Modeling the Factors Influencing Technology Students' Intentions to Use AI-Driven Virtual Simulation Apps in Technical Drafting and Design Education
International Journal of Technology and Design Education, (2026)
Milcah R. Mangubat
a
,
Jivulter C. Mangubat
a
,
Larry C. Gantalao
a
,
Jeffrey G. Dela Calzada
a
,
Bernabe C. Lumantas
a
,
Dennis L. Capuyan
a
,
Dharel P. Acut
b
,
Manuel B. Garcia
c,d
a College of Technology, Cebu Technological University, Cebu City, Central Visayas, Philippines
b College of Education, Cebu Technological University, Cebu City, Central Visayas, Philippines
c Educational Innovation and Technology Hub, FEU Institute of Technology, Manila, Philippines
d Graduate School of Education, Korea University, Seoul, South Korea
Abstract: The integration of artificial intelligence (AI) tools into educational settings is reshaping how students learn, create, and engage with digital technologies, particularly in fields such as technology and design education where virtual simulations and AI-assisted workflows are increasingly prevalent. Despite this momentum, student adoption of AI tools remains inconsistent, especially in developing regions where technological readiness and trust in emerging technologies vary widely. This study investigates factors influencing AI tool acceptance among 493 industrial technology students from a public university in Central Visayas, Philippines. Using structural equation modeling (SEM), the research examined the relationships among perceived usefulness, perceived ease of use, perceived risk, and behavioral intention to adopt AI-driven virtual simulation applications. Findings confirm that perceived usefulness and ease of use remain strong drivers of adoption intention, aligning with the Technology Acceptance Model (TAM). However, perceived risk demonstrated a significant negative influence on both perceptions and intentions, highlighting the impact of concerns related to data privacy, algorithmic transparency, and ethical implications of AI in design-oriented learning environments. Integrating perceived risk into TAM expands the explanatory power of the model in technical drafting and design education, offering a more comprehensive picture of how students evaluate and adopt AI-driven virtual simulation apps. This enriched perspective highlights the necessity for institutional strategies that build AI literacy, reinforce data governance mechanisms, and foster responsible engagement with emerging technologies. The results provide valuable insights for educators, curriculum designers, and policymakers working to advance AI-supported learning, particularly within resource-constrained and rapidly developing educational contexts.