Sadegh-Zadeh, Seyed-Ali, Movahhedi, Tahereh, Hajiyavand, Amir M. and Dearn, Karl D. (2023) Exploring undergraduates’ perceptions of and engagement in an AI-enhanced online course. Frontiers in Education, 8. ISSN 2504-284X
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Abstract or description
In the age of globalization, an internet connection has become essential for enhancing various human activities across the economic, cultural, and defense sectors, among others. This is particularly true for online classrooms. Microsoft Teams, a widely used digital education platform, provides capabilities that allow online teachers to facilitate better interactions and create more effective learning environments in online settings. This study aimed to explore students’ perceptions of synchronous online learning that occurred in an AI-enhanced online course, delivered using MS Teams. As an explorative study that examines the educational intersection of engineering and artificial intelligence, it represents the convergence of these two branches of learning and thus enriches both fields. The research involved 35 online students at the Staffordshire University, with data collected via online questionnaires to gather information about students’ perceptions of online learning through Microsoft Teams. After completing the online course materials, the questionnaires were distributed to students via Google Forms. The data were then descriptively analyzed. The study’s findings revealed that although online learning through Microsoft Teams was a novel experience for the students, the platform’s interactive and engaging learning environment motivated them to participate more actively, ultimately leading to a better comprehension of the course materials. Incorporating AI-enhanced features within the Microsoft Teams platform further augmented the online learning experience, as students appreciated the personalized learning recommendations and real-time feedback, which showcases the synergistic potential of AI and education in the digital age.
Item Type: | Article |
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Faculty: | School of Digital, Technologies and Arts > Computer Science, AI and Robotics |
Depositing User: | Ali SADEGH ZADEH |
Date Deposited: | 06 Feb 2024 16:37 |
Last Modified: | 06 Feb 2024 16:37 |
Related URLs: | |
URI: | https://eprints.staffs.ac.uk/id/eprint/8063 |