Integrating Natural Language Processing with Expert Systems for Streamlined Evaluation of Applications

Abstract

Screening of applications for jobs, education, grants, and the like can often be slow, subjective, and inefficient. To address these issues, a novel framework, the Hybrid AI Platform for Streamlining Evaluation (HAIPSE), is introduced, integrating expert rule-based reasoning with NLP and computer-vision techniques, delivering a structured alternative to traditional applicant tracking systems. The platform’s heuristic scoring module, powered by spaCy, extracts key application details and grades responses against predefined criteria.

To capture context and reduce reviewers’ workload, the HAIPSE incorporates large language models, Meta Llama 3-8B and Mistral 8 × 7B, generating concise essay summaries. The novel group-fairness metrics are applied within the evaluation pipeline, making the scoring process more transparent while preserving nuanced content. Furthermore, built-in debiasing steps embed proactive fairness checks directly into the framework’s design, preventing bias rather than merely detecting it post factum.

All components of the HAIPSE were trained and tested on a limited set of sample IDs and 2,000+ real applications from the NIB Trust Fund (Canada). Compared with manual reviews, the HAIPSE improves transparency and reduces bias, while a collaborative audit interface bridges AI automation and expert judgment, reinforcing responsible AI. Ethical considerations of fairness and responsible deployment ensure that the resulting assessments remain scalable, interpretable, and equitable.