AI-Automation Tooling in Computer Engineering Education: Mixed-Methods TAM/UTAUT Evidence for a General Acceptance Attitude

2026-06-12 · Source: cs.AI updates on arXiv.org · Field: Education & Learning — Educational Technology (EdTech), Academic Research & Higher Education, Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A mixed-methods, cross-sectional study investigated undergraduate computer engineering students' acceptance of AI automation tooling, specifically using the open-source platform n8n, across three workshops in Thailand involving 103 participants. The research employed a 12-item Likert instrument mapped to six TAM/UTAUT constructs (Performance Expectancy, Effort Expectancy, Behavioral Intention, Self-Efficacy, Hedonic Motivation, Output Quality) alongside qualitative thematic analysis. Findings indicated favorable acceptance across all constructs with large effect sizes, with Performance Expectancy being the strongest predictor and Hedonic Motivation the weakest. Methodologically, canonical TAM/UTAUT sub-facets collapsed into a single general acceptance factor. Qualitative data largely aligned on usefulness and enthusiasm but revealed a minority skeptical about output reliability. The study supports integrating AI automation tooling into computing curricula and identifies three instructional levers: instruction-sequencing scaffolds, self-efficacy supports, and trust-calibration interventions.

Key takeaway

For computer engineering educators considering AI automation tooling, this study provides strong evidence of student acceptance, particularly regarding perceived usefulness. You should integrate tools like n8n into your curriculum, focusing on instruction-sequencing scaffolds, self-efficacy supports, and trust-calibration interventions to address reliability concerns and maximize engagement. This approach will prepare your students for routine software practice.

Key insights

Undergraduate computer engineering students generally accept AI automation tools, with performance expectancy being a strong driver.

Principles

• TAM/UTAUT constructs can collapse into a single acceptance factor in short-form contexts.
• Performance Expectancy strongly predicts AI automation tool acceptance.
• Instructional levers include sequencing, self-efficacy, and trust calibration.

Method

A mixed-methods, cross-sectional study used a 12-item Likert instrument (TAM/UTAUT constructs) and thematic analysis of open-ended feedback to assess tool acceptance.

In practice

• Adopt AI automation tooling like n8n in computing curricula.
• Implement instruction-sequencing scaffolds for new tools.
• Provide self-efficacy supports to enhance tool engagement.

Topics

• AI Automation Tooling
• Computer Engineering Education
• n8n Platform
• TAM/UTAUT Model
• Student Acceptance
• Curriculum Development

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.