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Artificial Intelligence in Education: Origin, Development and Rise

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Intelligent Robotics and Applications (ICIRA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13016))

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Abstract

In recent years, artificial intelligence in education has grown in popularity. When it comes to a new trend for primary school, how to use the AI teacher for course teaching is the main challenge. In this paper, a review of artificial intelligence in education under origin, development and rise is considered. Firstly, we use the method of qualitative research to investigate the problem. Then, the development process and challenges for AI education are discussed in detail. Finally, the rise of artificial intelligence in education is summarized to promote the sustainable development of educational AI teachers.

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References

  1. Adamson, D., Dyke, G., Jang, H., Rosé, C.P.: Towards an agile approach to adapting dynamic collaboration support to student needs. Int. J. Artif. Intell. Educ. 24(1), 92–124 (2014). https://doi.org/10.1007/s40593-013-0012-6

    Article  Google Scholar 

  2. Chi, M.T., Wylie, R.: The ICAP framework: linking cognitive engagement to active learning outcomes. Educ. Psychol. 49(4), 219–243 (2014)

    Article  Google Scholar 

  3. Cumming, G.: Artificial intelligence in education: an exploration. J. Comput. Assist. Learn. 14(4), 251–259 (2008)

    Article  MathSciNet  Google Scholar 

  4. Dotter, A., Chaboyer, B., Jevremovi, D., Kostov, V., Ferguson, A.: The Dartmouth stellar evolution database. Astrophys. J. Suppl. Ser. 178(1), 89 (2008)

    Article  Google Scholar 

  5. Driscoll, M.: Psychology of learning for instruction. Educ. Tech. Res. Dev. 53(1), 108–110 (2005). https://doi.org/10.1007/BF02504860

    Article  MathSciNet  Google Scholar 

  6. Good, T.L., Brophy, J.E.: Contemporary Educational Psychology. Longman/Addison Wesley Longman (1995)

    Google Scholar 

  7. Holmes, W., Bialik, M., Fadel, C.: Artificial intelligence in education. Center for Curriculum Redesign, Boston (2019)

    Google Scholar 

  8. Khachatryan, G.A., et al.: Reasoning mind genie 2: an intelligent tutoring system as a vehicle for international transfer of instructional methods in mathematics. Int. J. Artif. Intell. Educ. 24(3), 333–382 (2014). https://doi.org/10.1007/s40593-014-0019-7

    Article  Google Scholar 

  9. Leelawong, K., Biswas, G.: Designing learning by teaching agents: the Betty’s Brain system. Int. J. Artif. Intell. Educ. 18(3), 181–208 (2008)

    Google Scholar 

  10. Lenat, D.B., Durlach, P.J.: Reinforcing math knowledge by immersing students in a simulated learning-by-teaching experience. Int. J. Artif. Intell. Educ. 24(3), 216–250 (2014). https://doi.org/10.1007/s40593-014-0016-x

    Article  Google Scholar 

  11. Li, J., Qin, H., Wang, J., Li, J.: OpenStreetMap-based autonomous navigation for the four wheel-legged robot via 3D-lidar and CCD camera. IEEE Trans. Ind. Electron. (2021)

    Google Scholar 

  12. Li, J., Wang, J., Peng, H., Hu, Y., Su, H.: Fuzzy-torque approximation enhanced sliding mode control for lateral stability of mobile robot. IEEE Trans. Syst. Man Cybern. Syst. (2021)

    Google Scholar 

  13. Li, J., Wang, J., Peng, H., Zhang, L., Hu, Y., Su, H.: Neural fuzzy approximation enhanced autonomous tracking control of the wheel-legged robot under uncertain physical interaction. Neurocomputing 410, 342–353 (2020)

    Article  Google Scholar 

  14. Li, J., et al.: Parallel structure of six wheel-legged robot trajectory tracking control with heavy payload under uncertain physical interaction. Assem. Autom. 40(5), 675–687 (2020)

    Article  Google Scholar 

  15. Li, J., Zhang, X., Li, J., Liu, Y., Wang, J.: Building and optimization of 3D semantic map based on lidar and camera fusion. Neurocomputing 409, 394–407 (2020)

    Article  Google Scholar 

  16. Li, X., Zheng, L.: Smart media use and its impact on after-school reading among primary and middle school students. China Educ. Technol. 12(1), 88–95 (2018)

