Abstract
This study explores the integration of artificial intelligence (AI) in reflective learning processes within STEM education. Based on the 6E+R learning model, an AI-supported summative reflection tool was implemented in a collaborative problem-solving (CPS) activity to examine its effects on students’ learning motivation, teamwork competency, and reflective content. A total of 28 high school students participated in a one-day Micro:bit autonomous vehicle workshop and were randomly assigned to either an experimental or control group. The experimental group used an AI-powered reflection tool that employed large language models (LLMs) to automatically generate structured summaries and feedback, while the control group engaged in traditional reflThis is to inform you that corresponding author has been identified as per the information available in the Copyright form.ection by manually recording and reviewing their learning experiences. Results indicated that the experimental group significantly outperformed the control group in both learAs Per Springer style, both city and country names must be present in the affiliations. Accordingly, we have inserted the city names in all affiliations. Please check and confirm if the inserted city names are correct. If not, please provide us with the correct city names.ning motivation and teamwork competency, with medium effect sizes. Topic modeling using BERTopic revealed that the experimental group's reflections focused more on strategy adjustment, logical reasoning, and conceptual understanding, whereas the control group's reflections were more descriptive and operational. These findings suggest that AI-supported structured feedback can enhance the quality of student reflections, increase learning engagement, and improve collaborative interactions. This study demonstrates the practical value and effectiveness of AI-assisted reflection within the 6E+R instructional model, offering new directions for reflective design in STEM collaborative learning and expanding the potential of generative AI in educational settings.
| Original language | English |
|---|---|
| Title of host publication | Innovative Technologies and Learning - 8th International Conference, ICITL 2025, Proceedings |
| Editors | Wei-Sheng Wang, Chin-Feng Lai, Yueh-Min Huang, Frode Eika Sandnes, Tengel Aas Sandtrø |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 171-180 |
| Number of pages | 10 |
| ISBN (Print) | 9783031981845 |
| DOIs | |
| Publication status | Published - 2026 |
| Event | 8th International Conference on Innovative Technologies and Learning, ICITL 2025 - Oslo, Norway Duration: 2025 Aug 5 → 2025 Aug 7 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Volume | 15913 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 8th International Conference on Innovative Technologies and Learning, ICITL 2025 |
|---|---|
| Country/Territory | Norway |
| City | Oslo |
| Period | 25-08-05 → 25-08-07 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 4 Quality Education
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- General Computer Science
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