Published on: April 2026
AI PERSONALIZED LEARNING PATH GENERATOR
Amirishetty Madhumitha Nanbothu Ramya Megoti Bhanu Prasad Somani Sai Charan
P Niharika
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Abstract
The AI Personalized Learning Path Generator is developed to address the growing challenge of unstructured and inefficient learning in today’s digital era. With the rapid expansion of domains such as Artificial Intelligence, Data Science, and Software Development, learners often face confusion in identifying the right skills and structured path required to achieve their career goals. To overcome this problem, the system leverages Artificial Intelligence and Natural Language Processing to analyze user-specific inputs such as resumes and career objectives. The system extracts relevant skills from the uploaded resume and understands the target role using advanced AI models.A key feature of the system is skill gap analysis, where the user’s existing skills are compared with the required skills for the desired role. Based on this analysis, the system generates a structured and personalized learning roadmap using Large Language Models (LLMs), specifically integrated through AI APIs.
How to Cite this Paper
Madhumitha, A., Ramya, N., Prasad, M. B. & Charan, S. S. (2026). AI Personalized Learning Path Generator. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.022
Madhumitha, Amirishetty, et al.. "AI Personalized Learning Path Generator." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.022.
Madhumitha, Amirishetty,Nanbothu Ramya,Megoti Prasad, and Somani Charan. "AI Personalized Learning Path Generator." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.022.
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References
- Alqahtani et al. (2023), AI-Driven Educational Recommendation System for Learning Outcome Prediction.
- Kumar and Sharma (2022), NLP-Based Resume Parsing System for Information Extraction.
- Chen et al. (2021), Performance Analysis of Machine Learning Models for Educational Data.
- Romero and Ventura (2020), Educational Data Mining and Learning Analytics: A Survey.
- Baniata et al. (2019), Deep Learning-Based Recommendation Framework Using RNN and GRU.
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- •Peer Review Type: Double-Blind Peer Review
- •Published on: Apr 03 2026
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