This study aims to develop and validate a Question Perception Scale that can diagnose how undergraduate students perceive questioning. Although the importance of asking questions has been increasingly emphasized in the era of AI utilization, existing ...
This study aims to develop and validate a Question Perception Scale that can diagnose how undergraduate students perceive questioning. Although the importance of asking questions has been increasingly emphasized in the era of AI utilization, existing studies have shown limitations in that they have mainly focused on factors that inhibit questioning. To address this research gap, the present study sought to develop a comprehensive perception scale encompassing undergraduates’ motivations, attitudes, and inhibiting factors related to questioning, as well as their question-related skills and attitudes when using AI and their learning motivation.
The rapidly changing digital environment driven by the Fourth Industrial Revolution and AI technologies has transformed not only individuals’ perceptions and attitudes but also instructional methods and communication practices in the classroom. These changes also influence how today’s undergraduates perceive and participate in digital-based question-and-answer activities. With this background, the study aimed to develop a scale based on prior research, focusing on individuals’ metacognition regarding questioning, their ability to ask questions during class, their beliefs about questioning, factors that hinder questioning in class, and their perceptions and attitudes toward AI-assisted questioning.
The developed scale underwent content validity and construct validity testing. Using the final confirmed scale, the study also analyzed the effects of students’ question perception on learning engagement and digital literacy competence.
This study addressed two research questions: (1) What is the validity of the Question Perception Scale for undergraduates? and (2) What are the effects of the Question Perception Scale on learning engagement and digital literacy competence?
To conduct the study, preliminary items were developed based on prior research related to question perception. Two rounds of expert validation were conducted with six professors with more than 15 years of teaching experience, resulting in a total of 66 items. Subsequently, two survey administrations were conducted with undergraduate students at K University. Through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), 39 items across six factors were finalized. In addition, correlation analysis and regression analysis were performed to examine the effects of question perception on learning engagement and digital literacy competence.
The results can be summarized as follows. First, the preliminary 66-item scale yielded six factors through EFA, labeled operationally as Question Attitude, Question Motivation, Question-Inhibiting Factors, AI-Assisted Questioning Skills, AI-Assisted Questioning Attitudes, and AI-Based Learning Question Motivation. CFA results based on the 39-item survey indicated an acceptable level of validity. Second, question perception had a significant effect on learning engagement. Third, question perception also had a significant effect on digital literacy competence. In other words, students who perceived questioning positively and regarded it as meaningful for learning tended to participate more actively in class and demonstrated higher levels of digital literacy competence.
The Question Perception Scale developed in this study is expected to serve as an effective tool for diagnosing undergraduates’ perceptions of questioning, their AI-assisted questioning skills and attitudes, and their AI-based learning competencies. Furthermore, the scale may provide foundational data for creating conditions that encourage active classroom participation through questioning, thereby contributing to improvements in learning engagement and AI literacy.
Because this study was conducted with students at K University located in Gyeongsangnam-do, there are limitations in generalizing the findings. Future research should include diverse undergraduate populations across different contexts, develop diagnostic tools that incorporate perceptions of AI ethics, and conduct longitudinal studies to examine changes in question perception over time.