TY - GEN
T1 - MAS-CPS Assessor
T2 - Poster papers and late breaking results, workshops and tutorials, practitioners, industry and policy track, doctoral consortium, blue sky and wideAIED papers presented at the 26th International Conference on Artificial Intelligence in Education, AIED 2025
AU - Chen, Yu
AU - Bao, Jiaqi
AU - He, Yutong
AU - Wu, Bian
AU - Hu, Yiling
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - In the context of intelligent technologies empowering learning environments, cultivating students’ collaborative problem-solving (CPS) skills through effective teamwork to address complex tasks has become an integral component of the 21st-century core competencies framework. However, the diagnosis of CPS skills, as the primary step in personalized development, faces numerous challenges. Traditional “human-to-human” assessment methods lack sample independence, while “human-to-computer” assessment methods, although overcoming this limitation, suffer from issues of unrealistic scenario construction. To address this challenge, this study focuses on the assessment of CPS skills in a human-to-computer collaborative setting, leveraging the superior performance of generative AI in text understanding and reasoning. Based on a multi-agent technology framework and using the PISA 2015 assessment approach as a foundation, this study constructs an agent collaboration environment powered by generative AI technology to simulate highly realistic human-to-human collaborative scenarios. This method aims to accurately evaluate students’ actual levels of collaborative problem-solving skills. From the perspectives of the construction and implementation of multi-agent collaborative scenarios and the measurement of CPS skills, this study provides new ideas and implementation pathways for educational assessment empowered by generative AI.
AB - In the context of intelligent technologies empowering learning environments, cultivating students’ collaborative problem-solving (CPS) skills through effective teamwork to address complex tasks has become an integral component of the 21st-century core competencies framework. However, the diagnosis of CPS skills, as the primary step in personalized development, faces numerous challenges. Traditional “human-to-human” assessment methods lack sample independence, while “human-to-computer” assessment methods, although overcoming this limitation, suffer from issues of unrealistic scenario construction. To address this challenge, this study focuses on the assessment of CPS skills in a human-to-computer collaborative setting, leveraging the superior performance of generative AI in text understanding and reasoning. Based on a multi-agent technology framework and using the PISA 2015 assessment approach as a foundation, this study constructs an agent collaboration environment powered by generative AI technology to simulate highly realistic human-to-human collaborative scenarios. This method aims to accurately evaluate students’ actual levels of collaborative problem-solving skills. From the perspectives of the construction and implementation of multi-agent collaborative scenarios and the measurement of CPS skills, this study provides new ideas and implementation pathways for educational assessment empowered by generative AI.
KW - Collaborative Problem-Solving
KW - Educational Assessment
KW - Large Language Model
KW - Multi-Agent
UR - https://www.scopus.com/pages/publications/105012426366
U2 - 10.1007/978-3-031-99261-2_24
DO - 10.1007/978-3-031-99261-2_24
M3 - 会议稿件
AN - SCOPUS:105012426366
SN - 9783031992605
T3 - Communications in Computer and Information Science
SP - 269
EP - 282
BT - Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium, Blue Sky, and WideAIED - 26th International Conference, AIED 2025, Proceedings
A2 - Cristea, Alexandra I.
A2 - Walker, Erin
A2 - Lu, Yu
A2 - Santos, Olga C.
A2 - Isotani, Seiji
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 22 July 2025 through 26 July 2025
ER -