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A Summary of the Research Methods of Artificial Intelligence in Teaching

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Multimedia Technology and Enhanced Learning (ICMTEL 2023)

Abstract

With modern technology developing rapidly, “Artificial Intelligence” becomes a hot word of the times. The integration of the development of information technology and artificial intelligence provides an opportunity for education optimization. This article briefly reviews the application of artificial intelligence in teaching from four aspects: learning environment creation, learning data analysis, learning resource matching, and learning path intervention. Through the creation of learning environment, it can broaden the learning dimension and help students to learn immersive. Through intelligent analysis such as multimodal data mining and affective computing learning analysis, it can identify students’ emotional feedback for a certain content and help teachers adjust teaching content and progress with strong pertinence. Learning resource matching technology helps to match learning resources according to students’ personality characteristics and appearance differences. Teachers can carry out learning path intervention for different students and help students to adjust their learning paths and consolidate knowledge learning. Some future research directions are proposed for some research methods. This will help relevant researchers to grasp the research in this field as a whole and play an important role in promoting the application and development of artificial intelligence in the field of education.

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References

  1. Lang, J.H.: Medicine in the era of big data and artificial intelligence. Chin. J. Matern. Child Health Res. 30(01), 1–3 (2019)

    Google Scholar 

  2. Jing, J., Huang, X.C.: Current status and future prospects of artificial intelligence-assisted diagnosis and treatment in laboratory medicine. Int. J. Lab. Med. 43(21), 2669–2673 (2022)

    Google Scholar 

  3. Zhang, K., Liu, X., Shen, J., Li, Z., Jiang, Y., et al.: Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell 181(6), 1423-1433.e11 (2020)

    Article  Google Scholar 

  4. Han, X., Gao, X.: Research on AI teaching design for primary schools based on EDIPT model. Educ. Equipment Res. 39(03), 35–39 (2023)

    Google Scholar 

  5. Li, X.C., Wang, F., Meng, J.T.: Application of artificial intelligence technology in supply chain logistics. China Logistics Purchasing 2023(06), 77–78 (2023)

    Google Scholar 

  6. Wang, Y. G., Xu, J. Q., Ding, J. H.: Global framework of education 4.0: future school education and model transformation – interpretation of the world economic forum report “schools of the future: defining new education models for the fourth industrial revolution”. J. Distance Educ. 38(03), 3–14 (2020)

    Google Scholar 

  7. Zhang, G.C.: Case analysis of AR/VR technology in experimental course teaching. Electron Technol. 51(08), 145–147 (2022)

    Google Scholar 

  8. Zhong, Z., Chen, W.D.: Design strategy and case implementation of experiential learning environment based on VR technology. China Audio-Vis. Educ. 2018(02), 51–58 (2018)

    Google Scholar 

  9. Qian, X.L., Song, Z.Y., Cai, Q.: Developing immersive learning in the Metaverse: characteristics, paradigms and practices of immersive learning based on 5G+AR. Educ. Rev. 2022(06), 3–16 (2022)

    Google Scholar 

  10. Meng, Y.: Empowering education intelligence transformation with gigabit optical network and cloud VR. Commun. World 2022(18), 34–35 (2022)

    Google Scholar 

  11. Ruan, B.: VR-assisted environmental education for undergraduates. Adv. Multimed. 2022, 3721301 (2020)

    Google Scholar 

  12. Liu, M., Shan, S., Wang, R., Wu, X., Chen, X.: Learning expressionlets on spatio-temporal manifold for dynamic facial expression recognition. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1749–1756. IEEE Computer Society (2014)

    Google Scholar 

  13. Yao, S., He, N., Zhang, H., Ji, X.: Micro-expression recognition by feature points tracking. In: Proceedings of the 10th International Conference on Communications, pp. 1–4. IEEE Computer Society (2014)

    Google Scholar 

  14. Xu, L.F., Wang, J.Y., Cui, J.N., et al.: Dynamic expression recognition based on dynamic time warping and active appearance model. J. Electron. Inform. Technol. 40(2), 338–345 (2018). https://doi.org/10.11999/JEIT170848

    Article  Google Scholar 

  15. Huang, X., Fu, R.D., Jin, W., et al.: Expression recognition based on image difference and convolutional deep belief network. Optoelectron.·Laser 29(11), 1228–1236 (2018)

    Google Scholar 

  16. Zhang, Z., Chen, T., Meng, H., et al.: SMEConvNet: a convolutional neural network for spotting spontaneous facial micro-expression from long videos. IEEE Access 6, 71143–71151 (2018). https://doi.org/10.1109/ACCESS.2018.2884349

    Article  Google Scholar 

  17. Tran, T.-K., Vo, Q.-N., Hong, X., et al.: Dense prediction for micro-expression spotting based on deep sequence model. Electron. Imag. 2019(8), 1–6 (2019)

    Google Scholar 

  18. Ding, J., Tian, Z., Lyu, X., Wang, Q., Zou, B., Xie, H.: Real-time micro-expression detection in unlabeled long videos using optical flow and LSTM neural network. In: Vento, M., Percannella, G. (eds.) CAIP 2019. LNCS, vol. 11678, pp. 622–634. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29888-3_51

