LEXAI: AN LLM-POWERED PERSONALIZED LEGAL CAREER ADVISOR USING PSYCHOMETRIC TRAIT MAPPING AND JOB INTELLIGENCE

Abstract

Selecting the appropriate legal career trajectory frequently presents a multifaceted problem for law students and early-career practitioners. Conventional career instruments depend on fixed inputs such as resumes and academic achievements, frequently overlooking the intricate strengths, interests, and rapidly changing requirements of the legal industry. We present LexAI, a real-time, intelligent legal career advisor driven by Large Language Models (LLMs) utilizing the LLaMA3 model through Ollama, included into a LangChain-managed pipeline. LexAI identifies six psychometric traits: confidence, public speaking, writing, analytical ability, empathy, and curiosity, from open-ended user replies. These are semantically aligned with over 40,000 UK law positions, processed and stored in ChromaDB, facilitating domain-specific suggestions in areas such as Corporate Compliance and Criminal Litigation. Our method produced more than 1,200 contextual inquiries from employment data to enhance trait elicitation and role alignment. Assessments indicate a 90.5% accuracy in trait extraction, a 4.6/5 rating for Top-1 role match (as evaluated by experts), and a 92% user satisfaction rate in pilot tests (N=25). LexAI facilitates vector-oriented memory, multilingual scalability, and jurisdiction-specific customization. This study illustrates how LLMs may revolutionize legal career counseling by providing intelligent, interactive guidance, thereby minimizing career mismatches and presenting a scalable model for AI-driven consulting across other professional fields.