Template-Type: ReDIF-Article 1.0
Author-Name: Ikrame HAI
Author-Email: ikramehai@gmail.com
Author-Workplace-Name: Faculty of Legal, Economic and Social Sciences of Fez, Sidi Mohamed Ben Abdellah University, Fez, Morocco
Author-Name: Mohammed ABDELLAOUI
Author-Email: ikramehai@gmail.com
Author-Workplace-Name: Faculty of Law, Economics and Social Sciences of Fez, Sidi Mohamed Ben Abdellah University, Fez, Morocco
Title: Enhancing Student Engagement in online through Artificial Intel-ligence
Abstract: In the constantly evolving educational landscape, the integration of Artificial Intelligence (AI) has emerged as a transformative force, particularly in the realm of online courses. This article explores the use of AI-based solutions to enhance student engagement in virtual learning environments. By leveraging intelligent algorithms, machine learning, and data analytics, educators can personalize the learning experience, tailor content delivery, and provide timely feedback tailored to individual student needs. The study examines various AI-based strategies designed to foster student engagement, including predictive analytics to identify at-risk students, chatbots for instant support, and adaptive learning platforms for personalized program delivery. Additionally, the article delves into the ethical considerations surrounding the implementation of AI in education, addressing concerns related to privacy, bias, and human-machine interface. Through a comprehensive review of existing literature and case studies, this research aims to provide insights into the effectiveness of AI in promoting active student participation, collaboration, and motivation in online courses. Furthermore, it explores potential challenges and future directions for AI integration in education, emphasizing the importance of a balanced and ethical approach to fully harnessing the technology's potential in developing a more engaging and inclusive learning environment.   Keywords: Artificial Intelligence (IA), Student Engagement, Online Learning, Personalized Learning, Machine Learning, Data Analytics. JEL code:  O10 Paper type: Theoretical Research
Journal: International Journal of Accounting, Finance, Auditing, Management and Economics
Pages: 505-516
Volume: 6
Issue: 3
Year: 2025
File-URL: https://ijafame.org/index.php/ijafame/article/view/1825
File-Format: text/html
File-URL: https://ijafame.org/index.php/ijafame/article/view/1825/1824
File-Format: application/pdf
Handle: RePEc:aoa:journl:v:6:y:2025:i:3:p:505-516