| Journal of Collective Sciences and Sustainability
Received: 20 June 2025; Revised: 13 September 2025; Accepted: 25 September 2025; Published Online: 29 September 2025.
J. Collect. Sci. Sustain., 2025, 1(2), 25410 | Volume 1 Issue 2 (June 2026) | DOI: https://doi.org/10.64189/css.25410
© The Author(s) 2025
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)
An AI-Driven Adaptive Training Platform with Digital
Twin-Based Skill Gap Analysis and Future Readiness
Insights
Manasvi Manoj Nayak, Pratik Neupane, Pranali Praveen Pahurkar and Drashti Shrimal*
Department of Computer Engineering, Thakur College of Engineering and Technology, Mumbai, Maharashtra, 400056, India
*Email: drashti.shrimal@thakureducation.org (Drashti Shrimal)
Abstract
The rapid digital transformation of industries has intensified the demand for adaptive, data-driven learning
ecosystems capable of continuously aligning workforce skills with evolving technological trends. Traditional
static training systems struggle to meet these dynamic needs, creating persistent skill gaps and limiting future
employability. This study addresses this challenge by exploring the integration of Artificial Intelligence (AI) and
Digital Twin (DT) technologies to create a hybrid, future-ready training framework. The proposed model
combines reinforcement learning with generative AI to dynamically assess learner progress, perform real-time
skill-gap analysis, and personalize training paths through a continuously evolving digital twin of each learner.
The framework was evaluated using pilot simulations in a vocational training environment. Results showed a
22 % improvement in personalization accuracy, 15%-20% reduction in skill gaps, and an 82 % accuracy in
future-readiness prediction compared with conventional adaptive learning systems. These findings highlight
the transformative potential of merging AI adaptability with DT contextualization to deliver immersive,
predictive, and career-aligned learning experiences. The impact of this research lies in redefining the paradigm
of personalized education and workforce development-moving beyond reactive learning to proactive,
anticipatory training models that prepare individuals for the demands of the digital economy.
Keywords: Adaptive learning; Artificial intelligence; Digital twin; Skill gap analysis; Future readiness; Reinforcement
learning; Personalized training.
1. Introduction
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• Processor: Intel Core i5 (11th Gen) or equivalent
• Memory (RAM): Minimum 16 GB
• Storage: 512 GB SSD for fast data access and storage of learner datasets
• Graphics Processing Unit (GPU): NVIDIA RTX 2060 or above (for training AI models and handling
simulations)
This configuration ensured smooth execution of reinforcement learning modules, real-time chatbot
interactions, and digital twin simulations without performance bottlenecks.
Funding Declaration
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-
profit sectors.
Artificial Intelligence (AI) Use Disclosure
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the
writing or editing of the manuscript and no images were manipulated using AI.
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