    Chapter  Google Scholar 

  19. Lei, L., Li, J., Chen, T., et al.: A novel graph-TCN with a graph structured representation for micro-expression recognition. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 2237–2245. ACM (2020)

    Google Scholar 

  20. Zhao, X., Ma, H., & Wang, R.: STA-GCN: spatio-temporal AU graph convolution network for facial micro-expression recognition. In: Proceedings of Chinese Conference on Pattern Recognition and Computer Vision, pp. 80–91. Springer, Berlin, Germany (2021)

    Google Scholar 

  21. Wang, S.-J., Yan, W.-J., Li, X., et al.: Micro-expression recognition using color spaces. IEEE Trans. on Image Process. 24(12), 6034–6047 (2015)

    Article  MathSciNet  Google Scholar 

  22. Ren, Z., et al.: LCDAE: data augmented ensemble framework for lung cancer classification. Technolo. Cancer Res. Treat. 21, 153303382211243 (2022). https://doi.org/10.1177/15330338221124372

    Article  Google Scholar 

  23. Zhai, X.S., Xu, J.Q., Wang, Y.G.: Research on emotional computing in online education: based on multi-source data fusion. J. East China Normal Univ. (Educ. Sci.) 40(09), 32–44 (2022)

    Google Scholar 

  24. Hu, W., Wang, Z.L.: Research on intelligent recommendation evaluation of test questions based on collaborative filtering algorithm. J. Qujing Normal Univ. 41(06), 49–54 (2022)

    Google Scholar 

  25. Liu, J.J.: Research on Fusion Recommendation Method Based on Content and Collaborative Filtering. Inner Mongolia Normal University (2019)

    Google Scholar 

  26. Beijing University of Posts and Telecommunications: User Experience Research and Usability Testing. Beijing University of Posts and Telecommunications (2018)

    Google Scholar 

  27. Xiao, X.: Discussion on the application of association rule algorithm in score analysis – taking the test scores of high school students as an example. New Curriculum 16, 60 (2022)

    Google Scholar 

  28. Wang, Y., Zhou, S., Weng, Z., Chen, J.: An intelligent analysis and service design method for user feedback. J. Zhengzhou Univ. (Eng. Edn.) 44(03), 56–61 (2022)

    Google Scholar 

  29. Wang, A., Zhao, Y., Chen, Y.: Information search trail recommendation based on markov chain model and case-based reasoning. Data Inform. Manag. 5(1), 228–241 (2021). https://doi.org/10.2478/dim-2020-0047

    Article  Google Scholar 

  30. Sun, Z.: Ontology-based Research on Resource Organization of Subject Information Gateway. Jiangsu University of Science and Technology (2010)

    Google Scholar 

  31. Ke, H.: Design of real-time data acquisition system for Internet of Things based on hybrid model. Inform. Comput. (Theory Ed.) 34(17), 40–42 (2022)

    Google Scholar 

  32. Yu, M., Liu, J., You, Y., Liu, C.: Evaluation of medium and long-term global flood forecasting based on Bayesian model averaging. Geogr. Sci. 42(09), 1646–1653 (2022)

    Google Scholar 

  33. Han, Y., Huang, R.: Design of educational resource recommendation scheme based on matching effectiveness. Microcomput. Appl. 34(05), 1–4 (2018)

    Google Scholar 

  34. Liang, T., Li, C., Li, H.: Top-k learning resource matching recommendation based on content filtering pagerank. Comput. Eng. 43(02), 220–226 (2017)

    Google Scholar 

  35. Huang, W.: Precise ideological and political exploration in colleges and universities based on student portrait analysis. J. Northeast. Univ. (Soc. Sci. Ed.) 23(03), 104–111 (2021)

    Google Scholar 

  36. Huang, B.: A preliminary study of “stratified teaching” in class groups. Teach.-Friend (02), 3 (1996)

    Google Scholar 

  37. He, L., Zhang, L.: Intervention response mode: a new model of inclusive education in the early years in the United States. J. Suzhou Univ. (Educ. Sci. Ed.) 2(04), 111–118 (2014)

    Google Scholar 

  38. Yang, W., Zhong, S., Zhao, X., Fan, J., Yang, L., Zhong, Z.: Research on the construction of elementary school mathematics learning intervention model based on learning analysis. China Dist. Educ. 04, 125–133 (2022)

    Google Scholar 

  39. Wu, F.: Entering Artificial Intelligence, pp. 200–214. Higher Education Press, Beijing (2022)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Teaching Reform Research Project of Nanjing Normal University of Special Education in 2019 “Research on Cultivation of Professional Core Quality of Normal University Students from the perspective of Professional Certification – Taking Educational Technology Major as an Example”; Universities’ Philosophy and Social Science Researches Project in Jiangsu Province (No. 2020SJA0631 & No. 2019SJA0544).

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Correspondence to Xiaoyan Jiang .

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Ge, H., Zhu, X., Jiang, X. (2024). A Summary of the Research Methods of Artificial Intelligence in Teaching. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 535. Springer, Cham. https://doi.org/10.1007/978-3-031-50580-5_15

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  • DOI: https://doi.org/10.1007/978-3-031-50580-5_15

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