AI-Automation Tooling in Computer Engineering Education: Mixed-Methods TAM/UTAUT Evidence for a General Acceptance Attitude
arXiv:2606.12424v1 Announce Type: cross Abstract: As generative AI and low-code workflow platforms become routine in software practice, a key educational question is whether the next generation of computer engineers will accept these tools as useful, usable, and worthy of sustained engagement. This paper reports a mixed-methods, cross-sectional study of undergraduate computer engineering students' acceptance of AI automation tooling, instantiated through the open-source platform n8n across three identically scripted workshops in Thailand (n = 103). A 12-item, five-point Likert instrument mappe
The proliferation of generative AI and low-code platforms makes understanding academic acceptance crucial for integrating these tools into future engineering practices.
This study provides early empirical evidence on how the next generation of computer engineers perceives AI automation tools, which will shape future curriculum and industry adoption.
The findings suggest a general acceptance attitude among computer engineering students toward AI automation, indicating a smoother integration pathway into the curriculum and professional workflow.
- · AI automation tool developers
- · Computer engineering educators
- · Students adopting AI tools
- · Software industry
- · Traditional software development methods
- · Engineering programs resistant to AI integration
Increased pressure on educational institutions to integrate AI automation tools into their computer engineering curricula.
Accelerated adoption of AI and low-code workflows in the software development industry as new graduates enter the workforce.
A potential shortage of educators and learning materials proficient in teaching cutting-edge AI automation, leading to a new niche market for educational content and training.
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Read at arXiv cs.AI