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From ad hoc to constructive: the ABC levels of GenAI integration in business education

by Qianqian Chai and Xue Zhou

Introduction – the challenge of GenAI integration in business education

Since the release of ChatGPT, Generative Artificial Intelligence (GenAI) has rapidly entered higher education. Business schools, with their strong ties to industry and emphasis on applied skills, provide a particularly important setting for examining GenAI’s role in curriculum design. Yet, while adoption has expanded quickly, the educational outcomes of GenAI integration have not been consistent (Kurtz et al, 2024). Across cases, educators identified both benefits and risks, including engagement, skills development, and overreliance. This unevenness suggests that rather than reflecting a single trajectory of adoption, early practice appears to involve different approaches to integration. The central issue is not whether GenAI is used, but how different approaches shape outcomes.

This blog draws on our recent study of GenAI integration in business modules at a UK Russell Group university (Zhou et al, 2026). Through a qualitative analysis of 17 educator cases across 24 modules, we examined how GenAI was incorporated into curriculum design, and how different approaches were associated with distinct benefits and challenges. Using the lens of constructive alignment (Biggs, 1996), we identified three patterns of integration: ad hoc, blended, and constructive, which together form the ABC levels for understanding how GenAI is integrated into curriculum practice. We use these levels to explore why some approaches appear more educationally effective than others. In particular, this blog will offer research-informed insights into how GenAI can be integrated more effectively and sustainably in business higher education. While the cases are drawn from business education, the patterns identified and principles of constructive integration have wider relevance across disciplines where GenAI is increasingly embedded in curriculum design.

ABC levels of GenAI integration in the business curriculum

Our analysis identified three levels of GenAI integration: ad hoc, blended, and constructive. Table 1 outlines these distinctions across key dimensions.

Table 1 ABC levels of GenAI curriculum integration

Constructive integration represents a qualitatively different approach, grounded in constructive alignment, where intended learning outcomes, teaching activities, and assessment are deliberately designed to develop and evaluate students’ ability to use GenAI critically and effectively. At this level, GenAI is not an optional or supporting tool, but an integral component of disciplinary learning, with a clear pedagogical purpose and coherent role across the curriculum.

By contrast, ad hoc integration is characterised by occasional and isolated use, where GenAI is introduced as an optional or experimental tool without being planned into the broader curriculum design. Blended integration moves beyond this by incorporating GenAI into selected learning activities or tasks, giving it a more purposeful pedagogical role, but its use remains only partially embedded. Both approaches therefore fall short of the coherence and strategic alignment that define constructive integration.

The distinction between these patterns is therefore not simply a matter of more or less GenAI use, but of how GenAI is positioned within the curriculum: as an experiment, as a support, or as a capability to be deliberately developed. Although developed from business education contexts, this typology offers a lens that can be applied more broadly to understand how GenAI is positioned within different disciplinary curricula.

Why constructive integration matters

Across the cases, GenAI integration generated benefits and challenges across students, educators, and institutions. At the student level, reported benefits included stronger engagement, confidence, and employability-related skills, while the main risks centred on overreliance, inequality, ethical concerns, and ineffective outputs. For educators, benefits included efficiency gains, professional learning, and improved teaching performance, but these were accompanied by increased workload and the need to redesign activities and assessments. At the programme level, GenAI enhanced curriculum relevance but raised concerns about academic standards.

Figure 1 shows that these benefits and challenges were not distributed evenly across the three patterns of integration. Constructive integration displayed the strongest and broadest benefits, while ad hoc and blended approaches showed narrower gains alongside more exposed challenges. In other words, the issue is not whether GenAI brings value or risk, but how curriculum design shapes the balance between them.

Figure 1 Trade-offs of GenAI integration: challenges (red) vs benefits (green)

What makes constructive integration different is not the removal of challenge, but the stronger presence of educational value. In the study, constructively integrated cases were linked more clearly to student engagement, capability development, employability, and curriculum relevance because GenAI was embedded through aligned outcomes, activities, and assessment, rather than added on as a tool or support. Importantly, these cases also showed stronger educator development, including pedagogical reflection and confidence, despite workload pressures. This suggests constructive integration enhances both student outcomes and educator learning by embedding AI within coherent curriculum design.

