Perkembangan Peserta Didik di Era Artificial Intelligence: Peran Deep Learning dalam Mewujudkan Pembelajaran Adaptif dan Persona

Luvita Indah Pratiwi, Miftahul Hanifa, Nencya Safara, Chelsa Bunga Nagita, Salsabilla Dinar Sandhi

Abstract


This study aims to analyze the role of Deep Learning in supporting student development through adaptive and personalized learning in the era of Artificial Intelligence (AI). A qualitative library research approach was employed using scientific articles, books, and relevant documents. Data were analyzed through content analysis. The findings indicate that Deep Learning can personalize learning materials, strategies, and assessments according to students' characteristics, thereby improving learning effectiveness. However, its implementation still faces challenges related to teachers' readiness, technological infrastructure, and ethical issues. Therefore, the use of AI should be supported by collaboration among technology, educators, and educational policies.


Keywords


Artificial Intelligence, Deep Learning, Student development, Adaptivelearning.

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References


Alam, A. (2022). Possibilities and apprehensions in the landscape of artificial intelligence

in education. Smart Learning Environments, 9(1), 1–15. https://doi.org/10.1186/s40561-

-00193-6

Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE

Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510

Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of

the field. International Journal of Educational Technology in Higher Education, 20(1), 1–

https://doi.org/10.1186/s41239-023-00392-8

Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial intelligence in education: Promises and

implications for teaching and learning. Boston, MA: Center for Curriculum Redesign.

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... Kasneci,

G. (2023). ChatGPT for good? On opportunities and challenges of large language models

for education. Learning and Individual Differences, 103, 102274.

https://doi.org/10.1016/j.lindif.2023.102274

Luckin, R. (2022). Machine learning and human intelligence: The future of education for the

st century. London, England: UCL IOE Press.

Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). AI literacy: Definition, teaching,

evaluation and ethical issues. Proceedings of the Association for Information Science and

Technology, 58(1), 504–509. https://doi.org/10.1002/pra2.487

OECD. (2021). OECD digital education outlook 2021: Pushing the frontiers with artificial

intelligence, blockchain and robots. Paris, France: OECD Publishing.

https://doi.org/10.1787/589b283f-en

Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education:

Challenges and opportunities for sustainable development. Paris, France: UNESCO.

Tuomi, I. (2020). The impact of artificial intelligence on learning, teaching, and education.

Luxembourg: Publications Office of the European Union.

UNESCO. (2021). AI and education: Guidance for policy-makers. Paris, France: UNESCO.

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2020). Systematic review of

research on artificial intelligence applications in higher education. International Journal

of Educational Technology in Higher Education, 17(39), 1–27

https://doi.org/10.1186/s41239-020-00239-7

Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, J. M., ... Li, Y. (2021). A review

of artificial intelligence in education from 2010 to 2020. Complexity, 2021, 1–18.

https://doi.org/10.1155/2021/8812542

Zhou, L., Wu, S., Zhou, M., & Li, F. (2020). School's out, but class's on: The largest online

education in the world today. Best Evidence of Chinese Education, 4(2), 501–519.

https://doi.org/10.15354/bece.20.ar023


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