This research paper focuses on the development, implementation, and assessment of a full-stack Conversational AI for personalized academic planning and career guidance. The proposed framework involves a combination of NLP technologies, trend-based performance analytics, and a modular full-stack architecture to overcome limitations of conventional, static advisory practices. First, the existing literature on AI technologies applied in an educational environment was analyzed and researched to define areas requiring further investigation. This allowed me to highlight crucial research gaps in the area, such as the absence of real-time conversation-based interactions, the automation of document analysis and summarization processes, and personalized, statistically-based performance tracking. Consequently, in this study, a completely new system is developed using a React.js frontend, a Node.js backend, a PostgreSQL database, and a dual-model AI engine that uses both the Google Gemini API and a local LLM as a fallback option. Specifically, the methodology includes the use of OCR based on Tesseract.js for automated marksheet digitization, an algorithmic statistical trend estimation model for cumulative GPA prediction, and a dynamic rule-based engine for creating customized career maps.
Conversational AI, Natural Language Processing, Academic Planning, Career Guidance, Educational Technology, Predictive Analytics, Large Language Models, Optical Character Recognition, Full-Stack Development, Personalized Learning
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