Abstract: This paper presents a framework for parallel intelligent education that involves physical and virtual learning for a personalized learning experience. We especially focus on Chat Generative Pre-trained Transformer (ChatGPT) owing to its considerable potential to supplement regular class learning. We address the strengths and weaknesses of learning with ChatGPT. Finally, we discuss the challenges and solutions of the proposed parallel intelligent education with ChatGPT.

[1]Anders BA, 2023. Is using ChatGPT cheating, plagiarism, both, neither, or forward thinking?Patterns, 4(3):100694.

[2]Brown TB, Mann B, Ryder N, et al., 2020. Language models are few-shot learners. Advances in Neural Information Processing Systems, p.1877-1901.

[3]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.

[4]Ethridge EA, Branscomb KR, 2009. Learning through action: parallel learning processes in children and adults. Teach Teach Educ, 25(3):400-408.

[5]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.

[6]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.

[7]Hare R, Tang Y, 2022. Player modeling and adaptation methods within adaptive serious games. IEEE Trans Comput Soc Syst, early access.

[8]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.

[9]Hu B, Wang JC, 2020. Deep learning based hand gesture recognition and UAV flight controls. Int J Autom Comput, 17(1):17-29.

[10]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.

[11]Liang J, Hare R, Chang TY, et al., 2022. Student modeling and analysis in adaptive instructional systems. IEEE Access, 10:59359-59372.

[12]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.

[13]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.

[14]Nižnan J, Pelánek R, Rihák J, 2015. Student models for prior knowledge estimation. Proc 8^th Int Conf on Educational Data Mining.

[15]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].

[16]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].

[17]Reidsema C, Kavanagh L, Hadgraft R, et al., 2017. The Flipped Classroom: Practice and Practices in Higher Education. Springer, Singapore.

[18]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].

[19]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.

[20]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.

[21]Vaswani A, Shazeer N, Parmar N, et al., 2017. Attention is all you need. Advances in Neural Information Processing Systems, p.6000-6010.

[22]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.

[23]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.

[24]Zhou J, Ke P, Qiu X, et al., 2023. ChatGPT: potential, prospects, and limitations. Front Inform Technol Electron Eng, early access.