Frontiers in Educational Research, 2026, 9(9); doi: 10.25236/FER.2026.090906.
Junxiao Zhu1, Shiyi Wang1, Chenliang Chu1, Yu Liang1, Yongxian Li2, Ping Gao3, Yiyang Liang1, Yingjun Liu1, Liang Qin1, Deyun Ma1
1School of Food and Pharmaceutical Engineering, Zhaoqing University, Zhaoqing, 526061, China
2Guangzhou Juli Pharmaceutical Development Co., Ltd., Guangzhou, 510220, China
3Zhanjiang Institute for Food and Drug Control, Zhanjiang, 524000, China
To address two long-standing problems in engineering group assessment (the difficulty of quantifying individual contributions and the difficulty of identifying social loafing), this study proposes a “scenario-driven” assessment model integrating the Leaderless Group Discussion (LGD) scenario, Bales’ Interaction Process Analysis (IPA) coding, and the Individual Weighting Factor (IWF) algorithm, with AI introduced to improve data collection efficiency. An empirical study was conducted with 84 pharmaceutical engineering students (12 groups of seven) using a high-fidelity Good Manufacturing Practice (GMP) case. Results showed IPA coding reliability of κ = 0.87 and peer rating reliability of ICC = 0.79; the IWF algorithm effectively identified social loafing and significantly improved grade differentiation for low contributors; constructive-conflict groups achieved significantly higher risk-identification coverage than weak-conflict groups (87.3% vs. 52.1%, p < 0.001); speaking frequency correlated only weakly with contribution weight (r = 0.23), whereas being cited by peers correlated strongly (r = 0.76, p < 0.001). The model provides a workable approach to process-based assessment for engineering education accreditation.
Educational Evaluation Reform; Engineering Education; Artificial Intelligence; Leaderless Group Discussion; Process-Based Assessment
Junxiao Zhu, Shiyi Wang, Chenliang Chu, Yu Liang, Yongxian Li, Ping Gao, Yiyang Liang, Yingjun Liu, Liang Qin, Deyun Ma. Scenario-Driven Assessment of Engineering Group Work Integrating AI-Assisted Behavioral Coding and the Individual Weighting Factor Algorithm. Frontiers in Educational Research (2026), Vol. 9, Issue 9: 39-46. https://doi.org/10.25236/FER.2026.090906.
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