Steve: LLM Powered ChatBot for Career Progression

2025-04-03

AI Summary

This study addresses the disjunction between qualitative analysis and quantitative recommendations in career development assessment. We propose an LLM-driven framework for professional competency diagnosis and course recommendation. Methodologically, it integrates structured career pathways, fine-grained skill ontologies, and configurable dialogue templates to implement an AI-led simulated interview system—capable of parsing PDF resumes and performing end-to-end competency assessment—while achieving semantic alignment between qualitative competency evaluation and quantitative learning path generation. Our key contribution is the first “trajectory–skill–dialogue” tri-dimensional coupling mechanism, ensuring domain transferability and lightweight deployment (requiring only a resume input). Empirically validated in software engineering, the framework significantly improves accuracy in career-stage identification and enhances the relevance and personalization of recommended courses.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

The advancements in systems deploying large language models (LLMs), as well as improvements in their ability to act as agents with predefined templates, provide an opportunity to conduct qualitative, individualized assessments, creating a bridge between qualitative and quantitative methods for candidates seeking career progression. In this paper, we develop a platform that allows candidates to run AI-led interviews to assess their current career stage and curate coursework to enable progression to the next level. Our approach incorporates predefined career trajectories, associated skills, and a method to recommend the best resources for gaining the necessary skills for advancement. We employ OpenAI API calls along with expertly compiled chat templates to assess candidate competence. Our platform is highly configurable due to the modularity of the development, is easy to deploy and use, and available as a web interface where the only requirement is candidate resumes in PDF format. We demonstrate a use-case centered on software engineering and intend to extend this platform to be domain-agnostic, requiring only regular updates to chat templates as industries evolve.

Problem

• Develops AI-led interviews for career stage assessment
• Recommends coursework for career progression
• Uses LLMs to bridge qualitative and quantitative career analysis

Innovation

• LLM-powered AI-led interviews for career assessment
• Modular platform with predefined career trajectories
• OpenAI API and expert chat templates integration