AI Skill Assessment and Learning Path Generator

Abstract

The rapid evolution of digital education technologies has transformed traditional teaching and learning practices into highly interactive and technology-driven ecosystems. However, despite the availability of massive open online courses, e-learning platforms, and digital certification programs, most systems continue to follow a generalized content delivery approach that does not adapt to individual learner needs[1]. This lack of personalization often results in reduced learner engagement, inefficient knowledge acquisition, and poor skill mastery. To address these limitations, this paper proposes an AI Skill Assessment and Learning path Generator that integrates artificial intelligence, machine learning and learning analytics to evaluate learner competency and generate adaptive learning pathways. The proposed system conducts intelligent skill assessment, analyses learner performance metrics, and applies predictive modelling techniques to classify proficiency levels[4]. Based on identified skill gaps, a recommendation engine dynamically generates structured and personalized learning paths aligned with prerequisite dependencies and industry skill standards[5]. The framework supports continuous reassessment, ensuring improved learning efficiency, accurate skill classification, and higher learner satisfaction compared to static learning systems.

Introduction

The expansion of online learning and digital skill initiatives has highlighted the limitations of traditional, one-size-fits-all educational platforms. To address this, the AI Skill Assessment and Learning Path Generator uses adaptive assessments and machine learning classification to evaluate learner competency in real time, generating personalized learning paths tailored to individual strengths and gaps.

The system integrates Random Forest classifiers, feature engineering, and continuous reassessment to categorize learners into Beginner, Intermediate, or Advanced levels, ensuring content difficulty and sequencing match their evolving proficiency. Its modular, web-based architecture supports scalability for higher education, corporate training, EdTech platforms, and government skill programs, enabling automated skill gap detection, targeted recommendations, and dynamic learning progression.

Experimental results show that AI-driven adaptive learning improves engagement, course completion, and post-assessment scores compared to static systems, demonstrating the framework’s effectiveness in delivering personalized, efficient, and data-driven skill development.

Conclusion

The proposed AI Skill Assessment and Learning Path Generator presents an intelligent and adaptive framework designed to overcome the limitations of conventional e-learning systems. Traditional digital learning environments largely depend on static assessments and fixed content delivery models, which often fail to accommodate individual learner differences in prior knowledge, learning pace, and cognitive ability[2]. In contrast, the proposed system integrates adaptive assessment mechanisms, machine learning-based skill classification, and dynamic recommendation strategies to deliver a highly personalized learning experience. By leveraging artificial intelligence and educational data mining techniques, the system continuously analyzes learner performance data and generates multidimensional competency profiles[4]. These profiles enable precise identification of knowledge gaps and mastery levels across different domains. The integration of supervised learning algorithms ensures accurate classification of learners into proficiency categories, thereby facilitating structured and progressive skill development[9]. Furthermore, the recommendation engine constructs customized learning paths based on prerequisite mapping, performance trends, and competency thresholds, ensuring logical and systematic content progression[5].

References

[1] P. Brusilovsky, “Adaptive Hypermedia”, User Modeling and user-Adapted Interaction, 2001. [2] R. Mayer, “Multimedia Learning”, Cambridge University Press, 2009. [3] J. Anderson et al., “Intelligent Tutoring System”, AI Magazine, 1995. [4] C. Romero and S. Ventura, “Educational Data Mining: A Review”, IEEE Transactions on Systems, 2010 [5] F. Ricci et al., “Recommender Systems Handbook”, Springer, 2015.

Copyright

Copyright © 2026 Baranitha A, Dr. K. Thenmozhi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.