Abstract
In modern digital learning, the unstructured use of tutorials often leads to incoherent knowledge without an application strategy and without a clear path of professional development. To overcome this challenge, this paper introduces Career Weave, an AI-powered career architect that generates personalized, project-based learning roadmaps.
Career Weave is an innovative Two-Pass LLM Orchestration with the Gemini 3 Flash engine. The Recruiter Pass converts unstructured user input into a standardized technical profile using a context parser. The Architect Pass performs qualitative Delta Analysis of the desired job requirements against the user competencies. This pass has a Feasibility Gate, a logic filter that avoids the generation of unrealistic career paths by evaluating transition realism.
The system is developed using Fast API, MySQL and React Flow, utilizing the principle of Sequential Milestone Unlocking to ensure learners remain focused on achievable goals within a standardized 24-week framework. Experimental results demonstrate the effectiveness of the framework to scale time to learn and recognize career jumps.
KEYWORDS
Artificial Intelligence, Personalized Learning, LLM, Skill Gap Analysis, Career Path Optimization, Educational Technology.
Keshav Dhanka1*, Ashish Saini2, Bhagawat Prasad3, Om Prakash Sharma4
1,2,3B. Tech Scholar, Department of Computer Science & Engineering, Jagannath University, Jaipur- 303901, Rajasthan, India
4Professor, Department of Computer Science & Engineering, Jagannath University, Jaipur- 303901, Rajasthan, India