PataLLM: personalized automated test assembly with educational knowledge graphs via reinforcement learning induced large language models

Yanan XIAO , Lu JIANG , Xiaoxia LI , Minghao YIN

Front. Comput. Sci. ›› 2027, Vol. 21 ›› Issue (1) : 2101312

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Front. Comput. Sci. ›› 2027, Vol. 21 ›› Issue (1) :2101312 DOI: 10.1007/s11704-025-51068-7
Artificial Intelligence
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PataLLM: personalized automated test assembly with educational knowledge graphs via reinforcement learning induced large language models
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Yanan XIAO, Lu JIANG, Xiaoxia LI, Minghao YIN. PataLLM: personalized automated test assembly with educational knowledge graphs via reinforcement learning induced large language models. Front. Comput. Sci., 2027, 21(1): 2101312 DOI:10.1007/s11704-025-51068-7

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References

[1]

Fuchimoto K, Minato S I, Ueno M . Automated parallel test forms assembly using zero-suppressed binary decision diagrams. IEEE Access, 2023, 11: 112804–112813

[2]

Liu Y, Zhang T, Wang X, Yu G, Li T . New development of cognitive diagnosis models. Frontiers of Computer Science, 2023, 17( 1): 171604

[3]

Chen X, Hu Z, Sun Y. Fuzzy logic based logical query answering on knowledge graphs. In: Proceedings of the 36th AAAI Conference on Artificial Intelligence. 2022, 3939−3948

[4]

Belov D I . Uniform test assembly: concepts, problems, solvers, and applications for adaptive testing. Journal of Computerized Adaptive Testing, 2017, 5( 1): 1–21

[5]

Miyandoab S Z, Rahnamayan S, Bidgoli A A. Compact NSGA-II for multi-objective feature selection. In: Proceedings of 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2023, 3868−3875

[6]

Ling H, Wang Z, Wang J. Learning to stop cut generation for efficient mixed-integer linear programming. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence. 2024, 20759−20767

[7]

Zhang Y . A novel multi-objective deep Q-network: addressing immediate and delayed rewards in multi-objective Q-learning. IEEE Access, 2024, 12: 144932–144949

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