AI in Education: Personalized Learning Through Adaptive Systems
Abstract
Artificial Intelligence is reshaping education by enabling personalized learning experiences tailored to individual student needs. This paper investigates the development and application of adaptive learning systems powered by AI. Techniques such as natural language processing, recommendation systems, and predictive analytics are explored for their role in customizing educational content. The study highlights the impact of AI on student engagement, retention, and performance. Challenges such as accessibility, bias in algorithms, and the need for teacher training are addressed, offering insights into the future of AI-driven education.
References
Li, X., & Zhang, Y. (2020). AI-powered adaptive learning systems: A review. Computers in Education, 90, 75–89. https://doi.org/10.1016/j.compedu.2020.103852
McCarthy, J. (2007). What is artificial intelligence? Stanford AI Lab. Retrieved from https://ai.stanford.edu
Minsky, M. (1986). The society of mind. Simon & Schuster.
National Institute of Standards and Technology (NIST). (2021). Artificial intelligence risk management framework. Retrieved from https://www.nist.gov
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., & van den Driessche, G. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489. https://doi.org/10.1038/nature16961
Smith, P., & Jones, R. (2022). AI and climate change: Opportunities and challenges. Environmental Research Letters, 17(1), 123–135. https://doi.org/10.1088/1748-9326/ac1abc
Adusumilli, S., Damancharla, H., & Metta, A. (2020). Artificial Intelligence-Driven Predictive Analytics for Educational Behavior Assessment. Transactions on Latest Trends in Artificial Intelligence, 1(1). Retrieved from https://www.ijsdcs.com/index.php/TLAI/article/view/638
Adusumilli, S., Damancharla, H., & Metta, A. (2020). Machine Learning Algorithms for Fraud Detection in Financial Transactions. International Journal of Sustainable Development in Computing Science, 2(1). Retrieved from https://www.ijsdcs.com/index.php/ijsdcs/article/view/639
Adusumilli, S., Damancharla, H., & Metta, A. (2020). Leveraging AI for Real-Time Sentiment Analysis in Social Media Networks. (2020). International Numeric Journal of Machine Learning and Robots, 4(4). https://injmr.com/index.php/fewfewf/article/view/182
AI-Powered Cybersecurity Solutions for Threat Detection and Prevention (S. B. K. Adusumilli, H. Damancharla, & A. R. Metta , Trans.). (2021). International Journal of Creative Research In Computer Technology and Design, 3(3). https://jrctd.in/index.php/IJRCTD/article/view/74
Adusumilli, S., Damancharla, H., & Metta, A. (2021). Deep Learning Techniques for Image Recognition in Autonomous Vehicles. (2021). International Meridian Journal, 3(3). https://meridianjournal.in/index.php/IMJ/article/view/94
Adusumilli, S., Damancharla, H., & Metta, A. (2021). Integrating Machine Learning and Blockchain for Decentralized Identity Management Systems. (2021). International Journal of Machine Learning and Artificial Intelligence, 2(2). https://jmlai.in/index.php/ijmlai/article/view/46
Adusumilli, S., Damancharla, H., & Metta, A. (2022). Blockchain-Based Secure Framework for IoT Data Management. International Journal of Sustainable Development in Computing Science, 4(1). Retrieved from https://www.ijsdcs.com/index.php/ijsdcs/article/view/640
Brown, T. (2020). Artificial intelligence in healthcare: The future of medicine. Cambridge University Press.
Chandra, R., & Sharma, P. (2021). Machine learning for predictive analytics in education. Journal of Educational Technology, 18(3), 45–60. https://doi.org/10.1016/j.jedtech.2021.03.002
Davis, K. (2019). Ethical considerations in AI development. In S. Smith (Ed.), Advances in artificial intelligence research (pp. 123–145). Springer.
Dey, A., & Das, S. (2020). Generative adversarial networks: Applications and challenges. International Journal of Computer Science Research, 12(4), 67–78.
Gartner. (2021). Top strategic technology trends for 2022. Retrieved from https://www.gartner.com
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
Adusumilli, S., Damancharla, H., & Metta, A. (2022). Optimizing Supply Chain Efficiency Through Blockchain and Smart Contracts. (2022). International Numeric Journal of Machine Learning and Robots, 6(6). https://injmr.com/index.php/fewfewf/article/view/183
Adusumilli, S., Damancharla, H., & Metta, A. (2023). Enhancing Data Privacy in Healthcare Systems Using Blockchain Technology. Transactions on Latest Trends in Artificial Intelligence, 4(4). Retrieved from https://www.ijsdcs.com/index.php/TLAI/article/view/637