How constructive integration is achieved

Table 2 presents examples from the modules in this study, showing how GenAI was constructively integrated into existing pedagogical strategies without requiring curriculum redesign.

Table 2 Constructive GenAI Integration into Existing Pedagogical Strategies

Taken together, the cases suggest several practical principles for integrating GenAI more coherently within the curriculum. These principles are not specific to business education, but reflect broader curriculum design considerations that can be adapted across disciplines with different pedagogical traditions.

  • Integration builds on existing pedagogical strategies: GenAI should be embedded within approaches already familiar to the discipline, such as project-based or simulation-based learning, without requiring curriculum redesign (Chugh et al, 2023).
  • Sharpen the role of GenAI by disciplinary purpose: In different contexts, GenAI supported strategic analysis, research and synthesis, reflective thinking, or data interpretation. Its value depends on alignment with module aims (Zhou & Milecka-Forrest, 2021).
  • Make AI use purposeful through assessment and evaluative tasks: In stronger cases, GenAI was connected to tasks that required students to interpret, justify, compare, or critique AI-supported outputs, rather than simply using AI to complete tasks (Biggs & Tang, 2010).
  • Support deeper student engagement through scaffolding: Structured guidance, such as prompting strategies, comparison activities, and reflective tasks, enabled more critical and purposeful use (Cukurova & Miao, 2024).

Overall, constructive integration is less about introducing new tools than about redesigning existing curriculum elements so that GenAI is meaningfully aligned with disciplinary learning.

Conclusion

The ABC levels developed in our study show that GenAI integration in business education does not follow a single trajectory but ranges from ad hoc and blended use to constructive integration. The key difference lies in approach: constructive integration embeds GenAI through aligned outcomes, activities, assessment, and scaffolding. The challenges observed across GenAI integration practices suggest an urgent shift from ad hoc GenAI integration toward strategic and constructive integration in business education. In this way, higher education can support students’ employability and capability development, strengthen educators’ professional and pedagogical confidence, and enable institutions to sustain coherent, future-facing curricula.

Dr Qianqian Chai is a Lecturer in Business and Management at Queen Mary University of London and Chair of the AI in Education Innovation Sub-committee in the School of the Arts. Her research focuses on AI in higher education, including curriculum design, academic integrity, and policy. q.chai@qmul.ac.uk

Professor Xue Zhou is a Professor in AI in Business Education and Dean of AI at the University of Leicester. Her research interests include digital literacy, digital technology adoption, cross-cultural adjustment, and online professionalism. xue.zhou@le.ac.uk


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Talking the talk: language for learning in higher education

by Estefania Gamarra, Marion Heron, Lewis Baker and Harriet Tenenbaum

Do you remember when you started university, and you were expected to use a whole new language? We don’t just mean new nomenclature such as ‘seminars’ or ‘tutorials’, but language that can help you make a clear argument or disagree politely with a classmate. This language, or educational dialogue, and in particular disagreeing politely, is critical to be an engaged citizen in a healthy democracy, without otherwise descending into unhealthy practices such as ‘cancel culture’ as recently highlighted in the media. In this blog post, we argue that universities have a responsibility not only to teach students how to talk in an academic context, but also for this teaching to be discipline-specific and embedded in the disciplinary study where possible.

There is a long-held misperception that all students who start university are able to talk the talk of the university, that is, they have the language skills, the terminology, and the confidence to articulate their opinions from their first day. This is just simply not true for many undergraduate students. Having English as a first language is also not necessarily an advantage. Bourdieu et al (1994, p8) said, “academic language… is no one’s mother tongue, not even that of children of the cultivated classes”.

What do we mean by language here? We have drawn on the pedagogy and research from compulsory school education, namely the work of scholars at Cambridge University. Their work on educational dialogue has been successfully incorporated into school teaching with impressive results. Educational dialogue here refers to communicative acts such as agreeing, disagreeing, reasoning and expressing ideas. Research in school settings has shown that encouraging such dialogue can boost academic attainment. One study highlighted the relationship between elaborating on ideas and attainment in reading, spelling, punctuation and grammar. Despite this compelling evidence, similar strategies have been underexplored in higher education.

