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STEM for All: An Equity-Oriented AI Personalization Framework for Inclusive Cognitive Tutoring
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Author(s): Amir Mahmood (Zhejiang Normal University, China), Nadia Rehman (Fatimah Jinnah Women University, Pakistan), Misbah Shafait Abbasi (Zhejiang Normal University, China), Mahnoor Masood Qureshi (Northwestern Polytechnical University, China), Aiman Batool (University of Punjab, Pakistan)and Kidega Charles (Zhejiang Normal University, China)
Copyright: 2026
Pages: 26
Source title:
AI-Powered Cognitive Tutors and the New Frontier of Personalized Learning
Source Author(s)/Editor(s): Samra Maqbool (Beijing Normal University, China), Hafiz Muhammad Ihsan Zafeer (Zhejiang Normal University, China)and Ayesha Tariq (The Islamia University of Bahawalpur, Pakistan)
DOI: 10.4018/979-8-3373-4217-7.ch015
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Abstract
This chapter introduces the Equity-Oriented AI Personalization Framework (EAPF) as a guide for integrating AI-powered cognitive tutors into STEM education through an equity-centered approach. While AI personalization enables individualized learning, it often overlooks sociocultural and structural inequities that shape access and outcomes for marginalized learners. Grounded in Universal Design for Learning, Culturally Relevant Pedagogy, Zone of Proximal Development, and Equity Literacy, the EAPF highlights four dimensions, Access, Representation, Adaptivity, and Agency, supported by Bias Mitigation and Teacher Integration as critical enablers. Through illustrative scenarios, the chapter demonstrates how equity can be embedded in AI tutor design, content, and feedback systems. It offers practical insights for educators, developers, and policymakers, reframing personalization as a justice-driven process that aligns AI tools with the diverse needs of STEM learners, particularly in under-resourced and linguistically diverse contexts.
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