Sep 2026· Journal of Applied Research in Higher Education· 0 citations· 20 references
TL;DR
The findings suggest that the educational value of generative AI depends on pedagogical orchestration, guided prompting and reflection rather than simple access to the tool.
Abstract
This study examines whether a pedagogy-guided, artificial intelligence (AI)-assisted learning environment can improve learning performance and student perceptions in undergraduate engineering education. Rather than positioning generative AI as an answer-generating tool, the study frames it as a scaffold embedded within a structured teaching process.
A quasi-experimental design was implemented in an undergraduate engineering course in Taiwan. Eighty-two students participated across an AI-assisted group and a comparison group receiving conventional instruction. The intervention followed four recurring stages: problem introduction, AI-supported exploration, critical reflection and discussion with feedback. Quantitative evidence was drawn from five rubric-assessed problem-solving assignments and a post-course questionnaire administered to the AI-assisted cohort, while qualitative evidence came from students' reflective writing. Because the questionnaire was administered only to the AI-assisted cohort, its results are reported descriptively rather than as between-group effects.
The AI-assisted cohort showed higher composite assignment performance and reported strong engagement, perceived usefulness and problem-solving confidence. Qualitative findings showed that structured reflection helped students question AI-generated responses, identify limitations and refine their own reasoning rather than accept outputs uncritically.
As the comparison group came from a previous semester, assignments were scored by the course instructor and prior AI exposure was not measured systematically, the findings should be interpreted as promising exploratory evidence rather than definitive causal proof.
The findings suggest that the educational value of generative AI depends on pedagogical orchestration, guided prompting and reflection rather than simple access to the tool.
The paper offers a classroom-tested model for integrating generative AI into higher engineering education in a pedagogically meaningful and ethically responsible way.
It is suggested that perceived learning usefulness remains relevant in mandatory AI-integration contexts and Pedagogical scaffolding—including prompt literacy, verification practices, and reflective documentation—provides a structured framework for guided and responsible use of generative AI tools in higher education.
Emese Belényesi, M. Korpics, Tamás Méhes et al.· Trends in Higher Education· 0 citations
It is demonstrated that generative artificial intelligence effectively supports critical thinking and classroom engagement when used as guided instructional scaffolding rather than as a provider of final answers.
Herman Herman, Muh. Nasir, A. Putra et al.· Journal La Edusci· 0 citations
A ten-step teaching framework for AI-supported creative interactive content design is proposed, aimed at fostering pedagogical innovation while preserving critical thinking, creativity, and student authorship.
Belén Mainer, Ana Pérez-Escoda· Education sciences· 0 citations
Investigation of the impact of integrating GenAI tools on student learning in the second-year engineering design course at the University of Prince Edward Island finds that students most frequently used GenAI for brainstorming, problem definition, and concept generation, while strongly engaging with ethical verificatio...
K. Grewal, Mikkayla Ellsworth-Reid, Prabhnoor Sigh et al.· Proceedings of the Canadian...· 0 citations
The findings unveil that the chatbot-supported cohort exhibited marked improvements in overall lesson planning, with notable gains in crafting worked examples and posing probing questions, alongside significant advancements in pedagogical knowledge, and a robust boost in teaching self-efficacy.
A. Abukhanova, Bibigul Almukhambetova, A. Mamekova et al.· Frontiers in Education· 0 citations
The principal contribution of the study lies in conceptualizing systematic role rotation as a pedagogical mechanism for structuring critical human–AI interaction in professional learning.
Zh.т. Kenzhebayeva, B. Matayev, E. Sarsembayeva et al.· Frontiers in Education· 0 citations
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