Abstract:

As generative artificial intelligence (AI) becomes embedded across educational systems, leadership challenges increasingly shift from technological sensing to strategic and institutional governance. Leadership education has widely adopted design thinking, innovation sprints, and human-centred experimentation—often supported by generative AI to accelerate ideation and iteration. However, these approaches typically emphasise product and service innovation rather than strategy formation, accountability, and governance. Consequently, leadership programmes may underprepare participants for making robust strategic choices when AI-driven change is fast, contested, and institutionally constrained.

This paper proposes a scenario-based learning design that treats scenarios as strategic prototypes rather than abstract forecasts. Building on design thinking and innovation research, scenario development is structured as an iterative process for testing assumptions about technological trajectories, stakeholder expectations, value creation, organisational capabilities, and governance constraints. A backcasting procedure translates plausible future scenarios into present-day governance-ready choices—clarifying which policies, capabilities, decision rights, and checkpoints must be established for strategies to remain viable and responsible across alternative futures. In this way, scenario work becomes a bridge between exploration and commitment, linking uncertainty to concrete decisions about governance structures and organisational business models.

Empirically, the study draws on two qualitative evidence streams:
(a) longitudinal material from leaders navigating digital- and AI-driven transformation in educational and knowledge-intensive organisations, and
(b) assessed leadership learning activities in which more than 200 participants conducted scenario-based strategy and business-model work.

Findings indicate that governance quality improved when participants moved from “scenario writing” to scenario testing through explicit assumption checks, value trade-off discussions, and governance checkpoints. Participants reported high learning value from the prototyping logic, and several described transfer of these routines into leadership practice beyond the programme. Generative AI added value when its role was clearly bounded and outputs were critically reviewed within the governance process.

The paper contributes to educational leadership and management research by demonstrating how scenario-based learning can shift leadership education from forecasting to prototyping strategic direction and governance. It offers a scalable approach for integrating responsible AI governance into strategy formation and business-model choices in leadership development.

Keywords:

Educational leadership and management, Leadership education, Creativity and design thinking, Scenario-based learning, Strategic prototyping, Backcasting, Governance capabilities, Responsible AI governance, Impact of AI on education.