This hesitation is nothing short of a missed opportunity. More than just a technological disruption, generative AI is a powerful catalyst for reflection. It presents us with a rare and urgent invitation to reconsider our methods, to do better and to discover more effective ways to educate—ways that move beyond surface-level mimicry to foster deep, authentic learning. It is an opportunity to be seized. The challenge of AI, therefore, is not a passing technological problem to be solved; it is a transformative, educational reality that we must now proactively shape. It is a time for a deliberate, ambitious and proactive redesign of our core mission.

No existing playbook defines appropriate responses. Solutions require time for deep thought, learning and experimentation, collective ambition, and progress demands that multiple actors in higher ed change their established practices and assumptions.

From reactive tweaks to proactive redesign

This distinction between a reactive and proactive response maps directly onto Ronald Heifetz’s adaptive leadership framework (Heifetz et al., 2009). Heifetz distinguishes between technical problems—challenges with known solutions that can be addressed by existing expertise and procedures—and adaptive challenges, which are problems that demand fundamental changes in values, beliefs, roles, relationships and work approaches. The emergence of GenAI in higher education represents a typical adaptive challenge: no existing playbook defines appropriate responses, solutions require time for deep thought, learning and experimentation, collective ambition, and progress demands that multiple actors in higher ed change their established practices and assumptions.

Treating Generative AI as just a technical problem is a flawed, reactive approach. This view traps us in an exhausting and counterproductive technological arms race (e.g., detection versus evasion) and stems from a narrow view of education. A proactive approach, by contrast, shifts the focus from the tools back to our educational foundations. It recognises that technology is never neutral. As Fawns (2022) argues, it is “entangled” with our pedagogy, values and objectives. This concept of entanglement highlights a significantly complex, multi-directional and systemic relationship. The influence isn’t just between the tool and our teaching; it extends in all directions, shaping and being shaped by our societal norms, our personal relationships and even our physical environments. This deep-seated nuance means we cannot simply “add” GenAI without it having deep, cascading implications. Its presence demands a re-evaluation of everything. We must reconsider why we teach and how learning is demonstrated within this entire web of connections. This proactive stance moves us from fear and inaction to intentional design.

A ‘Why’ is needed

I often hear valid concerns from colleagues about a lack of time and resources to take on such a fundamental redesign. These are not trivial concerns. But significant change is never driven by an abundance of resources; it is driven by a clear and shared ‘why’.

That ‘why’ is perhaps twofold.

First, there is the urgent professional responsibility to ensure the value and integrity of our diplomas. We must be able to validly certify the competencies we claim to teach.

But second, and far more powerfully, this new reality presents a positive opportunity. It compels a focus on the essential, irreplaceable human skills that, as Gonsalves (2024) points out, are demanded by this new context. This is the chance, in my view, to make education more profound, challenging and human.

Trusting professional craftsmanship

If we commit to this redesign, it cannot be a top-down, one-size-fits-all mandate. No central committee can prescribe the perfect AI response for every discipline, from nursing to new media. The real, nuanced answers will come from the professionals who live and breathe their subjects and understand the needs of their students. A successful strategy, therefore, must be built on a deep trust in the professional craftsmanship of our educators. This approach means that the role of the institution is not to dictate specific micromanagement actions, but to facilitate and guarantee the process happens. We must create useful frameworks, start conversations and provide the support that empowers academic teams to find their context-specific choices and solutions. This method is designed to build what Hattie’s (2023) work confirms as one of the most powerful influences on student achievement: collective teacher efficacy. But as Hattie warns, this is not merely a shared feeling of confidence; it is a rigorous, evidence-based belief that must be “fed with the evidence of impact.” The institutional role, therefore, is not just to simply encourage collaboration but to foster what is described as “nonthreatening, evidence-based instructional environments.” By providing frameworks for “pedagogically productive inquiry” focussed clearly on student impact, we empower educators to become “evaluators” and “change agents” (Hattie, 2023). This fosters a genuine culture of collective learning where colleagues explore, learn and form meaning together in a supportive environment.

Finding a collective direction

Trusting professional craftsmanship is a core principle. To make that trust meaningful and effective, it needs a destination. A purely “bottom-up” approach on its own risks fragmentation. To truly enable and support educators, an institution must facilitate a collective process to find shared meaning and set a clear direction.

This isn’t about creating a rigid, top-down checklist. It’s about the institution taking responsibility for guiding a fundamental conversation. This meta-level work isn’t a substitute for the necessary, practical reforms in professional development, curriculum design (including AI literacy considerations and changing learning outcomes), teaching practices and assessment choices. Instead, it must happen in addition to and in combination with them, providing the essential ‘why’ that guides those ‘hows’.

Crucially, this process demands that institutions provide decent time. These are not technical problems; they are adaptive challenges that touch the core of our identity. Time is essential for professionals to collectively find the sense in the nonsense—to grapple with complex ethical trade-offs and ask deeply existential questions: What does it mean to teach? What does it mean to learn? How and what should we change, and wherefore (why)?

Only by carving out this reflective space can an institution move beyond reactive fear and action poverty and ask the right foundational questions. For example:

  • Purpose: How will we ensure that every innovation serves our core humanistic goals, not mere metrics of efficiency?
  • Competence: What does it mean to be a “literate” graduate in an age of AI? This requires defining AI’s role as both a tool and a complex subject of study.
  • Ethics: What is our non-negotiable institutional stance on inclusion, data ethics and sustainability? This creates a framework for continuously asking who is served and who might be harmed.

Only by collectively grappling with these questions can an institution form a coherent direction. This shared direction is what ultimately makes true empowerment possible. It becomes the framework that guides planned action, aligns professional development and gives educators the confidence and support —secure in the knowledge of what they are collectively trying to achieve.

This commitment to finding and adapting that direction—to becoming a true learning organisation—is what moves the sector from a state of stagnation to one of shared, proactive purpose.

References

Fawns, T. (2022). An Entangled Pedagogy: Looking Beyond the Pedagogy—Technology Dichotomy. Postdigital Science and Education, 4(3), 711-728. https://doi.org/10.1007/s42438-022-00302-7

Gonsalves, C. (2024). Generative AI’s Impact on Critical Thinking: Revisiting Bloom’s Taxonomy. Journal of Marketing Education, 02734753241305980. https://doi.org/10.1177/02734753241305980

Hattie, J. (2023). Visible Learning: The Sequel: A Synthesis of Over 2,100 Meta-Analyses Relating to Achievement. Taylor & Francis.

Heifetz, R. A., Grashow, A., & Linsky, M. (2009). The Practice of Adaptive Leadership: Tools and Tactics for Changing Your Organization and the World. Harvard Business Press.

Little, J. W. (1990). The Persistence of Privacy: Autonomy and Initiative in Teachers’ Professional Relations. Teachers College Record, 91(4), 509-536. https://doi.org/10.1177/016146819009100403