    MathSciNet  Google Scholar 

  17. Ma, W., Adesope, O.O., Nesbit, J.C., Liu, Q.: Intelligent tutoring systems and learning outcomes: a meta-analysis. J. Educ. Psychol. 106(4), 901 (2014)

    Article  Google Scholar 

  18. Nesbit, J., Adesope, O., Liu, Q., Ma, W.: How effective are intelligent tutoring systems in computer science education? In: IEEE International Conference on Advanced Learning Technologies, pp. 99–103. IEEE (2014)

    Google Scholar 

  19. Peng, G., Yang, C., He, W., Chen, C.P.: Force sensorless admittance control with neural learning for robots with actuator saturation. IEEE Trans. Ind. Electron. 67(4), 3138–3148 (2019)

    Article  Google Scholar 

  20. Robertson, J., Cross, B., Macleod, H., Wiemer-Hastings, P.: Children’s interactions with animated agents in an intelligent tutoring system. Int. J. Artif. Intell. Educ. 14(3), 335–357 (2004)

    Google Scholar 

  21. Roll, I., Aleven, V., McLaren, B.M., Koedinger, K.R.: Improving students’ help-seeking skills using metacognitive feedback in an intelligent tutoring system. Learn. Instr. 21(2), 267–280 (2011)

    Article  Google Scholar 

  22. Shute, V.J.: Stealth assessment in computer-based games to support learning. Comput. Games Instr. 55(2), 503–524 (2011)

    Google Scholar 

  23. Steenbergen-Hu, S., Cooper, H.: A meta-analysis of the effectiveness of intelligent tutoring systems on college students’ academic learning. J. Educ. Psychol. 106(2), 331 (2014)

    Article  Google Scholar 

  24. Sung, Y., Liao, C., Chang, T., Chen, C., Chang, K.: The effect of online summary assessment and feedback system on the summary writing on 6th graders: the LSA-based technique. Comput. Educ. 95, 1–18 (2016)

    Article  Google Scholar 

  25. Yang, C., Chen, C., He, W., Cui, R., Li, Z.: Robot learning system based on adaptive neural control and dynamic movement primitives. IEEE Trans. Neural Netw. Learn. Syst. 30(3), 777–787 (2018)

    Article  MathSciNet  Google Scholar 

  26. Yang, C., Jiang, Y., Li, Z., He, W., Su, C.-Y.: Neural control of bimanual robots with guaranteed global stability and motion precision. IEEE Trans. Ind. Inf. 13(3), 1162–1171 (2016)

    Article  Google Scholar 

  27. Yang, C., Luo, J., Liu, C., Li, M., Dai, S.-L.: Haptics electromyography perception and learning enhanced intelligence for teleoperated robot. IEEE Trans. Autom. Sci. Eng. 16(4), 1512–1521 (2018)

    Article  Google Scholar 

  28. Yoo, J., Kim, J.: Can online discussion participation predict group project performance? Investigating the roles of linguistic features and participation patterns. Int. J. Artif. Intell. Educ. 24(1), 8–32 (2014). https://doi.org/10.1007/s40593-013-0010-8

    Article  Google Scholar 

  29. Zhang, Z., Chen, T., Wang, M., Zheng, L.: An exponential-type anti-noise varying-gain network for solving disturbed time-varying inversion systems. IEEE Trans. Neural Netw. Learn. Syst. 31(9), 3414–3427 (2020)

    Article  MathSciNet  Google Scholar 

  30. Zhang, Z., Yan, Z.: An adaptive fuzzy recurrent neural network for solving the nonrepetitive motion problem of redundant robot manipulators. IEEE Trans. Fuzzy Syst. 28(4), 684–691 (2020)

    Article  Google Scholar 

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Correspondence to Fuhai Sun .

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Ye, R., Sun, F., Li, J. (2021). Artificial Intelligence in Education: Origin, Development and Rise. In: Liu, XJ., Nie, Z., Yu, J., Xie, F., Song, R. (eds) Intelligent Robotics and Applications. ICIRA 2021. Lecture Notes in Computer Science(), vol 13016. Springer, Cham. https://doi.org/10.1007/978-3-030-89092-6_49

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  • DOI: https://doi.org/10.1007/978-3-030-89092-6_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89091-9

  • Online ISBN: 978-3-030-89092-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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