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AI in sustainable higher education: an interpretive structural model and MICMAC approach

Author

Listed:
  • Irene O. Mamites
  • Jose Maria S. Garcia II
  • Gibe S. Tirol
  • Nelson F. Nolon
  • Joy M. Olarte
  • Ulysis D. Malait
  • Melanie M. Himang

Abstract

Integrating artificial intelligence (AI) in sustainable higher education practices prove to be beneficial in the teaching and learning process of institutions. With the many probable practices which promote sustainability in higher education, stakeholders must be able to proactively prioritise practices given the lack of resources for the full-blown implementation of sustainable higher education practices. Along this line, this paper employs interpretive structural modelling (ISM) with MICMAC analysis to generate a framework for stakeholders to use in prioritising such practices. A real-life case study in a state university in the Philippines is conducted to understand how AI is integrated in sustainable higher education. Interestingly, the framework points out data collection monitoring systems as the core practice to be tackled by educational institutions.

Suggested Citation

  • Irene O. Mamites & Jose Maria S. Garcia II & Gibe S. Tirol & Nelson F. Nolon & Joy M. Olarte & Ulysis D. Malait & Melanie M. Himang, 2026. "AI in sustainable higher education: an interpretive structural model and MICMAC approach," International Journal of Business and Globalisation, Inderscience Enterprises Ltd, vol. 42(1), pages 1-22.
    • Handle: RePEc:ids:ijbglo:v:42:y:2026:i:1:p:1-22
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