Adaptive Learning Based on Artificial Intelligence to Overcome Student Academic Inequalities

Abstract

Background. In the context of higher education, academic inequality is a serious obstacle in achieving equitable learning outcomes among students. Factors such as educational background, learning styles, and differences in mastery of material are the main triggers for this inequality. To overcome this challenge, innovative approaches such as adaptive learning based on Artificial Intelligence (AI) have emerged as a potential solution. Purpose. This research aims to investigate the potential of AI-based adaptive learning in overcoming academic inequality among students. By combining AI technology, this research seeks to provide personalized solutions tailored to each student's learning needs. Method. This research uses quantitative methods with a survey model. A total of 20 respondents were selected representatively to provide their views on learning experiences, preferences and views regarding adaptive learning. This survey provides relevant data to understand whether the implementation of AI-based adaptive learning can be considered an effective measure to reduce academic inequality. Results. The research results show that the majority of respondents face difficulties in understanding course material in general. However, most also expressed openness to the use of AI-based adaptive learning. This positive perception can be an indication of the potential success of implementing this technology as a solution to overcome academic inequality. Conclusion. Taking into account the research results, AI-based adaptive learning is promising as a solution that can align the learning needs of individual students. Although implementation challenges remain, this research provides initial impetus for further exploration of the application of AI technologies in achieving academic equity in higher education settings.