AI-Powered Arabic Language Education in the Era of Society 5.0

Muhammad Rehan Anwar; Herdi Aziz Ahyarudin

doi:10.34306/itsdi.v5i1.607

Authors

Muhammad Rehan Anwar University of Agriculture Faisalabad (UAF)
Herdi Aziz Ahyarudin Gunadarma University

DOI:

https://doi.org/10.34306/itsdi.v5i1.607

Keywords:

Learning Media, Artificial Intelligence, Education, Digital Education

Abstract

In the Society 5.0 era characterized by technological integration, Arabic language education is experiencing a transformation through the application of artificial intelligence (AI). The background to this research is the increasing need for Arabic language skills amidst globalization and the growth of cross-cultural interactions. Meanwhile, AI technology has reached a level of maturity that allows its effective use in education, providing new opportunities to improve language teaching methods. This research aims to explore the potential of using AI in Arabic language teaching, understanding how this technology can facilitate more adaptive, interactive and efficient learning, according to individual student needs. The implications of this research are very significant. The implementation of AI in Arabic education not only improves language skills, but also enriches students' learning experience. In Society 5.0, where humans and technology synergize, the use of AI in Arabic language education can be a model for integrating intelligent technology in the learning process. The main contribution of this research is the development of an AI learning system that can be adopted by educational institutions, enabling them to provide more personalized and effective Arabic language education. This research also explores updates in teaching methods, introducing an approach based on individual student responses. Thus, this research provides an in-depth understanding of how AI can be adapted to each student's learning needs in the context of Arabic language education. Overall, this research not only provides new insights into the integration of AI in Arabic education, but also provides a foundation for the development of responsive and adaptive curricula in the Society 5.0 era.

Downloads

Download data is not yet available.

References

R. Calafato, “Learning Arabic in Scandinavia: Motivation, metacognition, and autonomy,” Lingua, vol. 246, p. 102943, 2020.

M. Al Rajab, S. Odeh, S. Hazboun, and E. Alheeh, “AI-Powered Smart Book: Enhancing Arabic Education in Palestine with Augmented Reality,” in International Symposium on Ambient Intelligence, Springer, 2023, pp. 167–178.

B. Rawat, A. S. Bist, N. Mehra, M. F. Fazri, and Y. A. Terah, “Study of Kumaon Language for Natural Language Processing in End-to-End Conversation Scenario,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 3, no. 2, pp. 143–149, 2022.

S. A. Almelhes, “A Review of Artificial Intelligence Adoption in Second-Language Learning,” Theory and Practice in Language Studies, vol. 13, no. 5, pp. 1259–1269, 2023.

Y. Jiang, X. Li, H. Luo, S. Yin, and O. Kaynak, “Quo vadis artificial intelligence?,” Discover Artificial Intelligence. Springer, 2022. doi: 10.1007/s44163-022-00022-8.

M. Lin and Y. Zhao, “Artificial intelligence-empowered resource management for future wireless communications: A survey,” China Communications, 2020, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9058606/

A. K. Upadhyay and K. Khandelwal, “Artificial intelligence-based training learning from application,” Development and Learning in …, 2019, doi: 10.1108/DLO-05-2018-0058.

A. Carvalho, A. Levitt, S. Levitt, E. Khaddam, and ..., “Off-the-shelf artificial intelligence technologies for sentiment and emotion analysis: a tutorial on using IBM natural language processing,” … Information Systems, 2019, [Online]. Available: https://aisel.aisnet.org/cais/vol44/iss1/43/

K. Elliott, R. Price, P. Shaw, T. Spiliotopoulos, M. Ng, and ..., “Towards an equitable digital society: artificial intelligence (AI) and corporate digital responsibility (CDR),” Society. Springer, 2021. doi: 10.1007/s12115-021-00594-8.

M. M. M. Peeters, J. van Diggelen, K. Van Den Bosch, and ..., “Hybrid collective intelligence in a human–AI society,” AI Soc, 2021, doi: 10.1007/s00146-020-01005-y.

