In recent years, a profound transformation has been underway, reshaping higher education and institutional structures at an unprecedented scale. Artificial intelligence disruptively intervenes in traditional ways of knowledge acquisition, knowledge delivery, and knowledge exchange in higher education as well as academic research. While most studies focus on challenges and opportunities of generative AI for higher education (Michel-Villarreal et al., 2023), explore the impact of artificial intelligence on teaching and learning (Popenici & Kerr, 2017), or discuss responsible implementation (Perea & Lankathilaka, 2023), broader discussions also point toward AI’s deeper role in reshaping authority structures and decision-making processes within institutions (Bearman et al., 2023). AI is not simply a tool for efficiency but has far-reaching implications for institutional roles, governance, and the balance of epistemic authority between educators, administrators, corporations, and students.

A core issue of this transformation is the learning in, of, and between organizations. AI is increasingly present in learning environments, influencing teaching relationships, institutional structures, and inter-organizational exchanges. As its implementations become relevant inside and outside the classroom, AI is integrating into the fabric of universities (Jain & Jain, 2019). This shift extends beyond academia to broader professional and institutional learning settings, where AI plays a role in shaping knowledge validation, expertise development, and workforce education. These developments prompt further inquiry into the extent to which AI influences the distribution of knowledge and authority within institutional structures. Considerations include how AI might shape existing hierarchies, the ways in which it may complement or challenge human expertise, and the frameworks that could guide its integration in academic and professional settings while maintaining ethical and educational integrity.

AI does not simply mirror existing knowledge structures but actively contributes to shaping and legitimizing knowledge within institutions. The processes through which AI systems prioritize, exclude, and validate information remain complex and often opaque, raising questions about their broader implications for institutional discourse and decision-making.

As AI becomes more integrated into organizational learning, questions arise about the extent to which human oversight influences its role in shaping intellectual engagement. In higher education, this includes considerations of how assessment frameworks might evolve to balance AI-generated content with critical dialogue and reflective engagement. In professional and institutional settings, the discussion extends to how AI literacy, ethical considerations, and evolving guidelines shape decision-making processes and knowledge practices.

Beyond immediate technological adoption, universities and organizations encounter ongoing discussions about how AI influences governance structures, assessment models, and knowledge-sharing practices. The evolving discourse on AI literacy raises questions about how individuals in academic and professional settings engage with AI-generated knowledge. Institutions continue to explore ways to approach AI-driven learning while balancing considerations of transparency, accountability, and intellectual rigor in shaping knowledge production.

Rather than viewing AI as a purely external influence on institutions, this symposium will consider the ways in which AI is entangled with knowledge production, assessment, and governance across various settings, such as universities, corporations, and research institutions. Rather than positioning AI simply as a tool for automation, discussions will address its potential implications for knowledge legitimacy, expertise, and epistemic authority. As AI’s role in institutional learning continues to develop, questions persist regarding its impact on accountability, human judgment, and organizational values. This symposium will investigate how various sectors engage with AI literacy, ethical considerations, and institutional strategies to shape their approaches to AI integration. The discussion will focus on the ways learning environments evolve in response to these changes, highlighting tensions between automation, human oversight, and the preservation of critical inquiry.

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