Explainable AI in Education and Human-AI Collaboration: New Frameworks for Interactive Intelligence
A mixed-methods study of 103 undergraduate computer engineering students in Thailand found broadly favorable acceptance of AI automation tooling — specifically the low-code platform n8n — across six established technology acceptance constructs. The research used TAM/UTAUT survey instruments alongside qualitative feedback gathered across three workshops, finding that perceived usefulness (Performance Expectancy) was the strongest driver while enjoyment (Hedonic Motivation) was the weakest. The findings suggest AI automation tools are ready for curricular integration, though a vocal minority of students expressed skepticism about output reliability.
Researchers conducted a cross-sectional, mixed-methods study examining how undergraduate computer engineering students in Thailand (n = 103) perceive and accept AI automation tooling, using the open-source workflow platform n8n across three identically structured workshops. A 12-item Likert survey mapped to six TAM/UTAUT constructs — Performance Expectancy, Effort Expectancy, Behavioral Intention, Self-Efficacy, Hedonic Motivation, and Output Quality — was analyzed alongside open-ended qualitative feedback. All six constructs showed favorable acceptance with large effect sizes, with Performance Expectancy (perceived usefulness) scoring highest and Hedonic Motivation (enjoyment) scoring lowest. A notable methodological finding was that the standard sub-facets of TAM/UTAUT collapsed into a single general acceptance factor in this short-form, post-workshop context, raising questions about the granularity of these frameworks in brief educational interventions. While quantitative and qualitative results largely aligned on usefulness and enthusiasm, they diverged on output quality: a small but clearly articulate group of students expressed skepticism about the reliability of AI-generated outputs. The authors recommend three instructional strategies — scaffolded instruction sequencing, self-efficacy supports, and trust-calibration interventions — to guide future curricular adoption of AI automation tools in computing education.
What's missing
The study is limited to a single cultural and institutional context (Thailand), a small convenience sample (n = 103), and a cross-sectional post-workshop design, which prevents causal inference or generalization to other regions or educational systems. The paper does not report long-term retention of acceptance attitudes or whether tool adoption persisted beyond the workshops. The collapse of TAM/UTAUT sub-constructs into a single factor may reflect the brevity of the instrument or the workshop format rather than a genuine theoretical finding, and replication with longer-term or multi-institution designs is needed.
What different sources said
- arXiv cs.AICenter
Examining the Usage of Generative AI Models in Student Learning Activities for Software Programming
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