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All Journal International Journal of Evaluation and Research in Education (IJERE)
Mingsiritham, Kemmanat
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Education

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Determinants of AI adoption for authentic assessment in open university systems Mingsiritham, Kemmanat; Chanyawudhiwan, Gan
International Journal of Evaluation and Research in Education (IJERE) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v15i1.36368

Abstract

Artificial intelligence (AI) is transforming higher education through personalized learning and innovative assessment methods. This study explores the factors influencing AI adoption for authentic assessment in open and distance learning environments. Using a survey of 185 instructors, an integrated framework based on the theory of planned behavior (TPB) and the technology acceptance model (TAM) was tested via structural equation modeling (SEM). Key constructs included attitude toward the behavior (ATT), subjective norm (SN), perceived behavioral control (PBC), self-efficacy (SE), and barriers to AI adoption (BAA), with intention to use AI (INT) and actual adoption behavior (AAB) as outcomes. Results showed that SE, ATT, PBC, and SN positively influenced INT, which in turn strongly predicted AAB. In addition, BAA had no significant effect on INT but showed a negative impact on AAB. The model demonstrated good fit and explained substantial variance (R²=0.746 for INT; R²=0.649 for AAB). These findings highlight the importance of enhancing instructors’ confidence, control, and institutional support while reducing perceived barriers. Strategic investments in training, infrastructure, and leadership support are crucial to advancing AI-enabled authentic assessment in higher education.
Fostering critical AI competency: a structural equation model of pre-service teachers’ trust and actual AI use in higher education Paiwithayasiritham, Chaiyos; Mingsiritham, Kemmanat; Sinthaworn, Waraporn; Busabong, Chularat
International Journal of Evaluation and Research in Education (IJERE) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v15i2.38385

Abstract

In recent years, education has experienced rapid change due to the rise of artificial intelligence (AI). This study analyzed how twelve interconnected factors, based on technology acceptance and trust theories, influence trust in AI for learning (TL) and actual AI use (AU) among pre-service teachers in Thailand. Using a quantitative design, with data collected from 260 pre-service teachers through purposive sampling based on prior AI experience. A 60-item, 5-point Likert-scale questionnaire, validated through pilot testing and internal consistency analysis (α=0.82–0.91). Data was analyzed using structural equation modeling (SEM) with maximum likelihood estimation. The model showed very good fit (χ²/df=1.601, root mean square error of approximation (RMSEA)=0.049) and explained 90.80% of behavioral intention (BI) and 71.20% of AU. Results indicated that cognitive load regulation (CLR) was the strongest predictor of TL (β=0.786, p<0.001), while responsible AI awareness (RAA) also showed positive effect. In contrast, AI self-efficacy (ASE) in a negative way (β=-0.159, p=0.009). The primary predictor of AU was BI (β=0.884, p<0.001). These findings highlight the importance of AI education systems, which will reduce teachers’ cognitive load and contribute to an improved ethical AI literacy in teacher training institutions.
Co-Authors Busabong, Chularat Chanyawudhiwan, Gan Paiwithayasiritham, Chaiyos Sinthaworn, Waraporn