scale-validation
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- PaperComputers & Education10 Jul 2026
Educational prompt engineering self-efficacy scale (Ed-PESS): Development and psychometric validation
Fatih Karataş, Recep GÜR, Barış Eriçok, Fatma BAŞARIR et al.
A new scale called the Educational Prompt Engineering Self-Efficacy Scale (Ed-PESS) was developed and psychometrically validated to measure educators' confidence in using prompt engineering for educational purposes.
- PaperAssessing Writing24 Jun 2026
Measuring engagement in technology-supported collaborative argumentative writing: Development and validation of the T-CLEA scale
Li-Jen Wang, Ying‐Tien Wu, Teng-Yao Cheng
This study develops and validates the T-CLEA scale to measure engagement in technology-supported collaborative argumentative writing.
- PaperComputers and Education: Artificial Intelligence19 Jun 2026
Enhancing AI literacy course satisfaction through empowerment in AI problem-solving and ethical awareness: Development and validation of an AI project-based learning scale
Jinyu Zhu, Siu Cheung Kong
The study developed and validated a scale to measure students' perceptions of project-based learning (PBL) experiences in AI literacy courses. Using structural equation modeling with 446 students, it found that empowerment in AI problem-solving and AI ethical awareness mediated the relationship between PBL and course satisfaction. The findings highlight that PBL can enhance AI literacy course satisfaction by fostering student empowerment and ethical awareness.
Original abstract
The use of the project-based learning (PBL) approach in developing AI literacy is widely adopted. However, student satisfaction with AI literacy courses has not been thoroughly explored. This study addresses this gap by examining the associations between students’ perceived PBL, empowerment in using AI for problem-solving, AI ethical awareness, and satisfaction with AI literacy courses. A total of 1,027 secondary and university students from 102 secondary schools and a university in Hong Kong participated in an AI literacy course, among whom 446 provided complete data for the structural equation modelling (SEM) analysis. We developed a context-grounded scale to measure students’ perceptions of the PBL experiences when using AI for problem-solving (AI-PBLS) within AI literacy course. This scale was validated through exploratory and confirmatory factor analyses. A two-step SEM results confirmed that empowerment in using AI for problem-solving and AI ethical awareness mediated the relationship between PBL and their satisfaction with the AI literacy course. This study makes a valuable contribution by introducing a robust scale for researchers to assess students’ perceived PBL experiences the context of AI applications. The findings further highlight the potential of PBL to enhance AI literacy course satisfaction by fostering conditions that empower students in using AI for problem-solving and increase their awareness of AI ethics.