The ABC levels of GenAI integration: understanding uneven outcomes in higher education

25 June 2026

Existing research into GenAI integration in higher education remains inconclusive. While some studies report benefits such as stronger engagement, faster feedback, and support for skill development, others raise concerns about overreliance, weak critical judgement, bias, and academic integrity. We suggest that this inconsistency is not accidental. It reflects an important underlying issue: GenAI is not being integrated following the similar approaches across modules and programmes. 

Drawing on research in business education, conducted as part of The President and Principal’s Fund for Educational Excellence in collaboration with students, we suggest that different curriculum integration patterns help explain why outcomes appear so varied. On this basis, we identified three distinct levels of GenAI curriculum integration, describing different ways in which AI can be embedded in teaching and learning. These are captured in the ABC framework: ad hoc, blended, and constructive. 

At the ad hoc level, GenAI is used occasionally in isolated tasks. It often appears as an optional or experimental tool, either introduced by the lecturer or adopted by students independently. While this can support exploration, it remains disconnected from intended learning outcomes and assessment. As a result, its impact is limited and uneven. 

At the blended level, GenAI is incorporated into selected teaching activities. It supports specific parts of the learning process, for example, idea generation, feedback, or practice tasks, but is not consistently aligned across the module. Here, GenAI begins to enhance learning, yet its role remains partial. Students may engage with it, but they are not necessarily required to critically evaluate or justify its use. 

At the constructive level, GenAI becomes an integral component of curriculum design. It is explicitly aligned with intended learning outcomes, teaching activities, and assessment. Students are not only using AI tools, but are expected to evaluate outputs, reflect on processes, and justify decisions. At this level, GenAI supports the development of disciplinary knowledge alongside higher-order skills such as judgement, critical evaluation, and ethical awareness. 

Across the cases analysed as part of the project, GenAI integration generated both benefits and challenges for students, educators, and institutions. However, these were not distributed evenly across the three patterns of integration. This is important because it suggests that differences in AI-related outcomes are not simply a matter of whether GenAI is used, but how it is integrated into the curriculum. Constructively integrated cases were linked more clearly to student engagement, capability development, employability, and curriculum relevance because GenAI was embedded through aligned learning outcomes, teaching activities, and assessment. Importantly, these cases also showed stronger educator development, including greater pedagogical reflection and confidence, despite ongoing workload pressures. Together, these findings suggest that constructive integration strengthens both student outcomes and educator learning by locating GenAI use within coherent curriculum design. 

In practice, this can be achieved without complete curriculum redesign, by integrating GenAI into existing curriculum strategies. In a marketing module, for example, students worked on live business projects using AI tools to generate and refine strategy. The assessment required them to justify their decisions using both AI outputs and marketing theory, making their reasoning visible. In a strategic management simulation, students used AI to analyse data and compare alternative decisions over time, with evaluation embedded in the task. In a business analytics context, students compared outputs from Excel and GenAI, critically reflecting on differences, limitations, and bias.  

For educators, the implication is straightforward but significant. Rather than asking where GenAI can be added, it is more productive to ask what role it should play in achieving learning outcomes. Constructive integration does not require more technology, but more intentional curriculum design.

The full paper is available here: From Experimentation to Integration: Embedding GenAI in Business Higher Education through the Lens of Constructive Alignment 

An associated blog can be found here: From ad hoc to constructive: the ABC levels of GenAI integration in business education 

Dr Qianqian Chai

Lecturer in Business Management

https://www.qmul.ac.uk/arts/people/academic-and-research-staff/language-centre/chai.html