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References
Anders BA, 2023. Is using ChatGPT cheating, plagiarism, both, neither, or forward thinking? Patterns, 4(3):100694. https://doi.org/10.1016/j.patter.2023.100694
Brown TB, Mann B, Ryder N, et al., 2020. Language models are few-shot learners. Advances in Neural Information Processing Systems, p.1877–1901.
Burns TR, Machado N, Corte U, 2015. The sociology of creativity: Part I: theory: the social mechanisms of innovation and creative developments in selectivity environments. Human Syst Manag, 34(3):179–199. https://doi.org/10.3233/HSM-150839
Ethridge EA, Branscomb KR, 2009. Learning through action: parallel learning processes in children and adults. Teach Teach Educ, 25(3):400–408. https://doi.org/10.1016/j.tate.2008.09.004
Franzwa C, Tang Y, Johnson A, et al., 2014. Balancing fun and learning in a serious game design. Int J Game-Based Learn, 4(4):37–57. https://doi.org/10.4018/ijgbl.2014100103
Gu JL, Wang JC, Guo XW, et al., 2023. A metaverse-based teaching building evacuation training system with deep reinforcement learning. IEEE Trans Syst Man Cybern Syst, 53(4):2209–2219. https://doi.org/10.1109/TSMC.2022.3231299
Hare R, Tang Y, 2022. Player modeling and adaptation methods within adaptive serious games. IEEE Trans Comput Soc Syst, early access. https://doi.org/10.1109/TCSS.2022.3203926
Hare R, Tang Y, 2023. Hierarchical deep reinforcement learning with experience sharing for metaverse in education. IEEE Trans Syst Man Cybern Syst, 53(4):2047–2055. https://doi.org/10.1109/TSMC.2022.3227919
Hu B, Wang JC, 2020. Deep learning based hand gesture recognition and UAV flight controls. Int J Autom Comput, 17(1):17–29. https://doi.org/10.1007/s11633-019-1194-7
Li L, Lin YL, Zheng NN, et al., 2017. Parallel learning: a perspective and a framework. IEEE/CAA J Autom Sin, 4(3): 389–395. https://doi.org/10.1109/JAS.2017.7510493
Liang J, Hare R, Chang TY, et al., 2022. Student modeling and analysis in adaptive instructional systems. IEEE Access, 10:59359–59372. https://doi.org/10.1109/ACCESS.2022.3178744
Liang J, Tang Y, Hare R, et al., 2023. A learning-embedded attributed Petri net to optimize student learning in a serious game. IEEE Trans Comput Soc Syst, 10(3):869–877. https://doi.org/10.1109/TCSS.2021.3132355
Molenaar I, de Mooij S, Azevedo R, et al., 2023. Measuring self-regulated learning and the role of AI: five years of research using multimodal multichannel data. Comput Hum Behav, 139:107540. https://doi.org/10.1016/j.chb.2022.107540
Nižnan J, Pelánek R, Rihák J, 2015. Student models for prior knowledge estimation. Proc 8th Int Conf on Educational Data Mining.
Radford A, Narasimhan K, Salimans T, et al., 2018. Improving Language Understanding by Generative Pre-Training. https://paperswithcode.com/paper/improving-language-understanding-by [Accessed on Mar. 11, 2023].
Radford A, Wu J, Child R, et al., 2019. Language Models Are Unsupervised Multitask Learners. https://paperswithcode.com/paper/language-models-are-unsupervised-multitask [Accessed on Mar. 11, 2023].
Reidsema C, Kavanagh L, Hadgraft R, et al., 2017. The Flipped Classroom: Practice and Practices in Higher Education. Springer, Singapore. https://doi.org/10.1007/978-981-10-3413-8
Schulman J, Zoph B, Kim C, et al., 2022. ChatGPT: Optimizing Language Models for Dialogue. https://openai.com/blog/chatgpt [Accessed on Mar. 11, 2023].
Shi HR, Liu GJ, Zhang KW, et al., 2023. MARL Sim2real transfer: merging physical reality with digital virtuality in metaverse. IEEE Trans Syst Man Cybern Syst, 53(4):2107–2117. https://doi.org/10.1109/TSMC.2022.3229213
Tang Y, Liang J, Hare R, et al., 2020. A personalized learning system for parallel intelligent education. IEEE Trans Comput Soc Syst, 7(2):352–361. https://doi.org/10.1109/TCSS.2020.2965198
Vaswani A, Shazeer N, Parmar N, et al., 2017. Attention is all you need. Advances in Neural Information Processing Systems, p.6000–6010.
Zhang WJ, Wang JC, Lan FP, 2021. Dynamic hand gesture recognition based on short-term sampling neural networks. IEEE/CAA J Autom Sin, 8(1):110–120. https://doi.org/10.1109/JAS.2020.1003465
Zhang YZ, Sun SQ, Galley M, et al., 2020. DIALOGPT: large-scale generative pre-training for conversational response generation. Proc 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, p.270–278. https://doi.org/10.18653/v1/2020.acl-demos.30
Zhou J, Ke P, Qiu X, et al., 2023. ChatGPT: potential, prospects, and limitations. Front Inform Technol Electron Eng, early access. https://doi.org/10.1631/FITEE.2300089
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Jiacun WANG, Ying TANG, and Ryan HARE drafted the paper. Fei-Yue WANG revised and finalized the paper.
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This paper is to be included in a special feature for which Ying TANG is a guest editor. Fei-Yue WANG is an editorial board member of Frontiers of Information Technology & Electronic Engineering. Ying TANG and Fei-Yue WANG were not involved with the peer review process of this paper. Jiacun WANG, Ying TANG, Ryan HARE, and Fei-Yue WANG declare that they have no conflict of interest.
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Wang, J., Tang, Y., Hare, R. et al. Parallel intelligent education with ChatGPT. Front Inform Technol Electron Eng 25, 12–18 (2024). https://doi.org/10.1631/FITEE.2300166
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DOI: https://doi.org/10.1631/FITEE.2300166