The Impact of Deep Learning on Personalized Learning Pathways in the Age of Smart Education

Abstract

Purpose: This study investigates the impact of deep learning technologies on personalized learning pathways within smart educational environments, addressing critical knowledge gaps in understanding how artificial intelligence algorithms influence educational personalization effectiveness and implementation outcomes.

Methodology: A comprehensive mixed-methods approach integrating systematic literature review, case study analysis, and comparative evaluation was employed. The investigation analyzed 65 high-quality publications from 2,847 initial studies, examined 12 representative smart education platforms, and developed a Personalization Effectiveness Index (PEI) framework to systematically evaluate deep learning implementations across technological, educational, and user experience dimensions.

Findings: Deep learning technologies demonstrate substantial improvements in personalized learning pathway generation, with Transformer architectures achieving 94.2% personalization precision rates and hybrid approaches providing optimal balance between performance and implementation feasibility. The analysis reveals significant performance variations across platforms (PEI scores ranging from 65.2 to 91.8 points) and identifies algorithmic explainability (68% of implementations) and data privacy concerns (45% of systems) as primary technical barriers.

Conclusion: Deep learning technologies represent a transformative force in educational personalization, requiring careful consideration of technical complexity, institutional capacity, and pedagogical alignment for successful implementation.

Practical Implications: Educational institutions should prioritize hybrid deep learning approaches for balanced performance with manageable deployment requirements, while technology developers can utilize the comparative framework to optimize algorithmic approaches for specific educational contexts.