Integrating Adaptive Intelligent Tutoring Systems into Private Higher Educational Institutes

The importance of tutoring in higher education is significant, aiming to offer students a personalized and coaching approach to learning. While literature indicates that students grasp and retain information better through diverse learning styles, adapting tutorials to individual preferences is often impractical for educators with face-to-face human tutors. In response to the widespread adoption of technology, educational institutions are integrating artificial intelligence (AI) into teaching, exemplified by adaptive Intelligent Tutoring Systems (ITS). These adaptive ITS act as personalized tutors, focusing on individual learning needs and preferences, thereby enhancing the overall learning experience. Despite numerous ITS developments, there is limited evidence of systems tailored to students' cognitive and learning styles across general subjects. This study addresses that gap, exploring how students absorb information more effectively when presented in their preferred manner, aligning with the Cognitive Fit Theory. Employing a quantitative approach, the study utilized online questionnaires to gather data on students' learning styles, cognitive preferences, and perceptions of ITSs. The analysis involved descriptive and inferential statistics based on the collected and normalized data. Based on the results obtained, many students stated that being taught with their learning style would enhance their learning experience. Hence, many students would prefer to learn based on their specific learning and cognitive styles. Also, most students agreed that using adaptive intelligent tutoring systems will improve their academic performance.