THE INFLUENCE OF CHATBOT AI ON COLLABORATIVE LEARNING AND PEER INTERACTIONS AMONG ENGINEERING STUDENTS

Aims: This study aims to investigate the influence of Chatbot AI on collaborative learning and peer interactions among engineering students. It focuses on understanding how Chatbot AI can enhance student engagement, communication, and collaborative problem-solving in engineering education. Study design: A quantitative, descriptive-correlational research design was employed. The study utilized a stratified random sampling technique to select 172 engineering students, gathering data through a self-administered questionnaire. The study assessed the frequency, functionality, and personalization of Chatbot AI and examined its impact on collaborative learning and peer interactions. Methodology: Data were analyzed using descriptive statistics and correlation analysis. Ethical considerations, including informed consent, anonymity, and confidentiality, were strictly adhered to. The study investigated three key areas: Chatbot AI integration, collaborative learning, and peer interactions. Results: Findings revealed that most students frequently used Chatbot AI for academic purposes, with a high level of agreement regarding its functionality and personalization. The study showed moderate levels of peer collaboration and student satisfaction with collaborative learning. Regression analysis indicated that while frequency and functionality of Chatbot AI positively influenced collaboration, its personalization feature was the most significant factor, accounting for 63% of the variance in collaborative learning and peer interactions. Conclusion: The study concludes that Chatbot AI significantly enhances collaborative learning and peer interactions, particularly through its personalized features. The integration of Chatbot AI is effective in supporting academic needs and promoting peer collaboration. However, there is still room for improvement in the frequency of collaboration and the quality of peer interactions. Future research should focus on further enhancing AI personalization and exploring its broader applications in collaborative learning across disciplines.