doi: 10.18178/ijiet.2026.16.9.2698
Artificial Intelligence Learning Experiences and Behavioral Intentions: A Causal Analysis of Generation Z Students in Industrial Education Programs
Thamasan Suwanroj1
,
Benchaporn Chanakul2,* ,
Apisan Siripan3 ,
Prachyanun Nilsook4 ,
Punnee Leekitchwatana5 ,
Kanaporn Kaewkamjan6 ,
Orawan Saeung1 ,
Oraphan Amnuaysin1 ,
Sasitorn Issaro1 ,
Thananan Areepong1
- 1Department of Computer Technology and Digital Media, Faculty of Industrial Technology, Nakhon Si Thammarat Rajabhat University, Nakhon Si Thammarat, Thailand
- 2Department of Measurement and Research, Faculty of Education, Nakhon Si Thammarat Rajabhat University, Nakhon Si Thammarat, Thailand
- 3Department of Industrial Art, Faculty of Industrial Technology, Nakhon Si Thammarat Rajabhat University, Nakhon Si Thammarat, Thailand
- 4Department of Information and Communication Technology for Education, Faculty of Industrial Education, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand
- 5Retired Government Official, School of Industrial Education and Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand
- 6Department of Business English, Faculty of Humanities and Social Sciences, Nakhon Si Thammarat Rajabhat University, Nakhon Si Thammarat, Thailand
- Manuscript receivedDecember 12, 2025
- revisedFebruary 5, 2026
- acceptedFebruary 26, 2026
- publishedSeptember 15, 2026
Abstract
Artificial Intelligence (AI) technology has significantly changed education by improving agility, flexibility, and access to knowledge. Students, especially in higher education including those in Industrial Education, engage with AI, influencing their perceptions and behaviors. This research aims to explore the causal relationships between AI learning experiences (Stimulus: S; AI Accuracy Experience: ACC, AI Insight Experience: INS, and AI Interactive Experience: INT) and perceptions, attitudes (Organism: O; Perceived Usefulness of AI: PU, Perceived Ease of Use of AI: PEOU), and Attitude toward AI Use: ATT), and intentions to use AI (Response: R; Behavioral Intention to Use AI: BEH) among Industrial Education students, using the Stimulus-Organism-Response model (SOR model) framework. This study utilized a quantitative methodology with a sample of 300 undergraduate Industrial Education students in Thailand. Data was collected via a 21-item questionnaire using a 5-point scale (IOC = 0.670–1.000; Cronbach’s α = 0.841–0.917) and analyzed using descriptive statistics and Structural Equation Modeling (SEM) in Jamovi version 2.4.1.1. The research findings reveal that Industrial Education students’ intention to use AI (BEH) is directly influenced by their attitude towards AI (ATT), with a coefficient (β) of 0.103*(p < 0.050). Their intention is also indirectly affected by their experience with AI (ACC, INS, INT) through their perceptions (PU, PEOU) and attitude (ATT), with coefficients (β) of 0.106* (p < 0.050) and −0.307**(p < 0.010). The model aligns well with the empirical data. The research indicates that AI experience, perception, and attitudes are interconnected factors influencing the intention to use AI in learning. This emphasizes the need for learning systems that enhance AI experiences, and foster awareness and positive attitudes, ultimately benefiting students in industrial education and beyond. These findings can guide policy and curriculum development in the AI-driven learning and Industry 4.0 contexts.
Keywords
- Artificial Intelligence (AI) learning usage experience
- perceived usefulness of AI
- perceived ease of use of AI
- attitude toward AI use
- behavioral intention to use AI
- Stimulus-Organism-Response (SOR) model
How to Cite
Thamasan Suwanroj, Benchaporn Chanakul, Apisan Siripan, Prachyanun Nilsook, Punnee Leekitchwatana, Kanaporn Kaewkamjan, Orawan Saeung, Oraphan Amnuaysin, Sasitorn Issaro, and Thananan Areepong, "Artificial Intelligence Learning Experiences and Behavioral Intentions: A Causal Analysis of Generation Z Students in Industrial Education Programs," International Journal of Information and Education Technology, vol. 16, no. 9, pp. 2398-2410, 2026. https://doi.org/10.18178/ijiet.2026.16.9.2698
Copyright & License
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).