Authors : Gazi Touhidul Alam; Md Ismail Jobiullah; Arannita Saha Suspee; Mohammed Majid Bakhsh; Abu Saleh Muhammad Saimon; Syed Mohammed Muhive Uddin

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/2k9kdvym

Scribd : https://tinyurl.com/c53d5858

DOI : https://doi.org/10.38124/ijisrt/25apr1081

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Abstract : Brief Summary of the Research: This investigation studies AI-driven Learning Management Systems which function as educational centers to support employee training at Business Analyst and Quality Assurance levels on projects based in the United States. The research uses Natural Language Processing (NLP), predictive analytics with adaptive learning algorithms to discover methods which resolve expertise deficits and strengthen connections while adhering to industry standards that evolve over time.  Purpose of the Study: The research aims to create and prove an expandable system based on AI-LMS which generates customized data- based training content. The framework exists to boost team coordination and enhance productivity and minimize project redo through technical and soft skill alignment between BA and QA personnel with present and future project specifications.  Methodology: The research employed multiple methods consisting of these elements:  This research examined the available industry documents and previous scientific studies focused on AI-based training systems along with LMS frameworks and BA-QA role efficiency improvement through literature reviews.  This section includes evaluating AI-powered LMS platform implementations at organizations which use these systems for the training of BA and QA professionals.  We designed an experimental AI-based LMS module that enables prototyping tests of personalized paths and real-time competence evaluation and tailored content methods through its prototype.  The system will deploy predictive analytics combined with user performance metrics for determining how well training interventions work thus enabling better understanding of continuous improvement opportunities. Key Findings: The learning management system developed using AI delivers a personalized experience when it aligns educational content to BA and QA professional skill levels together with their learning preferences. Through enhanced communication channels the technology enables better cross-team associations which results in better project team coordination with decreased project implementation errors. The LMS data analytics offers insights that fuel prompt corrective actions resulting in higher project output while decreasing valuable project work that must be redone. The framework exhibits effective scalability for different business needs because the research proved its availability to diverse teams operating remotely or across cultural backgrounds.  Conclusion: Results show that establishing an AI-based LMS as a unified knowledge center represents a strong approach for training BA and QA professionals. The usage of advanced AI technologies strategically produces individual and team competency growth and project management efficiency improvements in the competitive environment of US-based IT and business projects. The research introduces new possibilities for conducting investigations about AI learning ecosystems as essential instruments to develop future-proof employee capabilities across industries.

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