Education, Science, Technology, Innovation and Life
Open Access
Sign In

The potential and risks of artificial intelligence in promoting personalized learning

Download as PDF

DOI: 10.23977/jaip.2025.080112 | Downloads: 29 | Views: 514

Author(s)

Jiang Qiang 1

Affiliation(s)

1 Zhejiang Yuexiu University, Shaoxing, Zhejiang, China

Corresponding Author

Jiang Qiang

ABSTRACT

As a significant advancement in the field of technology, Artificial Intelligence (AI) has achieved automation of specific tasks by simulating and enhancing human cognitive functions. In the field of education, AI has notably promoted the development of personalized learning. By analyzing learning data, AI can identify students' learning patterns and provide targeted academic guidance, enabling real-time feedback and dynamic adjustments to learning content. Additionally, AI offers personalized learning resources and auxiliary tools to enhance motivation and efficiency in learning. However, the application of AI in personalized learning also faces risks such as privacy and data security, algorithmic bias, and educational equity. To address these challenges, strict data protection measures must be taken to ensure algorithmic fairness and to promote the equitable distribution of educational resources.

KEYWORDS

Artificial Intelligence (AI), personalized learning, data security, educational equity

CITE THIS PAPER

Jiang Qiang, The potential and risks of artificial intelligence in promoting personalized learning. Journal of Artificial Intelligence Practice (2025) Vol. 8: 86-91. DOI: http://dx.doi.org/10.23977/jaip.2025.080112.

REFERENCES

[1] Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach. Pearson.
[2] Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433-460.
[3] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
[4] IBM. (2017). Watson Education. Retrieved from https://www.ibm.com/watson/education
[5] Kumar, N. S. (2019). Implementation of artificial intelligence in imparting education and evaluating student performance. Journal of Artificial Intelligence, 1(01), 1-9. 
[6] Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582-599.
[7] Knewton. (2018). Alta. Retrieved from https://www.knewton.com/alta
[8] Hirschberg, J., & Manning, C. D. (2015). Advances in natural language processing. Science, 349(6245), 261-266.
[9] Timms, M. J. (2016). Letting artificial intelligence in education out of the box: Educational cobots and smart classrooms. International Journal of Artificial Intelligence in Education, 26(2), 701-712.
[10] Dede, C. (2017). Emerging technologies and distributed learning. American Journal of Distance Education, 31(2), 122-136.
[11] Binns, R., Veale, M., Van Kleek, M., & Shadbolt, N. (2018). It's Reducing a Human Being to a Percentage: Perceptions of Justice in Algorithmic Decisions. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1-14. 
[12] Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.
[13] Warschauer, M., & Matuchniak, T. (2019). New Technology and Digital Worlds: Analyzing Evidence of Equity in Access, Use, and Outcomes. Review of Research in Education, 34(1), 179-225.
[14] Selwyn, N. (2019). Should Robots Replace Teachers? AI and the Future of Education. Polity Press.
[15] Protection, F. D. (2018). General data protection regulation (GDPR). Intersoft Consulting, Accessed in October, 24(1).
[16] Gupta, R., Saxena, D., & Singh, A. K. (2021). Data security and privacy in cloud computing: concepts and emerging trends. arXiv preprint arXiv:2108.09508.
[17] Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. In Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency (pp. 149-159).
[18] Diakopoulos, N. (2016). Accountability in algorithmic decision making. Communications of the ACM, 59(2), 56-62. 
[19] Williamson, B. (2017). Big data in education: The digital future of learning, policy and practice. Sage.
[20] Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
[21] Robinson, K. (2011). Out of Our Minds: Learning to be Creative. Hardcover.
[22] Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. 

Downloads: 15127
Visits: 485153

Sponsors, Associates, and Links


All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.