THE LOST ART OF PROCESS As generative AI embeds itself into everyday academic and professional life, management education is witnessing a fundamental shift. Technology has drastically simplified the creation of outputs, but it has done little to help students explain the reasoning behind them, notes Aston Business School’s Paul Jones
A I hasn’t replaced thinking; it’s exposed how little we teach it. As the months progressed last year, I found myself having a new kind of conversation with students. The work they submitted was often fluent, well-structured and confidently expressed. The arguments appeared coherent. The language was polished. On the surface, everything seemed to be working. However, when I asked simple, follow-up questions, the confidence sometimes faded. Why did you choose this approach? What alternatives did you consider? What mattered most in making that decision? Too often, the answers were hesitant. Students could describe what they had produced, but struggled to explain how they arrived there or what guided their judgement along the way. The finished product was familiar; the process that created it was not. This is not a story about students trying to deceive, nor is it a lament about declining standards. It reflects a shift that many schools are encountering as generative AI becomes part of everyday academic and professional practice. AI has made it easier to produce outputs. What it has not made easier is how to explain thinking. The challenge is no longer whether students can generate answers, but whether they can account for their decisions, justify their reasoning and articulate the values shaping their choices. The problem is not artificial intelligence: it is judgement and it is thinking. Beyond an AI problem It would be easy to frame this as an AI problem. Much of the public debate already does. Concerns about plagiarism, authenticity and academic integrity dominate headlines, policy discussions and staff briefings. In response, institutions rush towards detection tools, revised regulations and carefully worded guidance, but focusing too
narrowly on AI risks missing the more uncomfortable insights it offers. Generative AI has not replaced thinking; it has exposed how little explicit attention we have been giving to it. When students struggle to explain their reasoning, this is rarely because AI has hollowed out their intellect. More often, it is because education systems have rewarded the appearance of coherence more consistently than the quality of judgement behind it. Business education has become very good at teaching students how to produce convincing outputs. Students learn to structure arguments, apply models, reference literature and reach defensible conclusions. These are important capabilities, but they are not the same as learning how to weigh alternatives, recognise trade-offs, or sit with uncertainty long enough to make a considered decision. AI makes this distinction impossible to ignore. When a tool can generate a fluent response in seconds, the value of education can no longer rest solely on the production of answers. The differentiator becomes the ability to explain why one course of action was chosen over another and what assumptions shaped that judgement. In this sense, AI acts less as a disruptive force and more as a diagnostic one. It reveals where learning has been focused on outcomes rather than processes and where assessment has privileged polish over understanding. The challenge it poses is not primarily technological, but educational. Are we teaching students how to think, or merely how to present thinking done elsewhere? Knowledge, judgement & leadership At the heart of this challenge is a distinction business education has always relied on, but rarely articulated clearly: the difference between knowing something and judging something. Judgement is not the same as knowledge; it is not a model, a framework, or a body of content that
42 Ambition • ISSUE 4 • 2026
Made with FlippingBook - Share PDF online