IMPROVING STUDENT ENGAGEMENT THROUGH AI-BASED LEARNING TOOLS A QUANTITATIVE STUDY

Abstract

Quick developments in the ways higher education institutions incorporate AI software have completely shifted the discussion from 'whether' it should be used to 'how' making wise decisions about its integration in teaching can be carried out. The student teachers who are completing their B.Ed courses are the ones who will be most deeply affected by this. The habits and methods developed around the time of getting the degree frequently continue to be practiced for the rest of one's professional life in schools. This study mainly focuses on the efficacy of AI-assisted tools in maintaining students' attention to class by studying the eighth semester B.Ed students of the University of Gujrat. The University of Gujrat is a place where the students have highly accessible technology but when it comes to well-thought uses in the classroom the situation is uneven. One hundred and fifty final year B.Ed students filled out a valid 44-question survey based on a rating scale. Besides mere numeric scoring, the research also portrayed interrelations between various aspects that describe the usage of AI-based educational tools. The simplest variable was how much these tools were used. Users' sense of helpfulness was another aspect. The perceived strength of the feedback and the students' lessons being adjusted as their needs were the last two aspects that were considered. As for the measurement of engagement, it was based on the actions taken, mental effort and emotions experienced during learning. Since the connections mattered more than isolated results, correlation tests took the lead in the statistical analysis. Influences among variables were obtained by the help of IBM SPSS program. With the help of regression models, the main determinants of engagement were brought to light. Even small differences across answers offered notable links. They show that there are markedly positive associations between the use of AI-based learning instruments and the engagement of students (r =.63, p <.001), cognitive engagement being the one that has the highest correlation individually (r =.61). The regression results showed that AI tool utilization predicted 41.2% of the variance in student engagement scores (R =.412). The relevant literature and research perspectives on issues related to this paper have been organized per Self-Determination Theory and the Technology Acceptance Model and further, the paper aims to present curriculum designers, faculty and university management of the University of Gujrat with some practical suggestions.