Investigating AI-Powered Tutoring Systems that Adapt to Individual Student Needs, Providing Personalized Guidance and Assessments

Year 2023, Volume: 31, 67 - 73, 30.10.2023

Mohammed Rızvı

https://doi.org/10.55549/epess.1381518

Abstract

This comprehensive literature review seeks to assess the potential of AI-powered tutoring systems that are able to adapt and provide personalized guidance tailored to individual student needs. As Artificial Intelligence (AI) technologies continue to progress at a rapid rate, there is ever increasing interest in leveraging these capabilities for educational purposes. By offering customized instruction based on each student's strengths, weaknesses, and learning style preferences, AI-powered tutoring systems may revolutionize how students learn. The review will examine various studies and research papers exploring the design, implementation techniques as well as effectiveness of such innovative solutions. This includes delving into algorithms like machine learning, natural language processing or data mining which enable these systems to adjust their interactions according to students' requirements. Moreover, it will investigate any positive impacts such personalized teaching has had on academic performance levels in addition to engagement motivation amongst learners. Additionally, this study shall look into existing challenges faced when using AI-powered tutoring systems; from ethical concerns about privacy issues thought too effective teacher -student communication. After taking all findings from available literature into account we can then identify areas where more work is needed, offer suggestions for future improvements or studies within this field. In conclusion, with our synthesis of insights gathered during our investigation we hope improve awareness & understandings around utilizing AI technology for educational purposes so that teachers & students alike can benefit from personalized adaptive educations experiences.

Keywords

AI-powered tutoring systems, Adaptive learning, Machine learning

References

• Afini Normadhi, N. B., Shuib, L., Md Nasir, H. N., Bimba, A., Idris, N., & Balakrishnan, V. (2019). Identification of personal traits in adaptive learning environment: Systematic literature review. Computers & Education, 130, 168–190.
• Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13–49.
• Almohammadi, K., Hagras, H., Alghazzawi, D., & Aldabbagh, G. (2017). A survey of artificial intelligence techniques employed for adaptive educational systems within elearning platforms. Journal of Artificial Intelligence and Soft Computing Research, 7(1), 47–64.