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Research Highlights

  • The Problem: Educational leaders are challenged by the rapid integration of artificial intelligence, which is advancing faster than the establishment of shared trust, clear policies, and understanding of risks related to academic integrity and professional identity. 

  • The Method: The researcher leverages paradox theory and trust theory to analyze leadership responses to artificial intelligence tensions, utilizing a 2026 national survey of 1,057 higher education faculty members conducted by the American Association of Colleges & Universities and Elon University. 

  • Quantitative Finding: 95% of faculty respondents report that generative artificial intelligence will increase student overreliance on the tools; 90% believe these tools will diminish critical thinking skills; 83% forecast negative impacts on student attention spans. 

  • Qualitative Finding: High-trust environments enable "both/and" sense-making where competing demands are navigated as tensions; low-trust climates collapse into "either/or" logic focused on simplification, control, and compliance; unresolved tensions signal perceived threats to professional agency and community wellbeing.

Abstract

Artificial intelligence integration in education is advancing faster than shared trust and understanding are being established. Educational leaders are responding in real-time to questions of risk and efficacy while navigating conditions that are still forming. This article situates artificial intelligence integration within the paradox of educational leadership, identifying trust as the critical condition shaping leaders’ interpretations of the tensions.

Leveraging paradox and trust theory, the article explores how competing demands are not problems to be solved, but tensions to be navigated. When trust is present, leaders respond to tension points through both/and sense-making; when trust is absent, decision-making collapses into either/or approaches.

The article offers actionable practices for building shared understanding, establishing guardrails, and creating conditions for the integration of reflective, contextually responsive artificial intelligence.

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