Date
Publisher
arXiv
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 mapped to six TAM/UTAUT constructs - Performance Expectancy (PE), Effort Expectancy (EE), Behavioral Intention (BI), Self-Efficacy (SE), Hedonic Motivation (HM), and Output Quality (OQ) - was complemented by inductive thematic analysis of open-ended feedback. Analyses combined ordinal reliability estimation, bootstrap confidence intervals, non-parametric tests, multiple-comparison-controlled correlations, polychoric dimensionality diagnostics, a common-method-bias check, and between-session comparisons. Acceptance was favorable across all six constructs with large effect sizes, with PE emerging as the strongest construct and HM as the weakest. Dimensionality diagnostics further revealed that canonical TAM/UTAUT sub-facets collapsed into a single general acceptance factor in this short-form post-workshop context, a finding with important methodological and theoretical implications. Qualitative themes converged with the quantitative profile regarding usefulness and enthusiasm but diverged on output quality, revealing a small yet articulate reliability-skeptical minority. The findings support the curricular adoption of AI automation tooling in undergraduate computing education and identify three theory-grounded instructional levers: instruction-sequencing scaffolds, self-efficacy supports, and trust-calibration interventions.
Study at a glance
- Question: How do undergraduate computer engineering students accept and perceive AI-automation tooling after hands-on instructional workshops?
- Setting and data: Thai university, 103 undergraduate computer engineering students across three workshops, March to April 2026.
- Method: Mixed methods combining a cross-sectional post-workshop Likert survey and inductive thematic analysis of open-ended feedback.
Why it matters
As generative AI and low-code platforms reshape software practice, empirical evidence remains sparse on whether undergraduate engineering students will accept these AI-automation tools as useful, usable, and worth sustained engagement.
Key findings
- Students reported broadly favorable acceptance across all six surveyed constructs, with Performance Expectancy rated highest (mean 4.28) and Hedonic Motivation lowest (mean 4.00).
- Dimensionality diagnostics revealed that individual acceptance sub-facets collapsed into a single general acceptance attitude rather than separable dimensions in this short-form post-workshop setting.
- While qualitative feedback largely matched survey enthusiasm regarding usefulness, student comments diverged on output quality by highlighting inconsistency and inaccurate results from automated prompts.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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