What does this research mean for the field?

Integrating generative AI as a pedagogy-guided scaffold with structured reflection improves problem-solving performance, engagement, and critical reasoning in undergraduate engineering students compared to conventional instruction. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To evaluate whether embedding generative artificial intelligence within a structured pedagogical framework enhances problem-solving performance and student perceptions in undergraduate engineering education.

September 5, 2026Journal of Applied Research in Higher Education

Pedagogy-guided integration of generative AI in undergraduate engineering education

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Authors

CHCheng-Wei Hung PCPen-Chou Chen

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Overview

Quasi-experimental study demonstrates improved problem-solving in engineering undergraduates using structured generative AI, highlighting the need for pedagogical guidance over tool access.

Key Points

To evaluate whether embedding generative artificial intelligence within a structured pedagogical framework enhances problem-solving performance and student perceptions in undergraduate engineering education.
Conducted a quasi-experimental study of 82 undergraduate engineering students in Taiwan, comparing an AI-assisted cohort to a comparison group receiving conventional instruction from a prior semester.
Structured the intervention into four recurring instructional phases: problem introduction, AI-supported exploration, critical reflection, and discussion with feedback.
Evaluated student outcomes using five rubric-graded problem-solving assignments, a post-intervention perception questionnaire administered to the AI cohort, and student reflective writings.
The AI-assisted cohort achieved higher composite performance across the five rubric-assessed problem-solving assignments compared to the conventional instruction cohort.
Descriptive results from the AI-assisted group demonstrated high levels of student engagement, perceived tool usefulness, and self-reported problem-solving confidence.
Qualitative analysis of reflective writing revealed that structured reflection prompted students to detect AI errors, identify limitations, and refine independent reasoning.

Cite This Study

Hung et al. (2026) studied this question.

synapsesocial.com/papers/6a9bd4436b95aff0620ebcb0 https://doi.org/10.1108/jarhe-05-2026-0859

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September 5, 2026Journal of Applied Research in Higher Education

Pedagogy-guided integration of generative AI in undergraduate engineering education

View Full Paper

Ask AI

Bookmark

Authors

CHCheng-Wei Hung PCPen-Chou Chen

Discussion

Member takes

Overview

Quasi-experimental study demonstrates improved problem-solving in engineering undergraduates using structured generative AI, highlighting the need for pedagogical guidance over tool access.

Key Points

To evaluate whether embedding generative artificial intelligence within a structured pedagogical framework enhances problem-solving performance and student perceptions in undergraduate engineering education.
Conducted a quasi-experimental study of 82 undergraduate engineering students in Taiwan, comparing an AI-assisted cohort to a comparison group receiving conventional instruction from a prior semester.
Structured the intervention into four recurring instructional phases: problem introduction, AI-supported exploration, critical reflection, and discussion with feedback.
Evaluated student outcomes using five rubric-graded problem-solving assignments, a post-intervention perception questionnaire administered to the AI cohort, and student reflective writings.
The AI-assisted cohort achieved higher composite performance across the five rubric-assessed problem-solving assignments compared to the conventional instruction cohort.
Descriptive results from the AI-assisted group demonstrated high levels of student engagement, perceived tool usefulness, and self-reported problem-solving confidence.
Qualitative analysis of reflective writing revealed that structured reflection prompted students to detect AI errors, identify limitations, and refine independent reasoning.

Cite This Study

Hung et al. (2026) studied this question.

synapsesocial.com/papers/6a9bd4436b95aff0620ebcb0 https://doi.org/10.1108/jarhe-05-2026-0859

View Full Paper

Ask AI

Bookmark