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The future prospects of deep learning and neural networks: Artificial intelligence's impact on education
- 1 Shijiazhuang Foreign Language Education Group, Shijiazhuang, 050000, China
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Abstract
Artificial Intelligence (AI) has transformed a variety of areas, and education is no exception. With the development of deep learning and neural network, AI is poised to change the way people teach and learn. This paper explores the future prospects of deep learning and neural networks in education, highlighting the potential benefits and challenges they may bring. AI technologies, like deep learning algorithms and neural networks, have the potential to transform education through customized learning experiences, intelligent tutoring, streamlining administrative duties, and facilitating data-based decision making. Enhanced personalized learning helps students to learn at their own pace and in their preferred style, smart tutoring systems offer personalized guidance and support. Automation of administrative tasks increases efficiency and accuracy, while data-driven decision making helps educators make informed choices about students' outcomes. However, the implementation of AI in education poses challenges such as data privacy, equity, and the preservation of the teacher-student relationship. Efforts should be made to address these challenges and fully harness the potential of deep learning and neural networks in education.
Keywords
artificial intelligence, deep learning, neural networks, education
[1]. Prain, V., Cox, P., Deed, C., et al. (2013). Personalisedlearning: lessons to be learnt. British Educational Research Journal 39(4), 654–676.
[2]. Savery, J. R. and Duffy, T. M. (1995). Problem based learning: An in-structional model and its constructivist framework. Educationaltechnology 35(5), 31–38.
[3]. I.B.M. (2007) Virtual Worlds: Real Leaders. Online Games putthe future of buisiness leadership on display.
[4]. de Freitas, S. (2008). Emerging technologies for learning. Becta, Tech. Rep.
[5]. Graesser, A. C., Chipman, P., Haynes, B. C. and Olney, A. (2005). Auto Tutor: An intelligent tutoring system with mixed-initiative dialogue. IEEE Transactions on Education 48(4), 612-618.
[6]. Boyle, E., Connolly, T. M. and Hainey, T. (2011). The role of psychology in understanding the impact of computer games. Entertainment Computing 2(2), 69–74.
[7]. Charles, D. and McAlister, M. (2004). Integrating ideas about invisible playgrounds from play theory into online educational digital games. in Entertainment Computing–ICEC 2004. Springer, 598–601.
[8]. Connolly. T. M., Boyle, E. A., MacArthur, E., et al. (2012). A systematic literature review of empirical evidence on computer games and serious games. Computers & Education 59(2), 661–686.
[9]. Druckman, D., Bjork, R. A., et al. (1992). In the mind’s eye: Enhancing human performance. National Academies Press.
[10]. Hwang, G.-J., Tsai, C.-C., Yang, S. J., et al. (2008). Criteria, strategies and research issues of context-aware ubiquitous learning. Educational Technology & Society 11(2), 81–91.
[11]. C.-M. Chen and Y.-L. Li, “Personalized context-aware ubiquitous learning system for supporting effective English vocabulary learning,” Interactive Learning Environments, vol. 18, no. 4, pp. 341–364, 2010.
[12]. Ogata, H. and Yano, Y. (2004). Knowledge awareness map for computersupported ubiquitous language-learning. in Wireless and Mobile Technologies in Education, 2004. Proceedings. The 2nd IEEE International Workshop on. IEEE, 19–26.
[13]. Wang, T. I., Tsai, K. H., Lee, M.-C. and Chiu, T. K. (2007). Personalized learning objects recommendation based on the semantic-aware discovery and the learner preference pattern. Educational Technology & Society 10(3), 84–105.
[14]. Sharma, R. C., Kawachi, P. and Bozkurt, A. (2019). The landscape of artificial intelligence in open, online and distance education: Promises and concerns. Asian J. Distance Educ. 14(2), 1–2.
[15]. Piech, C., Huang, J., Chen, Z., Do, C., Ng, A. and Koller, D. (2015). Tuned models of peer assessment in MOOCs. Educational Data Mining.
[16]. Koedinger, K. R., Booth, J. L. and Klahr, D. (2012). Instructional complexity and the science to constrain it. Science, 336(6081), 380-385.
[17]. Sleeman, D. H. and Brown, J. S. (Eds.). (1979). Intelligent tutoring systems [Special issue]. International Journal of Man–Machine Studies, 11, 1–3.
[18]. Kahraman, H. T., Sagiroglu, S. and Colak, I. (2010). Development of adaptive and intelligent Web-based educational systems. in Proc. 4th Int. Conf. Appl.Inf. Commun. Technol., 1–5.
[19]. Peredo, R., Canales, A., Menchaca, A. and Peredo, I. (2011). Intelligent Web-based education system for adaptive learning. Expert Syst. Appl. 38(12), 14690–14702.
[20]. Rus, V., D’Mello, S., Hu, X. and Graesser, A. (2013). Recent advances in conversational intelligent tutoring systems. AI Mag 34(3), 42–54.
[21]. Pokrivcakova, S. (2019). Preparing teachers for the application of AI-powered technologies in foreign language education. J. Lang. Cultural Edu. 7(3), 135–153.
[22]. Mikropoulos, T. A. and Natsis, A. (2011). Educational virtual environments: A ten-year review of empirical research (1999–2009). Comput. Edu. 56(3), 769–780.
[23]. Kumar, A. (2020). AI's New Role In Education: Automated Grading. https://elearningindustry.com/artificial-intelligence-new-role-in-education-automated-paper-grading.
Cite this article
Yu,P. (2024). The future prospects of deep learning and neural networks: Artificial intelligence's impact on education. Applied and Computational Engineering,33,94-101.
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Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation
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