Teaching Towards Sustainable Massive IoT: A Review of Energy-Efficient Computing Approaches

Teaching Towards Sustainable Massive IoT: A Review of Energy-Efficient Computing Approaches

Authors

  • Bernard Dione Université Cheikh Anta Diop de Dakar
  • Grace Temilolu Ikenna University of Education
  • Sangeeth K St Joseph college, Kannur University
  • Subhash John St Joseph college, Kannur University

DOI:

https://doi.org/10.64780/msl.v1i2.180

Keywords:

AI-enabled learning, Next-generation digital platforms, Inclusive education, Accessible education, Sustainable innovation, Higher education, Pedagogical transformation

Abstract

The rapid development of next-generation technologies, particularly those powered by Artificial Intelligence (AI), has transformed higher education, providing new pathways for inclusive and accessible learning. This study systematically reviews the role of AI in reshaping teaching methodologies, focusing on AI-enabled digital platforms such as adaptive learning systems, intelligent tutoring tools, and personalized content delivery. The findings indicate that AI technologies enhance the personalization of education by tailoring learning experiences to individual student needs, while also facilitating real-time feedback, automating assessments, and supporting self-regulated learning. These advancements significantly improve overall learning outcomes and contribute to greater educational accessibility. However, challenges such as data privacy concerns, unequal access to technology, and the need for teacher training remain barriers to broader implementation. Ultimately, this research underscores the importance of integrating AI into higher education to achieve long-term sustainability, inclusivity, and academic excellence, while highlighting the need for further development to address existing obstacles and fully harness the potential of AI in education.

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2025-11-30

How to Cite

Dione, B., Ikenna, G. T., K, S., & John, S. (2025). Teaching Towards Sustainable Massive IoT: A Review of Energy-Efficient Computing Approaches. Management, Sustainability, and Life Span, 1(2), 86–102. https://doi.org/10.64780/msl.v1i2.180

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Vol. 1 No. 2 (2025): November 2025

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