
In today's competitive job market, resume screening plays a critical role in recruitment processes. However, traditional Applicant Tracking Systems (ATS) rely heavily on keyword matching and lack transparency, personalization, and intelligent feedback. This paper presents AICRUIT, a dual-mode intelligent resume evaluation platform designed to enhance resume screening using Natural Language Processing (NLP) and Explainable AI (XAI). The system operates in two modes: a Normal Review Mode using TF-IDF-based keyword matching and an AI Review Mode powered by advanced language models. It evaluates resumes across multiple criteria such as structure, skills, experience, and ATS compatibility. Additionally, AICRUIT provides an explainable score breakdown, enabling users to understand and improve their resumes effectively. Experimental results demonstrate high usability, with structured scoring and real-time feedback improving resume quality and user engagement. The platform bridges the gap between automated screening and human-like evaluation, making it a powerful tool for job seekers.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
