TY - GEN
T1 - AI-Supported Summative Reflection to Foster Motivation and Teamwork in STEM
T2 - 8th International Conference on Innovative Technologies and Learning, ICITL 2025
AU - Lin, Chia Ju
AU - Pedaste, Margus
AU - Huang, Yueh Min
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/105011346152
UR - https://www.scopus.com/pages/publications/105011346152#tab=citedBy
U2 - 10.1007/978-3-031-98185-2_19
DO - 10.1007/978-3-031-98185-2_19
M3 - Conference contribution
AN - SCOPUS:105011346152
SN - 9783031981845
T3 - Lecture Notes in Computer Science
SP - 171
EP - 180
BT - Innovative Technologies and Learning - 8th International Conference, ICITL 2025, Proceedings
A2 - Wang, Wei-Sheng
A2 - Lai, Chin-Feng
A2 - Huang, Yueh-Min
A2 - Sandnes, Frode Eika
A2 - Sandtrø, Tengel Aas
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 5 August 2025 through 7 August 2025
ER -