This paper critiques generative AI in leadership education as an instrument of optimization, where summarizing, coaching, and personalizing risk producing fluent, compliant leadership subjects while narrowing ethical inquiry. It develops Trickster AI, a speculative pedagogical framework informed by Ananse, Èṣù, and Māui, where contradiction, delay, reversal, humor, and refusal expose hidden premises of authority. The paper reframes friction as culturally situated and politically consequential; develops refusal as epistemic, procedural, and care-based practice; and proposes Leadership Through Rupture, where contradiction becomes information. It also addresses cultural extraction through consent, co-design and Indigenous data governance. A companion paper details implementation.
This qualitative case study examines how associate deans at a medium-sized Catholic university respond to the rapid integration of generative artificial intelligence (AI). Guided by decolonial and feminist frameworks, the study analyzes survey data from seven academic leaders to understand their concerns, decision-making processes, and emerging practices. Our findings reveal uneven adoption, limited institutional guidance, and ongoing concerns about academic integrity, equity, and bias. The study also highlights the often-unseen relational and emotional labor involved in supporting faculty and students. We conclude by recommending flexible, collaborative approaches to AI governance that attend to context, care, and diverse ways of knowing.
This article presents a technical architecture and classroom protocols for Trickster AI, an approach to generative AI in leadership education that prioritizes epistemic friction over optimization. Building on a companion paper, it operationalizes trickster pedagogy through a five-layer system: pedagogical policy, cultural logic, contradiction engine, rupture scheduler, and debrief-evidence layers. It presents four protocols: the contradiction clinic, refusal studio, delay device, and relational accountability simulation, guiding interaction, debriefing, accessibility, trauma-informed care, and relational assessment. The central contribution is a bridge between decolonial theory and educational design practice, showing epistemic friction can be specified as a design problem, ethically governed and translated into pedagogical protocols.
Artificial intelligence (AI) is reshaping decision-making and organizational life, yet leadership development in higher education has not evolved at the same pace. To address this gap, the article proposes the concept of the "augmented leader" and develops a four-competency framework for AI-integrated leadership: algorithmic literacy, strategic use of AI, ethical leadership and governance, and culture and adoption. Based on an integrative literature review and expert-informed thematic synthesis, the framework offers a structured perspective on leadership in AI-mediated environments. The article argues that higher education must embed AI as a core leadership capability, preparing students to lead effectively and responsibly in human-AI systems.
This case study investigates "The Worldbuilding Project," a collaborative initiative between two diverse San Diego high schools that offers a model for AI leadership development grounded in critical pedagogy. Utilizing a six-step worldbuilding methodology, students prototyped socio-technical futures and authored a collective social contract to reclaim human agency over automated cognition. Findings suggest this cross-institutional partnership fosters a "Post-Digital Leadership" paradigm, prioritizing civic responsibility over technical proficiency. By centering student voices, this study provides a blueprint for educational institutions to transition from reactive prohibition toward humanizing, future-oriented digital governance.
