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Risks of AI Bias and Inequities in Learning
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Author(s): S. Praveenkumar (SRM Institute of Science and Technology, India), Nilesh Anute (Sri Balaji University, India), V. Vaissnave (SRM Institute of Science and Technology, India), T. Ragupathi (SRM Institute of Science and Technology, India), Shyam Fardale (Datta Meghe Institute of Management Studies, India), P. Selvakumar (Department of Science and Humanities, Nehru Institute of Technology, Coimbatore, India)and T. C. Manjunath (Rajarajeswari College of Engineering, India)
Copyright: 2026
Pages: 28
Source title:
Democratizing Education With AI and the Future of Personalized Learning
Source Author(s)/Editor(s): Raed Awashreh (United Arab Emirates University, UAE)
DOI: 10.4018/979-8-3373-2302-2.ch009
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Abstract
As artificial intelligence (AI) systems become increasingly embedded within the education sector, there is growing concern about the issue of AI bias and its far-reaching implications for fairness, equity, and student outcomes. AI applications such as adaptive learning platforms, automated grading systems, student performance prediction models, and admissions algorithms are designed to enhance efficiency, personalize instruction, and support data-driven decision-making. However, these systems are only as unbiased as the data they are trained on and the assumptions embedded in their algorithms. If not carefully designed and audited, AI technologies can perpetuate, or even exacerbate, existing inequalities related to race, gender, socioeconomic status, language proficiency, and ability. This makes the conversation around AI bias in education not just a technical issue but a deeply ethical and social one. Bias can also be introduced during the design and implementation stages of AI development.
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