Decolonizing the Digital Classroom: A Critical Analysis of Power, Privilege, and Algorithmic Bias in AI-Mediated Learning Environments

Suraj Begum R; Saravana Mahesan S; Jiang Min; Divya D; Thennarasu Sakkan

doi:10.54392/ajir25417

Authors

Suraj Begum R Department of Science and Humanities, Sri Krishna College of Engineering and Technology, Coimbatore-641008, Tamil Nadu, India Author
Saravana Mahesan S MBA Department, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Vellanur, Avadi, Chennai- 600062, India Author
Jiang Min Faculty of Education, Shinawatra University, Thailand Author
Divya D Department of Information Technology, KPR Institute of Engineering and Technology, Tamil Nadu 641407, India. Author
Thennarasu Sakkan Department of Linguistics, Central University of Kerala, Tejaswini Hills, Periye (P.O.), Kasaragod - 671316, Kerala, India Author

DOI:

https://doi.org/10.54392/ajir25417

Keywords:

Algorithmic Bias, Algorithmic BiasAI in Education, Digital Inequality, Indian Higher Education, Critical Data Studies

Abstract

The increasing use of Artificial Intelligence (AI) in education is a concern because these technologies often strengthen the very colonial power structures they are meant to challenge. The impact of AI-based learning systems on Indian university students' experiences was examined in detail in our study. We employed a critical framework that integrated critical data studies, critical pedagogy, and postcolonial theory. In order to collect our data, 113 students from a variety of institutional, linguistic, and socioeconomic backgrounds participated in a cross-sectional survey that was guided by Community-Based Participatory Action Research (CPAR). Using logistic regression and chi-square tests, our analysis revealed distinct patterns of algorithmic bias. One significant discovery was the pervasive linguistic marginalization: more than half of the participants (53.10%) stated that their mother tongue influence or accent prevented AI from correctly identifying them. This problem was significantly worse for students who speak tribal languages (χ²=18.43, p<0.001) and for first-generation learners (χ²=12.67, p<0.01). Additionally, we found a significant cultural mismatch. Only about one-third of the students (35.41%) felt that AI accurately reflected Indian contexts, while a large majority (53.09%) felt the content was dominated by Western perspectives. The frequency of surveillance-related harm was also high: 60.16% of students reported discomfort during AI proctoring, and SC/ST students reported misrecognition rates that were 2.3 times higher (OR=2.34, 95% CI: 1.45-3.78, p<0.001). Students from distant learning programs and government institutions experienced more algorithmic bias (χ²=15.82, p<0.01). Students used linguistic self-censorship (85%), avoiding cultural examples when interacting with AI (68%), and selectively disengaging from AI (55%) as resistance tactics. Results demonstrate that educational AI cannot be considered neutral if epistemic, cultural and sociotechnical inequality are not taken into consideration. There is a need for decolonial AI frameworks that prioritize community governance, multilingual representation, culturally sustaining pedagogy, and algorithmic transparency.

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