Impact of Social Class on AI Usage by University Students in Lahore

Abstract

The study aims to examine the acceptance and effectiveness of generative AI tools in the field of English language learning (ELL) by university students in Pakistan. Bourdieu's theory is used as the theoretical framework to check the social class of economic, cultural and social capital which are indicators of family income, parental education, school background, possession of a device and institutional support. To achieve the required results the mixed methods design is used which involved 100 students across four universities of Pakistan such as University of Management and Technology (UMT), Punjab University (PU), Government College University (GCU) Lahore and Fatima Jinnah Women University (FJWU) Islamabad and 20 semi structured interviews are conducted in these four universities. The results from the quantitative analysis using SPSS showed that there were statistically significant relationships between income and frequency of using AI and income and perceived usefulness of AI, higher income students used AI more frequently and perceived it as more useful. Economic capital was the strongest predicted factor in the regression analysis of adoption of AI. While in qualitative results the thematical analysis identifies institutional support, cultural perceptions and technological literacy as important moderating factors. The findings together show that the social class has strong effects on access and perception of using AI for language learning purposes. The study contributes in the global debate of equality in AI mediated education by contextualizing the findings within the socio-economic experiences of Pakistan.  It highlights the significance of policies and institutional practices that correspond with the principles of inclusion so that generative AI can be used as a tool to promote, not perpetuate, inequality.