Enhancing Learning in Data Analytics through AI Tutoring Systems: A Student-Centered Evaluation

This study explores the implementation and effectiveness of Artificial Intelligence (AI)-based tutoring tools in a higher education data analytics course. As AI becomes increasingly integrated into education, it is important to examine how students interact with such tools to ensure meaningful and ethical integration that supports learning. This research addresses the need to understand student perceptions, motivation, and concerns regarding AI tutors, particularly in technical subjects like data analytics. A structured quantitative methodology was employed, involving undergraduate students enrolled in a data analytics course at a Malaysian university named as Universiti Teknologi PETRONAS. Participants completed a 29-item survey instrument aligned with four research objectives. Results showed that students generally viewed AI tools positively describing them as helpful, user-friendly, and supportive of deeper learning. High agreement was recorded in areas such as usability, motivation, and content alignment, with many students reporting improved understanding and confidence in handling analytical tasks. Correlation heatmaps revealed consistent attitudes and experiences, while concerns such as overreliance and algorithmic transparency were more varied. Ethical issues including data privacy and equitable access were noted as ongoing challenges. Overall, this study contributes evidence that AI-based tutors can enhance student engagement and learning outcomes, provided their use is guided by ethical safeguards, student agency, and pedagogical alignment.