This study aims to examine the impact of AI-based adaptive learning systems (ABALS) on reducing educational disparities, particularly those associated with socioeconomic status and gender, inside higher education institutions in Pakistan. The aim is to investigate how these technologies improve equal access to education through personalized learning experiences and identify the practical and operational challenges associated with their implementation.
Data were gathered using a quantitative research technique from a sample of 384 university students in Pakistan who were chosen by purposive sampling and given a structured questionnaire. Statistical analysis was performed by using SPSS version 27.0, and the instrument’s reliability was validated by a pilot test and Cronbach’s alpha coefficient.
The results demonstrate a significant beneficial relationship between the use of ABALS and a reduction of socioeconomic and gender inequalities in higher education. The systems exhibited a modest association with socioeconomic equality (r = 0.331, p = 0.002) and a more robust link with gender parity (r = 0.511, p < 0.001). The results indicate that ABALS may significantly alleviate structural inequities in schooling when implemented properly.
This research highlights the importance of incorporating AI-based educational solutions in national education policy, especially in marginalized areas. It advocates for investments in digital infrastructure, educator training and inclusive system design to guarantee that ABALS can provide fair outcomes for varied student demographics. The results can assist higher education institutions and policymakers in formulating strategies that enhance resource allocation and promote inclusive, student-focused learning environments.
This paper fills a significant research gap by offering actual data on the effects of ABALS in Pakistan’s rural and underprivileged higher education institutions. This study adds new perspectives on how AI may be used to enhance fairness in educational settings with limited resources because the majority of previous research has focused on technologically advanced contexts.