In our university classrooms, we hear students say things such as: ‘I know the answer, but don’t know how to phrase it’ and ‘I need to learn how to express my answer like that’. So, if students are themselves noticing a need for academic language, why are we so behind in the higher education context? And more importantly, what language do these students need? Do they all need the same academic language to confidently talk the talk? This is exemplified by the dialogue below between two engineering students working on answering multiple-choice questions together, an excerpt from our forthcoming research:

Student A:  Yeah, listen, we need to be able when we say “force”, to say why.  

Student B:  Yeah, to flip it.  

Student A:  Because we were right, like, C is incorrect, but we don’t say why it is not incorrect.  

Student B:  I don’t know how to word it, you know.

In our current research project, supported by a Nuffield Foundation grant, we explore whether pairs of Foundation Year students across Engineering, Psychology and Bioscience, engaging in discipline-specific multiple-choice questions, can learn to develop these academic language skills and the extent to which they can do this in an academic year-long intervention programme.

Our early findings indicate that while students are capable of using academic language, the forms they adopt vary by discipline. For example, consider one of the most basic interactions in academic discussions – giving and asking for reasons. Typically, the default marker for requesting justification is “why?”. The following extract from a psychology discussion illustrates this:

Student A:  Why do you think that is?

Student B:  Because, uh, if you got negative emotion, you know, so that is not called positive psychology. Yep, yeah, so I’m thinking about understanding like how to prevent negative emotions.

In contrast, in science courses such as biology or engineering, it was more common to use “how?” rather than “why?” when asking for reasoning. Consider this extract from an engineering discussion:

Student A:  Yes. Then the same as D.

Student B:  D? How?

Student A:  And then it’s…

Student B:  Oh.

Student A:  And this is…

Student B:  So the arrow goes this way…

Student A:  So then P goes this way…

Here, Student B not only asks for the reasoning by using “how?”, but the response unfolds as a sequence of steps outlining the reasoning process. This example also highlights another subject-specific difference: while psychology students typically expand on each other’s arguments or examples, engineering students more frequently build on each other’s equations, often with the assistance of pen and paper.

So, based on these snippets of authentic student dialogues, let’s return to the question posed at the beginning. Yes, all students can and do need to learn academic language to talk to each other and develop understanding, but the type of language depends on the discipline. Disciplinary differences can be seen in the way students build on each other’s ideas (eg long turns, short turns) as well as the words and phrases used. The evidence from our project shows this.

We argue that learning to talk the language of higher education should not be considered a prerequisite but instead, should be an essential feature of the higher education curriculum embedded within disciplinary studies.

Why is this important? Integrating academic language training into the curriculum can enhance students’ academic confidence, foster a stronger sense of belonging, and ultimately improve retention rates. In a post‐COVID world, where student engagement is waning, this conversation‐based approach may also help rebuild the social and collaborative fabric of university life.

Moreover, the skills developed through such training are highly transferable beyond academia. Students acquire essential discussion and teamwork abilities that prove invaluable in their future careers. It is important to emphasise that developing these skills requires deliberate training; we must not assume that students will acquire them without practice and guidance.

Although students may already use discipline‐specific language, targeted training helps them become accustomed to engaging in – and, more importantly, listening to – disagreement. These conversational practices become part of their repertoires, enabling them to generalize these skills across various contexts. As noted earlier, we must all learn to engage in constructive disagreement to counteract cancel culture. While the manner of such discourse may vary by discipline, developing these skills is essential for active participation in a healthy, thriving democracy.

Estefania Gamarra Burga is a Postdoctoral Research Fellow at the University of Surrey. Her research interests include educational dialogue, discourse analysis, gender, and spatial cognition in STEM and higher education.

Marion Heron is Associate Professor of Educational Linguistics in the Surrey Institute of Education, University of Surrey. She supervises doctoral students on topics in the field of applied linguistics and higher education. She researches in the areas of language and education, with a particular interest in classroom discourse, genre and doctoral education.

Lewis Baker is a Senior Lecturer in the Faculty of Engineering and Physical Sciences and a Chartered Science Teacher. His research interests include teaching pedagogy and science education, often within a foundation year context.

Harriet Tenenbaum is Professor of Social and Developmental Psychology. Her research focuses on social justice in young people, everyday conversations, and teaching and learning across the lifespan.