findings

• High student engagement and perceived career relevance: 96% of students recognized the importance of learning about GenAI within their studies, and 71% believed they would continue using it in both academic and future professional life, reflecting the perceived necessity of GenAI skills for employability in higher education and beyond.
• Prior exposure was substantial: 70% of respondents had already used some form of GenAI tool before the module (for idea generation, research, improving academic writing, and generating answers), with 42% describing themselves as familiar or very familiar with AI before instruction.
• Academic misconduct clarity emerged as the top concern: Students identified the provision of information on academic misconduct as the most useful element of the learning, yet only just over half fully understood how to use GenAI in line with university standards — revealing a need for clearer institutional guidelines on acceptable GenAI use.
• Curiosity spans ethics, applications, and the future: Students were keen to learn about business applications of AI, its use in future career paths, ethical use, and the ability to critically assess GenAI outputs; qualitative themes clustered around understanding GenAI, using it for academic success, job security, and business application.
• Desire for practical, career-oriented AI literacy: The framework aligns with Ng et al.'s (2021) AI literacy model — students want to understand, apply, and evaluate AI — while contextualizing content to real-world business problems and employability.
• Constructivism, tutor-facilitated delivery: Learning used a broadly constructivist approach in which students experimented with tools (notably ChatGPT) and assessed output accuracy and reliability, with the tutor providing a supportive environment and face-to-face Feedback rather than relying on AI for immediate feedback.

What this means for practice

• Instructors. Put an assignment-level statement of acceptable use in every GenAI brief. Providing information on academic misconduct was the most useful element of the module for students, yet only just over half reported fully understanding how to use GenAI within university standards.
• Instructors. Do not treat your cohort as a blank slate: 70% of respondents had already used a GenAI tool before the module and 42% called themselves familiar or very familiar with AI, so plan for mixed prior experience rather than assuming novice status.
• Instructors. Anchor activities in real business problems and ethical consequences, since students' strongest interests were business applications of AI, its use in future careers, and ethical use; the Deloitte fabricated-references episode is the kind of industry case the authors use to make the stakes concrete.
• Instructors. Have students use the tool and then interrogate its output — the module had them experiment mainly with ChatGPT, assess the accuracy and reliability of what it produced, and discuss findings face to face — rather than relying on AI for immediate Feedback.
• Instructors. Scaffold GenAI capability deliberately across the program, as the proposed framework does with its Learn → Build → Apply progression, instead of leaving it to one first-year module, and treat student feedback as a formative input to the next iteration of the curriculum.

Limitations

• A single case study: one first-year module in one UK business school, with the conceptual framework derived from that setting and existing literature rather than tested; the authors assert its transferability, but no evaluation in a second institution or program is presented.
• The survey drew 149 responses, a 28% participation rate, from first-year undergraduates — voluntary, self-selected, self-report data, with no comparison group and no measure of learning or attainment.
• The question set was non-validated, piloted with colleagues and amended before dissemination; three open-ended prompts were analyzed by reflexive thematic analysis in NVivo, so the themes come from brief written answers to self-defined questions.
• No student outcome or employability data were collected, so the framework's claimed contribution to AI literacy, employability, and ethical conduct remains a design proposal rather than a demonstrated effect.

Citation

Drummond, M., & Dale, G. (2026). Generating a student-informed teaching and learning conceptual framework for GenAI in business schools: a case study. Journal of University Teaching and Learning Practice, 23(6).