Journal of Teaching Innovation and Reform

Vol. 2 (2026)
Articles

Designing AI-Based Adaptive Learning Architectures for Inclusive and Culturally Responsive STEM Education

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  • Wai Nwe Kyaw,
  • Khar Thoe Ng
Wai Nwe Kyaw
INTI International University (IIU), Persiaran Perdana BBN, Putra Nilai, 71800, Nilai, Negeri Sembilan, Malaysia
Khar Thoe Ng
INTI International University (IIU), Persiaran Perdana BBN, Putra Nilai, 71800, Nilai, Negeri Sembilan, Malaysia

Published 2026-06-23

Keywords

  • Artificial intelligence,
  • Inclusive education,
  • Culturally sustaining pedagogy,
  • Educational equity,
  • Educational Ecosystems

How to Cite

Designing AI-Based Adaptive Learning Architectures for Inclusive and Culturally Responsive STEM Education. (2026). Journal of Teaching Innovation and Reform, 2, 105-119. https://doi.org/10.65638/2978-5634.2026.2.09

Abstract

The utilization of Artificial Intelligence (AI) is progressively revolutionizing pedagogical methodologies by providing adaptive, individualized, and easily accessible learning prospects for a wide range of learners. Nonetheless, numerous AI-driven educational frameworks persist in adhering to standardized methodologies that inadequately cater to issues of cultural diversity, fairness, and inclusivity. This article delves into the potential of AI-facilitated education in fostering the establishment of culturally sustaining and inclusive learning environments that bolster fair participation and meaningful learning encounters for all students. By drawing upon theories of inclusive education, culturally sustaining pedagogy, and technology-enhanced learning, this theoretical exposition advocates for a model that integrates AI technologies with culturally responsive and inclusive teaching strategies. The model accentuates features such as accessibility, provision of multilingual support, adaptive scaffolding, recognition of learner diversity, and student-centric engagement to preserve learners' cultural identities while advancing academic success and self-regulated learning. Moreover, the article delves into the functionalities of AI tools like intelligent tutoring systems, personalized feedback mechanisms, learning analytics, and speech recognition technologies in supporting students with varied linguistic, cultural, and educational requisites within inclusive educational settings. Furthermore, the research scrutinizes obstacles linked with the implementation of AI, encompassing issues of algorithmic partiality, digital disparity, ethical quandaries, and educator preparedness. It posits that efficacious AI-driven education necessitates cooperative efforts among educators, policymakers, communities, and technology innovators to ensure equitable and culturally responsive learning milieus. By proffering a culturally sustaining viewpoint on the amalgamation of AI and offering guidance for crafting future-proof inclusive educational frameworks rooted in diversity, accessibility, and social equity, this article enriches ongoing dialogues on inclusive and fair education.

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