Navigate Career Thriving AI Powered Courses Guidance Junction with Students Reviews
DOI:
https://doi.org/10.32628/CSEIT241061128Keywords:
Student Review System, career guidance, AI-powered recommendation, peer reviews, chatbot integration, educational improvementAbstract
This project introduces a Student Review System, a web-based platform enabling students to share reviews on courses, professors, and facilities, fostering a valuable knowledge base for academic decision-making. Recognizing the challenges students face in choosing career paths, the Navigate Career Thriving AI-Powered Courses Guidance Junction with Student Reviews extends this concept by integrating peer and expert insights, personalized recommendations, and a real-time chatbot. The platform supports review creation, editing, and categorized searches, ensuring transparency and easy access to relevant insights. A user authentication system maintains credibility by preventing misuse. The chatbot provides interactive support, answering queries and offering tailored guidance aligned with individual interests. Additionally, analytical tools empower administrators to monitor feedback trends and address improvement areas, fostering institutional growth. This comprehensive system combines peer feedback and AI-driven recommendations to help students make confident, informed academic and career decisions.
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Dabbagh, N., & Kitsantas, A. (2012). Personal Learning Environments, social media, and self-regulated learning: A natural formula for connecting formal and informal learning. The Internet and Higher Education, 15(1), 3-8. DOI: https://doi.org/10.1016/j.iheduc.2011.06.002
Huang, H., & Fu, X. (2009). Enhancing e-learning effectiveness using an intelligent content recommendation system. International Journal of Distance Education Technologies, 7(3), 56-71.
Manouselis, N., Drachsler, H., Verbert, K., & Duval, E. (2012). Recommender systems for learning. Springer Briefs in Electrical and Computer Engineering. DOI: https://doi.org/10.1007/978-1-4614-4361-2
Chatti, M. A., Jarke, M., & Frosch-Wilke, D. (2007). The future of e-learning: A shift to knowledge networking and social software. International Journal of Knowledge and Learning, 3(4/5), 404-420. DOI: https://doi.org/10.1504/IJKL.2007.016702
Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 3-10.
Koper, R. (2014). Conditions for effective smart learning environments. Smart Learning Environments, 1(1), 1-17. DOI: https://doi.org/10.1186/s40561-014-0005-4
Aleven, V., McLaren, B. M., Roll, I., & Koedinger, K. R. (2006). Toward meta-cognitive tutoring: A model of help-seeking with a cognitive tutor. International Journal of Artificial Intelligence in Education, 16(2), 101-128.
Brusilovsky, P., & Millán, E. (2007). User Models for Adaptive Hypermedia and Adaptive Educational Systems. In The Adaptive Web (pp. 3-53). Springer. DOI: https://doi.org/10.1007/978-3-540-72079-9_1
Zhang, J., & Zhou, X. (2014). Recommender Systems for E-learning: Framework, Technologies, and Research Issues. In Recommender Systems Handbook (pp. 811-838). Springer.
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