The rapid integration of artificial intelligence (AI) into the institutional and organizational context of higher education has created a paradoxical cycle: students increasingly rely on AI to generate assignments and solve tasks, while educators adopt AI-driven tools to assess submissions (Michel-Villarreal et al., 2023; Wang et al., 2024). This dynamic risks reducing learning to a transactional exchange between algorithms, where AI evaluates AI-generated work, bypassing the human critical engagement essential for intellectual growth (Porayska-Pomsta, 2024). Even when transparency policies are implemented (Sidiropoulos & Anagnostopoulos, 2024), they fail to address the core issue of the learning process in organisation, thereby undermining the foundational goals of higher education and leaving the institutions as such purposeless. This paper proposes reorienting pedagogical frameworks toward organizational education, emphasizing structured, collaborative learning ecosystems grounded in Weber's concept of transepistemic design (Keller & Weber, 2020). We advocate for human-in-the-loop (HITL) systems that ensure AI tools remain subordinate to the collective judgment of the learning community, aligning with Weber's emphasis on the social embeddedness of learning processes within organizations. Our approach considers the multi-layered nature of organizational learning, addressing individual (micro), organizational (meso), and societal (macro) levels of educational processes. We reimagine assessment design to prioritize process-oriented tasks that necessitate human cognition, creativity, and critical reflection, ensuring students engage deeply with content rather than outsourcing understanding to machines. To future-proof education within organizational contexts, we integrate future skills such as metacognition, systems thinking, and design thinking, aligning with Weber's concept of 'Bildung' in organizational settings. Curricula center on authentic problem-solving, where AI acts as a scaffold for research and ideation, not a substitute for original thought. We argue for ethical guardrails and faculty training to balance AI's efficiency with accountability, addressing the challenge of data overload in transdisciplinary work. Educators must teach students to interrogate data sources, biases, and relevance, framing AI as a tool for curation rather than conclusion. This reflection calls for a symbiotic integration of AI within organizational education frameworks, where pedagogical practices prioritize human agency, critical thinking, and disciplinary rigor. By embedding mentorship, fostering collaboration, and recentering curricula on knowledge produced within academic and organizational traditions, institutions can ensure AI enhances the cultivation of agile, ethically grounded thinkers capable of navigating complex organizational environments.

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