The rise of artificial intelligence (AI) in higher education is reshaping universities as learning organizations, requiring a fundamental reassessment of knowledge creation, assessment, and academic integrity. While AI offers efficiency, innovation, and personalized learning, it also challenges traditional epistemic structures, raising concerns about the erosion of deep learning, critical thinking, and independent inquiry. This prompts a crucial question: Should AI be integrated as a tool for institutional learning, or does it undermine universities' role in fostering intellectual growth? AI has the potential to enhance organizational learning by automating knowledge management, supporting faculty development, and enabling adaptive learning (Senge, 1990). Institutions that engage in double-loop learning (Argyris & Schön, 1978) can go beyond policy adjustments, rethinking pedagogy and assessment models to align with AI’s presence in knowledge production. This approach fosters AI literacy, helping students critically engage with AI rather than passively relying on it. However, overreliance on AI risks cognitive offloading, reducing memory retention, decision-making, and analytical depth (Jackson, 2025; Zhai et al., 2024). AI’s propensity for misinformation and epistemic bias (Sidiropoulos & Anagnostopoulos, 2024) raises concerns about its role in academic environments. Additionally, AI disrupts traditional learning gap models (Light et al., 2009), allowing students to bypass foundational learning stages, prompting critical institutional questions: • How can assessments prioritize process over product, ensuring human cognitive engagement? • Should universities embed AI literacy to cultivate critical evaluation skills? • How can faculty adapt to integrate AI responsibly in pedagogy? To address these challenges, universities must rethink assessment strategies, shifting from product-based to process-based evaluation, ensuring students engage in critical reasoning and intellectual agency. Institutions should also train faculty in AI integration, moving from a reactive to a proactive learning model. Rather than banning AI, universities should redefine academic integrity, establishing clear, ethical AI usage guidelines that promote responsible engagement. By adopting organizational learning principles, universities can transform AI from a disruptive force into a catalyst for pedagogical evolution, ensuring that knowledge remains a dynamic, critical, and human-centered process in the AI age.

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