CareerAlly: An Intelligent NLP-Driven Chatbot
In the current dynamic job market, career success depends on aligning skills with the evolving demands of specific job roles. However, many job-seekers struggle to identify skill gaps and assess their readiness for desired roles. Traditional methods lack a well-defined approach to self-evaluation, leaving candidates uncertain about necessary improvements. This research presents CareerAlly, an intelligent Chatbot designed to assist users in identifying skill gaps and strengthening their competencies for targeted job roles. As a solution, the Chatbot processes the user’s resume as input, applying natural language processing (NLP) techniques such as Named-Entity Recognition (NER) and Regular Expressions (regex) to extract relevant skills. CareerAlly then maps these extracted skills against a predefined job role-skill database (Jobmatcher) to identify deficiencies. To evaluate the user’s expertise, the Chatbot uses a Large Language Model (LLM) to generate skill-specific multiple-choice questions (MCQs) for the chosen job role, enabling objective knowledge assessment. Finally, CareerAlly provides users with a report that visualizes skill-based performance. The Chatbot uses Knowledge Graphs (neo4j) to recommend learning resources for job preparation. CareerAlly’s Artificial Intelligence (AI)-driven methodology offers systematic career guidance and supports users in addressing skill gaps, helping them improve their professional qualifications in a competitive job market. It will help individuals make informed decisions, enhance their competencies, and align their skills with industry demands. In effect, it will increase their employability.
Authors
- Aparna Iyer
- Mayur Gaikwad (ORCID: https://orcid.org/0000-0002-5399-2619)
- Aniket Kailas Shahade (ORCID: https://orcid.org/0000-0003-1025-7654)
- Richa Shukla
- Anchal Sachan
- S. Hevardhan
Institutions
- Symbiosis International University (IN)
Publication Details
- Published
- 2025-03-06
- DOI
- https://doi.org/10.1109/icoeca66273.2025.00019
- Citations
- 1
- Primary Topic
- AI in Service Interactions
- Type
- article
- Field-Weighted Citation Impact
- 2.48