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.

Keywords

Algorithmic Bias, Decolonial Pedagogy, AI in Education, Digital Inequality, Indian Higher Education, Critical Data Studies,

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References

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