Case Study: Postgraduate Students’ Class Engagement in Various Online Learning Contexts When Taking Privacy Issues to Incorporate with Artificial Intelligence Applications

Abstract

Artificial Intelligence (AI) has transformed the Education sector. It made it possible for academic institutions to personalize content according to students’ individual needs and improve administrative tasks such as grading assignments. This has increased efficiency in teaching and learning but has also raised relevant concerns about data privacy issues. Researchers have pointed out the potential hindrance of this concern to the further development and implementation of AI technology in Education. In this research project, the authors conducted a mixed method to investigate the above issue by assessing students’ class engagement in various online learning contexts when considering AI privacy issues or not. The first part of this project presented a quantitative approach (quasi-experimental design) while this paper focused on the qualitative approach (interviews) conducted with the same group of 99 students from the postgraduate school via Zoom. Individual student interviews were conducted with randomly chosen 9 students from the two experimental groups in phase one of this research, and thematic analysis was used to analyze the relevant data based on the 4-factor theoretical framework (skills, emotion, participation, and performance) from The Online Student Engagement Scale (OSE). The study discovered that the majority of the students regarded the privacy consent taken into consideration when implementing AI applications in an online learning context had enhanced their class engagement. In addition, the findings indicated that students’ emotions and participation engagements increased the most out of the four OSE factors.