Knowledge, in its deepest sense, is not an immediate act nor a simple accumulation of information but a historical, progressive, and dialectical process through which the human spirit recognizes itself in its relationship with the world. Through this leadership movement in organizations, decision-makers not only learn about their reality but also seek to influence and transform it, understanding the practices of organizational knowledge management (creation, exchange, and transfer) in the public university (Mohammad-AlQhtani, 2025; Tarko-Kassa and Jing, 2025; Vyas, 2024), starting from the orchestration of a conservatory for the development of an artificial intelligence laboratory. This is achieved through a descriptive-interpretive and critical phenomenological analysis (Hegel, 1971: 1807) of secondary documents and narratives of institutional practices, using grounded theory as the analytical method and axial coding to identify the institutional, human, leadership, resource, and infrastructure factors that influence their effectiveness. This is done from the perspective of knowledge-based theory (Miller, 1995), institutional theory (Meyer and Rowan, 1977; Scott, 2008), and theories of organizations and social change (dependency theory, modernization theory, conflict theory, dynamic capabilities theory, life cycle theory, and teleological theory). The following question is addressed: How does the structural flexibility of organizations in emerging economies facilitate or hinder technological leaps relative to legacy innovation models? How do institutional resistance and new dynamic capabilities interact during the transition to an AI-based management model? To what extent does the transfer of tacit knowledge, mediated by international collaboration networks, determine the capacity of organizations in developing countries to absorb explicit knowledge? This allows us to identify categories, relationships, and patterns linked to institutional governance rules, the reach of university services, the use of models, algorithms, and frameworks, as well as the university's core values and social purpose, in relation to the impact of research funded by public sources. We describe three isomorphic processes coercive, mimetic, and normative based on pressure for legitimacy and mandate exerted by national organizations upon which the creation of new knowledge and the formation of expectations for social employability depend. This also addresses global uncertainty, improves the efficiency of the teaching profession, fosters the development of 21st-century skills, and optimizes educational management. The use of procedural manuals, workflows, and collaboration agreements is considered a form of knowledge transfer that universities replicate by adopting similar methods. In line with the European-international dimension, the study aligns with Sustainable Development Goals 4, 9, 16, and 17 by analyzing how knowledge governance and the management of AI laboratories in public universities contribute to responsible innovation, international academic cooperation, and institutional strengthening in emerging economies through Open Science and Responsible Research and Innovation (RRI) approaches. The European public university is conceived as a key player in the social innovation ecosystem, capable of articulating institutional leadership, ethical values, and digital transformation in service of the common good. Beyond the professionalization of their members (researchers, administrators), they share a common educational foundation and behavioral models. This means that European and Latin American actors are assuming increasingly similar roles in their efforts to transform the university context and consolidate dynamic capacities through international academic networks.

Using a qualitative (Denzin et al., 2023), interpretive, phenomenological, and emergent design (Creswell, 2013), faculty narratives were analyzed to understand the complexity of artificial intelligence in the university setting, without recourse to predefined theoretical frameworks. The analysis was conducted using the constant comparative method of grounded theory (Strauss & Corbin, 1990), supported by MAXQDA, a software tool for organizing, tracking, and systematizing the data. The corpus consisted of the complete transcript of the Institutional Program on Artificial Intelligence in the University Setting (UJAT, 2026), totaling 3 hours, 27 minutes, and 2 seconds, equivalent to 1,807 paragraphs, 25,578 words, and 159,732 characters. The analytical process continued until theoretical saturation was reached, identifying 64 emergent categories. Through axial coding, 17 core categories related to learning, teacher training, digital culture, digital literacy, artificial intelligence, intellectual property, technology, and the university were integrated. Subsequently, selective coding allowed for the articulation of three thematic axes: (1) teaching perspectives; (2) AI training pathways; (3) a future vision of the university as a base for international academic cooperation and institutional strengthening in emerging economies. For theoretical validation, the Lakatos Scientific Research Program (Jiménez-León, 2026) was followed, structuring an integrative model: (a) core: knowledge as a strategic resource, AI adoption as a multicausal change process, dual cognition in natural thinking (pedagogical judgment, criteria, meaning) and automated thinking (algorithmic assistance, suggestions, optimization); (b) protective belt: empirically adjusted dimensions, resources, institutional pressures, dynamic capabilities, micropolitics, modernization/leapfrogging; (c) layered model architecture. Cognitive Layer A (micro): Teacher's natural thinking, pedagogical judgment, expert intuition, situated ethics, metacognition. Automated thinking: suggestions, templates, generation, automated evaluation. Knowledge Layer B (meso): Creation, sharing, and transfer processes, mechanisms, and communities of practice. Institutional Layer C (macro): rules, legitimacy, authorities, ethics, intellectual property, data governance, accreditation. Social/organizational Change Layer D (expanded macro): technological dependence, inequality of capabilities, resistance, cycles, public purposes. Code grids and diagrams were used for analytical visualization. Methodological rigor was ensured through criteria of credibility, dependability, and confirmability, via peer review, analytical traceability, and comparison with open institutional sources. The study was approved by the University Research Ethics Committee. Generative artificial intelligence was used in the preparation of this manuscript as a support tool for improving writing and linguistic revision with the Grammarly and Transkriptor tools. The intellectual content, analysis, interpretation, and conclusions are the sole responsibility of the authors.

This study demonstrates that the integration of Artificial Intelligence (AI) in higher education transcends mere technological acquisition; it is, fundamentally, a challenge of governance and organizational change. Under the principles of Open Science and RRI (Research, Innovation, and Research), an effective transition depends on overcoming mimetic isomorphism, in which institutions replicate external structures for legitimacy while maintaining anachronistic pedagogical practices of control and prohibition. The evidence suggests that structural flexibility is a necessary but insufficient condition for technological leapfrogging. Without a clear governance framework, this flexibility leads to fragmented and "ceremonial" adoptions that fail to scale to the institutional level. Conversely, the consolidation of dynamic capacities enables universities to reconfigure their resources, moving from initial resistance toward critical regulation and meaningful pedagogical appropriation. In the context of developing universities, a critical gap is identified between the availability of explicit knowledge (guidelines and regulations) and its actual implementation. True transformation doesn't happen by decree, but rather through the transfer of tacit knowledge via communities of practice, mentorship, and co-design of instruction. These elements act as the engine of absorption, allowing AI to be "territorialized" in the classroom. As the following excerpt indicates: [...] "I decided to go to the parks under the mango trees with the cell phones, to provide training to help democratize the tool so that others can use it." Participant. Finally, collaboration within international academic networks is vital to reducing uncertainty and increasing institutions' capacity to absorb this knowledge. In short, the meaningful adoption of AI requires a systemic articulation between tiered teacher training, knowledge management, and ethical governance oriented toward the common good. Only in this way will technological innovation translate into a real educational transformation, capable of closing gaps in the Latin American university environment.

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