Enhancing Student Engagement and Performance with Artificial Intelligence
Keywords:
Artificial Intelligence, Student Engagement, Academic Performance, Adaptive Learning, Intelligent Tutoring Systems, Educational Technology, Personalized Learning, Learning AnalyticsAbstract
The topic of focus of this research is on the application of Artificial Intelligence (AI) in learning institutions to facilitate students’ engagement and effectiveness. Using both the survey and ratings with a cross-sectional study design, the investigation includes quantitative data on academic profile, learning activities, and tests in combination with the qualitative data obtained through questionnaires and focus group. The target sample is 300 participants, undergraduate students from a large university in different categories using stratified technique. The AI tools used in the study are the Adaptive Learning Systems, Intelligent Tutoring Systems, Learning Analytics Platforms, and Chatbots. The results show an improvement of the students’ engagement and academic performance scores from 65% to 80% and 70% to 85% when AI is used respectively. Significantly, an empirical relationship between the two is high and positive (r = 0.89) which shows that both engagement and performance are related. Also, the approval ratings of students about the AI tools are high based on the satisfaction questionnaires, special attention being paid to the aspects of convenience and efficiency. Usage analytics show continuous usage of AI platforms for the entire semester at a very systematic level whereas performance analytics show the maximum betterment in Adaptive Learning Systems and Intelligent Tutoring Systems. The feedback process containing AI as part of the feedback delivery system is shown to be highly effective with the majority of feedback to be considered as highly effective. To the knowledge of the author, this research adds new insights to the literature by presenting possible directions in utilizing AI in education, specifically focusing on recommendation systems and learning environments. The study provides various implications that would be useful to educators, policymakers, and designers of technologies seeking ways of integrating AI effectively into various learning settings to enhance teaching-learning processes and improve learning outcomes and equity.