From Fragmentation to Phronesis: A Hermeneutic Pedagogy for Business Education in the Age of Generative AI

Synthesis: Heron and Meekins (2026) argue that business education should respond to generative AI neither by prohibition nor by treating automation as pedagogically neutral, but by re-centering practical wisdom — phronesis — through a hermeneutic pedagogy. Their claim is that AI makes a longstanding problem more visible: business schools have treated the production of information as evidence of understanding, when professional judgment depends on interpreting a particular situation, examining one's own preconceptions, encountering competing perspectives, and taking responsibility for a decision. They propose REVEAL, a six-movement heuristic (Research, Examine, Viewpoint, Engage, Align, Learn) that positions AI as a provisional interlocutor rather than an invisible coauthor, and illustrate it with an enterprise-technology implementation case.

Key Findings

1. The problem is information production mistaken for understanding. GenAI intensifies a pre-existing tendency in business education to accept polished analysis as evidence of competence.
2. Phronesis is the organizing aim. Practical wisdom is the capacity to deliberate well about what is good and fitting in a concrete situation, and it cannot be delegated to a system that generates options without accountability.
3. Hermeneutics supplies the method. Gadamerian concepts — historically effected consciousness, dialogue, application, fusion of horizons — frame understanding as situated, dialogic and revisable rather than as the application of a procedure.
4. REVEAL translates the philosophy into teachable moves. Research (let the situation address the learner), Examine (interrogate preconceptions), Viewpoint (map competing horizons), Engage (communicate across them), Align (act without erasing plurality), Learn (return with altered understanding).
5. AI is positioned as a non-ideal dialogic partner. It can summarize research, simulate a stakeholder, or draft an announcement; students must judge, revise and answer for what they use.
6. The framework is aimed at judgment-rich courses. Leadership, organizational behavior, strategy, business ethics, change management, human resource development and business communication are the intended settings.

Why prohibition and normalization both fail

The article opens with two common responses to GenAI in business schools: ban it, or treat it as a neutral efficiency tool. The first assumes that removing the technology restores the conditions under which learning was previously evidenced, which the authors argue is both impractical and historically naive. The second assumes that because AI can produce a competent-looking deliverable, the learning that used to be inferred from that deliverable is still being produced.

Both responses skip the interpretive question. A stakeholder map generated in seconds may be accurate and still tell an instructor nothing about whether a student understands which stakeholders matter, why their interests conflict, or what a leader owes them. The authors' alternative is to ask what kind of human judgment the course is trying to develop, then design AI use so that judgment becomes visible.

Phronesis and the hermeneutic frame

Phronesis, drawn from Aristotle and extended through recent work on business ethics education, is the cultivated capacity to deliberate about what is good and fitting in circumstances. It is not a rule or a technique; it is exercised in concrete situations and developed through shared deliberation. The authors add a Gadamerian account of understanding: learners bring historically effected consciousness — preconceptions formed by their history and culture — and understanding happens through dialogue in which those horizons meet and are revised.

That frame changes what counts as a good business classroom activity. A case is no longer a problem to be solved correctly but a situation that addresses the learner, who must examine how it appears to them and to others. AI's fluency is useful precisely because it makes the alternative interpretations easy to generate and therefore easier to interrogate. But the system cannot experience accountability to stakeholders, cannot stand within the consequences of a decision, and cannot take responsibility for interpretation. It is, in the authors' term, a non-ideal dialogic partner: useful for surfacing options and simulating perspectives, unsuitable as an authority.

REVEAL in practice

Research asks students to build an interpretive context brief rather than jump to a plan, using AI to summarize literature and generate questions while examining organizational history, prior initiatives and trust. Examine requires a reflexive account of initial assumptions — for example, a student who reads resistance as irrationality may discover a bias toward system optimization. Viewpoint produces a stakeholder horizon map that shows how the problem looks from sales, marketing, IT and leadership, and how those perspectives change the question. Engage asks for a communication artifact plus a rationale, with assessment focused on interpretive responsiveness rather than polish; comparing an AI-generated announcement with a revised one is an explicit move. Align requires an implementation proposal that preserves unresolved tensions — phased rollout, co-design, data-governance rules, human review — with justification rather than false consensus. Learn returns students to the whole with altered understanding.

The accompanying case is a mid-sized organization implementing an AI-enabled enterprise stack, with sales worried about surveillance, marketing about creative autonomy, IT about legacy integration, and executives expecting rapid adoption. The case is designed so that no single deliverable can be correct; the assessment question is whether the student can hold the plurality and still act.

What this means for practice

• Start with the judgment, not the tool. Name the professional judgment the course develops, then decide where AI helps surface alternatives and where it substitutes for the work.
• Make preconceptions an assignment. A short reflexive account of how a student first framed the case gives instructors evidence that a polished deliverable cannot.
• Use AI to generate the perspectives students must reconcile. Simulated stakeholders are useful when students are required to critique and revise them, not when they are accepted as findings.
• Assess the rationale, not the artifact. A communication plan with a justification shows interpretive responsiveness; a clean memo does not.
• Keep unresolved tensions in the deliverable. Requiring students to name what cannot be reconciled is closer to professional practice than forcing consensus.

Limitations

• This is a theoretical article, not an empirical study; REVEAL is illustrated with a constructed case and no student data.
• The framework depends on instructors who are themselves comfortable with hermeneutic concepts, and the article does not report how faculty were prepared or how consistently the six movements can be enacted across courses.
• No assessment instruments, rubrics or reliability evidence are provided, so claims about making judgment visible remain programmatic.
• Phronesis is difficult to operationalize and to grade; the article does not address how an instructor distinguishes a genuinely interpretive response from a fluent imitation.
• The business-school focus limits transfer, and the authors do not discuss workload or class size.

Citation

Heron, P., & Meekins, D. (2026). From Fragmentation to Phronesis: A Hermeneutic Pedagogy for Business Education in the Age of Generative AI. The Interdisciplinary Journal for Research on Pedagogy and Learning Design, 1(1).