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

◐ medium · 2026-08-20 · generative-ai, business-education, higher-ed, curriculum-design, teacher-role, academic-integrity, student-engagement

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 programmes, 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 recognised 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 contextualising content to real-world business problems and employability.
  • Constructivist, 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.

Implications for AI in Education

  • Students are demand-side drivers of GenAI integration: Rather than resisting AI, business students actively seek GenAI skills for employment, signalling that curricula should integrate GenAI education into core programmes to meet student demand and prepare learners for AI-enabled workplaces.
  • Ethical and misconduct guidance must be explicit and scaffolded: The gap between students' engagement with GenAI and their uncertainty about acceptable academic conduct points to the need for clear institutional policies, academic integrity guidance, and assignment-level briefs that define acceptable use.
  • Educators need capability alongside policy: Effective integration is hampered by limited GenAI capability among educators, so teacher development and clear faculty guidance are prerequisites for consistent, well-designed GenAI learning across programmes.
  • Ethics and business application should anchor activities: Learning design should foreground real-world business applications of AI and its ethical, social, and equity dimensions, echoing the Deloitte fabricated-references incident as a cautionary example of industry malpractice.
  • A scaffolded, level-aligned framework can guide delivery: The proposed framework maps GenAI content and activities to each year of undergraduate study (Learn → Build → Apply), providing a structured pathway for developing AI literacy and employability skills throughout a degree.
  • Student feedback is a formative design resource: Using student input to construct theory and refine teaching reflects a formative approach that can keep GenAI education responsive to evolving needs as the technology advances.

Connected Concepts

Connected Articles

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).