Undergraduate students’ experience of the instructional method used in AI course in Bangladesh

Abstract

The growing demand for skilled professionals who can work with Artificial Intelligence (AI) in the Fourth Industrial Revolution (4IR) and the increasing number of universities worldwide offering AI courses to undergraduate students to meet the demand, has made the classroom a primary place where students learn how to develop, maintain and use AI. Therefore, this study aimed to investigate effective instructional methods by exploring undergraduate students’ experience of the instructional method used in AI course in Bangladesh. To conduct this study, 18 participants were selected, where data collected from 13 participants helped the study to reach data saturation level. This data was collected using an In-depth interview which lasted for 25 to 30 minutes. Grounded theory (GT) methodology was used throughout the process of data collection and analysis, where four main stages of GT were used, open coding, axial coding, selective coding, and theory formation. Five interviews were first conducted, analyzed using these steps, then another 5 more interviews were conducted, analyzed following the same steps and comparing the themes and categories with the first 5 interviews. That led to 3 more interviews which after analyzing it, the data was found to be saturated. From this analysis, five theories were developed. These theories emphasized the importance of feedback, hands-on practice, self-paced learning, real-life problem solving, and connection to future education in enhancing students learning outcomes. The findings from this study can inform the design of professional development programs for AI teachers that will enhance their instructional method, leading to better learning outcomes for students