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Generative AI as a Reflective Scaffold in a UAV-Based STEM Project: A Mixed-Methods Study on Students' Higher-Order Thinking and Cognitive Transformation

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Title: Generative AI as a Reflective Scaffold in a UAV-Based STEM Project: A Mixed-Methods Study on Students' Higher-Order Thinking and Cognitive Transformation
Language: English
Authors: Shih-Yeh Chen, Wei-Cheng Chen, Chin-Feng Lai (ORCID 0000-0001-7138-0272)
Source: Education and Information Technologies. 2025 30(17):24787-24814.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 28
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Scaffolding (Teaching Technique), Reflection, Artificial Intelligence, STEM Education, Thinking Skills, Cognitive Processes, Engineering Education, College Students, Aviation Technology, Technology Uses in Education
DOI: 10.1007/s10639-025-13758-4
ISSN: 1360-2357
1573-7608
Abstract: In today's knowledge society, where artificial intelligence (AI) technologies are increasingly integrated into educational contexts, designing intelligent learning systems with cognitive scaffolding functions has become a critical issue in learning technology research. Unmanned Aerial Vehicle (UAV) courses, which involve mechanical assembly, sensor integration, data processing, and flight control, present a highly integrated STEM learning task and an ideal context for examining the effectiveness of generative AI-assisted learning. This study employed a quasi-experimental design involving 64 first-year engineering students, who were randomly assigned to either an experimental group or a control group to participate in a six-week UAV-based project-oriented STEM course. The experimental group interacted with a GPT-based system featuring semantic prompts and dynamic feedback to support reflective questioning and strategic adjustment, while the control group engaged in paper-based reflection activities. Independent sample t-tests revealed that students in the experimental group significantly outperformed their counterparts in STEM literacy, levels of reflection, and higher-order thinking indicators. Furthermore, thematic analysis of students' reflection records and AI dialogues demonstrated that the GPT system effectively facilitated verbalized reflection and multi-level cognitive regulation. Students' engagement gradually shifted from operational procedures to conceptually driven strategic understanding. This study confirms the feasibility of AI-supported reflection and provides concrete theoretical and practical implications for the design and evaluation of future intelligent learning systems.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1501366
Database: ERIC
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: In today's knowledge society, where artificial intelligence (AI) technologies are increasingly integrated into educational contexts, designing intelligent learning systems with cognitive scaffolding functions has become a critical issue in learning technology research. Unmanned Aerial Vehicle (UAV) courses, which involve mechanical assembly, sensor integration, data processing, and flight control, present a highly integrated STEM learning task and an ideal context for examining the effectiveness of generative AI-assisted learning. This study employed a quasi-experimental design involving 64 first-year engineering students, who were randomly assigned to either an experimental group or a control group to participate in a six-week UAV-based project-oriented STEM course. The experimental group interacted with a GPT-based system featuring semantic prompts and dynamic feedback to support reflective questioning and strategic adjustment, while the control group engaged in paper-based reflection activities. Independent sample t-tests revealed that students in the experimental group significantly outperformed their counterparts in STEM literacy, levels of reflection, and higher-order thinking indicators. Furthermore, thematic analysis of students' reflection records and AI dialogues demonstrated that the GPT system effectively facilitated verbalized reflection and multi-level cognitive regulation. Students' engagement gradually shifted from operational procedures to conceptually driven strategic understanding. This study confirms the feasibility of AI-supported reflection and provides concrete theoretical and practical implications for the design and evaluation of future intelligent learning systems.
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