J. J. Bryson and A. Theodorou, “How society can maintain human-centric artificial intelligence,” Human-centered digitalization and services, 2019, doi: 10.1007/978-981-13-7725-9_16.

J. Chen, H. Lu, H. Zhou, and Y. Zhou, “Exploration on Curriculum Teaching Based on OBE and AI,” in 2019 10th International Conference on Information Technology in Medicine and Education (ITME), IEEE, 2019, pp. 385–389.

L. Li, N. Wang, and S. Tang, “OBE-Based Reform for Software Project Management Curriculum,” in 2019 14th International Conference on Computer Science & Education (ICCSE), IEEE, 2019, pp. 1075–1079.

C. Lukita, S. Suwandi, E. P. Harahap, U. Rahardja, and C. Nas, “Curriculum 4.0: Adoption of Industry Era 4.0 as Assessment of Higher Education Quality,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 14, no. 3, pp. 297–308, 2020.

B. Tao, V. Díaz, and Y. Guerra, “Artificial intelligence and education, challenges and disadvantages for the teacher,” Arctic Journal. researchgate.net, 2019. [Online]. Available: https://www.researchgate.net/profile/Vianney-Perez/publication/338236746_2019_7212_30_ARTIFICIAL_INTELLIGENCE_AND_EDUCATION_Challenges_and_disadvantages_for_the_teacher_1/links/6023e1cf458515893996fcb7/2019-7212-30-ARTIFICIAL-INTELLIGENCE-AND-EDUCATION-Ch

O. Zawacki-Richter, V. I. Marín, and ..., “Systematic review of research on artificial intelligence applications in higher education–where are the educators?,” International …. educationaltechnologyjournal …, 2019. doi: 10.1186/s41239-019-0171-0.

P. T. Palomino, A. M. Toda, W. Oliveira, A. I. Cristea, and S. Isotani, “Narrative for gamification in education: why should you care?,” in 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), IEEE, 2019, pp. 97–99.

T. Hariguna, U. Rahardja, and A. Ruangkanjanases, “The impact of citizen perceived value on their intention to use e-government services: an empirical study,” Electronic Government, an International Journal, vol. 16, no. 4, pp. 426–440, 2020.

Q. Aini, U. Rahardja, and T. Hariguna, “The antecedent of perceived value to determine of student continuance intention and student participate adoption of ilearning,” Procedia Comput Sci, vol. 161, pp. 242–249, 2019, doi: 10.1016/j.procs.2019.11.120.

D. Nallaperuma et al., “Online incremental machine learning platform for big data-driven smart traffic management,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 12, pp. 4679–4690, 2019.

G. Li et al., “Detecting cyberattacks in industrial control systems using online learning algorithms,” Neurocomputing, 2019, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0925231219309762

D. Gürdür, J. El-khoury, and M. Törngren, “Digitalizing Swedish industry: What is next?: Data analytics readiness assessment of Swedish industry, according to survey results,” Comput Ind, 2019, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0166361518302987

Q. Aini, S. Riza Bob, N. P. L. Santoso, A. Faturahman, and U. Rahardja, “Digitalization of Smart Student Assessment Quality in Era 4.0,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no. 1.2, pp. 257–265, Apr. 2020, doi: 10.30534/ijatcse/2020/3891.22020.

E. Blasch, T. Pham, C. Y. Chong, W. Koch, and ..., “Machine learning/artificial intelligence for sensor data fusion–opportunities and challenges,” … Electronic Systems …, 2021, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9475913/

Y. Deng, T. Zhang, G. Lou, X. Zheng, and ..., “Deep learning-based autonomous driving systems: A survey of attacks and defenses,” … on Industrial …, 2021, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9397393/

D. Chen, P. Wawrzynski, and Z. Lv, “Cyber security in smart cities: a review of deep learning-based applications and case studies,” Sustain Cities Soc, vol. 66, p. 102655, 2021.

R. Gupta, D. Srivastava, M. Sahu, S. Tiwari, R. K. Ambasta, and P. Kumar, “Artificial intelligence to deep learning: machine intelligence approach for drug discovery,” Mol Divers, vol. 25, pp. 1315–1360, 2021.