TL;DR
The Ethico-Regulatory Governance (ERG) Framework is proposed, a conceptual model designed to bridge global ethics with local compliance, and offers a scalable, adaptable solution for universities navigating the complexities of GenAI.
The Ethico-Regulatory Governance (ERG) Framework is proposed, a conceptual model designed to bridge global ethics with local compliance, and offers a scalable, adaptable solution for universities navigating the complexities of GenAI.
Universities are going through an AI ready transition. Evolving legacy educational system to a self-developing optimized system is a crucial to stand globally competitive. This paper proposes ‘Flexible, Logical, Decentralized and Human-guided’ framework to accommodate Artificial Intelligence with utmost emphasis on ethics management, governance, policy and equitability. At a heart of this plan lies an internally-household, self-developing Large Language Model (LLM) integrated within sandbox structure to negate risks related to data privacy and model hallucination. Students are empowered with adaptive, self-paced learning environments, while an academic staff can be benefit from capacity-building modules. The system automates assessment processes to offer timely constructive feedback without any bias. It balances the utilization of automated systems with effective human-guided governance making it an adaptable, secure framework for digital transformations at universities.
This Viewpoint argues that prevailing ethics-based and compliance-oriented approaches to artificial intelligence (AI) in health are insufficient for the dynamic, context-dependent realities of contemporary AI systems. It proposes a shift toward collaborative stewardship, a model that emphasizes shared responsibility, continuous learning and meaningful stakeholder participation across the full lifecycle of AI in health. The analysis draws on a structured synthesis of peer-reviewed studies, major international policy documents and interdisciplinary scholarship published between 2021 and 2025. Using this evidence base, the paper introduces the C-STEER framework, which outlines practical components of collaborative stewardship and maps them to key stages of the AI lifecycle. The synthesis reveals that static ethical principles and top-down regulatory models frequently fail to account for real-world variability, equity concerns and the evolving behavior of systems. Governance approaches that combine legal, technical, organizational and participatory mechanisms, supported by continuous monitoring and local adaptation, are better positioned to build trust, enhance accountability and promote equitable outcomes. By defining collaborative stewardship and presenting the C-STEER framework, this Viewpoint moves beyond compliance-driven governance and offers a practical, context-responsive model for responsible AI integration in health systems.
The HCSAIGF contributes to AI governance research by providing an integrated explanatory architecture and offers a conceptual basis for future empirical research and more coherent governance practices.
An academic adaptation of Patrick Rudolf Dannacher's presentation at the 10th Jakarta Geopolitical Forum 2026 is presented, examining Indonesia's strategic position in the evolving global AI landscape.
The increasing use of artificial intelligence (AI) in higher education has intensified debates on academic ethics, authorship, and institutional governance, particularly in intelligent education environments. Although international guidelines for the responsible use of AI exist, they tend to adopt general normative approaches and show limitations when applied to specific institutional contexts. This study proposes a socio-technical framework for the ethical governance of AI in higher education. Through a systematic review, following PRISMA guidelines, of studies indexed in Scopus and Web of Science (2022–2025), normative gaps are identified and five interdependent dimensions are structured. In addition, an operationalization based on indicative variables and indicators is proposed to support institutional diagnosis. The study concludes that the ethical adoption of AI requires dynamic and context-sensitive models that balance technological innovation, academic integrity, and human oversight. Spanish-language metadata / Metadatos en españolTítulo en español: Gobernanza ética de la inteligencia artificial en la educación superior: una revisión sistemática y una propuesta de marco sociotécnico para entornos educativos inteligentesResumen: El uso creciente de la inteligencia artificial (IA) en la educación superior ha intensificado los debates sobre la ética académica, la autoría y la gobernanza institucional, particularmente en los entornos educativos inteligentes. Aunque existen directrices internacionales para el uso responsable de la IA, estas suelen adoptar enfoques normativos generales y presentan limitaciones cuando se aplican a contextos institucionales específicos. Este estudio propone un marco sociotécnico para la gobernanza ética de la IA en la educación superior. Mediante una revisión sistemática, realizada conforme a las directrices PRISMA, de estudios indexados en Scopus y Web of Science entre 2022 y 2025, se identifican brechas normativas y se estructuran cinco dimensiones interdependientes. Además, se propone una operacionalización basada en variables indicativas e indicadores para apoyar el diagnóstico institucional. El estudio concluye que la adopción ética de la IA requiere modelos dinámicos y sensibles al contexto que equilibren la innovación tecnológica, la integridad académica y la supervisión humana. Palabras Claves: gobernanza ética de la inteligencia artificial; inteligencia artificial en la educación superior; marco sociotécnico; inteligencia artificial responsable; integridad académica; entornos educativos inteligentes; gobernanza institucional de la IA; supervisión humana; revisión sistemática PRISMA; gobernanza de la tecnología educativa; ética de la IA en la educación; diagnóstico institucional. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i4.1298Dimensions.Open Alex.
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