Abstract
Artificial Intelligence (AI) is emerging as a transformative force in the field of education, particularly at the school level. This paper explores the role of AI in improving the quality of education through innovative tools such as machine learning, intelligent tutoring systems, natural language processing, and automated assessment systems. AI enables personalized learning experiences, enhances student engagement, and supports teachers by reducing administrative workload. It also facilitates smart learning environments that adapt to individual learner needs. Despite its benefits, challenges such as lack of infrastructure, teacher training, and ethical concerns remain significant barriers to implementation. The study highlights that AI can play a crucial role in bridging educational gaps and improving learning outcomes when integrated effectively into the school education system.
Keywords
AI
Quality Education
Machine Learning
NLP
Neural Networks…
References
- Allen, J. (1998). Artificial intelligence and philosophy. Cambridge University Press.
- Bhatnagar, R., Gupta, P., & Sharma, S. (2018). Artificial intelligence: Concepts, applications and future directions. Journal of Information Systems Research, 12(3), 45–60.
- Brachman, R. J. (2006). Systems that know what they’re doing. MIT Press.
- Hernández-Orallo, J. (2017). Evaluation in artificial intelligence: From task-oriented to capability-oriented measurement. Artificial Intelligence Review, 48(3), 351–370.
- http://www.library.thinkquest.org
- https://en.wikibooks.org/wiki/Computer_Science:Artificial_Intelligencehttp://www.how stuffworks.com/arificialintelligence
- https://healthcare-bulletin.co.uk/article/the-role-of-artificial-intelligence-in-modern-healthcare-advances-challenges-and-future-prospects-3187/
- https://ijsea.com/archive/volume9/issue4/IJSEA09041004.pdf
- https://www.vedantu.com/english/artificial-intelligence-essay
- Jain, A. K., Duin, R. P. W., & Mao, J. (2004). Statistical pattern recognition: A review. IEEE
- Transactions on Pattern Analysis and Machine Intelligence, 22(1), 4–37.
- Khashman, A. (2009). Neural networks for credit risk evaluation. Journal of Applied Soft Computing, 9(1), 135–139.
- McCarthy, J. (1988). What is artificial intelligence? Stanford University.
- McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–133.
- Minsky, M. (1958). Steps toward artificial intelligence. Proceedings of the IRE, 46(1), 8–30.
- Newell, A., & Simon, H. A. (1976). Computer science as empirical inquiry: Symbols and search. Communications of the ACM, 19(3), 113–126.
- Nilsson, N. J. (2009). The quest for artificial intelligence. Cambridge University Press.
- PWC. (2019). Sizing the prize: What’s the real value of AI for your business and how can you capitalise? PricewaterhouseCoopers. https://www.pwc.com
- Russell, S., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.
- Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460.
- Von Neumann, J. (1958). The computer and the brain. Yale University Press.
- Wiener, N. (1948). Cybernetics: Or control and communication in the animal and the machine. MIT Press.
