n8n Study: Students Accept AI Automation Tools in Engineering Education

A new study by Aung Pyae, published on arXiv, investigates whether undergraduate computer engineering students accept AI automation tooling in their education. Using the open-source platform n8n as a representative tool, the researcher ran three identical workshops across Thailand with 103 participants. The mixed-methods approach combined a 12-item Likert survey mapped to six TAM/UTAUT constructs (Performance Expectancy, Effort Expectancy, Behavioral Intention, Self-Efficacy, Hedonic Motivation, and Output Quality) with open-ended qualitative feedback.

The quantitative analysis employed ordinal reliability estimation, bootstrap confidence intervals, non-parametric tests, multiple-comparison-controlled correlations, polychoric dimensionality diagnostics, and common-method-bias checks. Results showed favorable acceptance across all six constructs with large effect sizes. Performance Expectancy emerged as the strongest driver, while Hedonic Motivation was the weakest. Notably, dimensionality diagnostics revealed that all six sub-facets collapsed into a single general acceptance factor in this short-form post-workshop context.

Qualitative themes converged with the quantitative profile on usefulness and enthusiasm but diverged on output quality, revealing a small yet articulate minority skeptical about tool reliability. The findings support the curricular adoption of AI automation tooling in undergraduate computing education and identify three instructional levers: instruction-sequencing scaffolds, self-efficacy supports, and trust-calibration interventions.