Domain-First Intelligence: An AI-Driven Pre-Screening Framework for University Placement Cells to Mitigate CGPA-Centric Filtering Bias in Campus Recruitment

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Publication Details

Journal
International Journal of Innovative Research in Technology
Published
2026-09-14
DOI
https://doi.org/10.64643/ijirt.208432-459
Primary Topic
Higher Education and Employability
Type
article
Field-Weighted Citation Impact
0.00
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article

Domain-First Intelligence: An AI-Driven Pre-Screening Framework for University Placement Cells to Mitigate CGPA-Centric Filtering Bias in Campus Recruitment

Shubham Sarkar, Sunil Mahajan, Khushali Amreliya, Dev Sanghavi
International Journal of Innovative Research in Technology
Higher Education and Employability
article

Domain-First Intelligence: An AI-Driven Pre-Screening Framework for University Placement Cells to Mitigate CGPA-Centric Filtering Bias in Campus Recruitment

Shubham Sarkar, Sunil Mahajan, Khushali Amreliya, Dev Sanghavi
article en

Abstract

Placement in most institutions of engineering and technology still depends on an absolute cumulative grade point average (CGPA) cut-off, typically CGPA ≥ 7.0, to shortlist which students even qualify for presentation to potential employers.In effect, a significant number of "average-CGPA" students are precluded from evaluation of their job-related competencies even before such evaluation takes place, despite CGPA being a poor and error-prone measure of job competence.Following recent findings of discrimination in traditional and artificial intelligenceassisted hiring procedures, we suggest here a competency-focused, fairness audited placement process wherein students are rated through competency assessment relevant to the specific job, effort-based bonus rating (internship, projects, certification), and an optional process of blind screening prior to candidate review by a recruiting company.Due to the fact that the pipeline hasn't been developed yet, simulation is used to validate its design, where the population consists of 1,000 students and is calibrated to a realistic CGPA range (3.5-9.9)divided according to the criteria of gender, prior exposure to the field and first-generation status; and where the competency-based pipeline is run on this population along with the CGPA-only pipeline.The simulation results show that the competency-based pipeline provides an opportunity for assessment to 974 out of 1,000 students (97.4%), compared to 311 (31.1%) in the case of the CGPA-only pipeline, and increases the portion of average-CGPA students in the assessed population from 0% to 97.5%; in addition, the pass rate stays over 93% for all subgroups of the population examined, and pass rate grows monotonically depending on the degree of prior exposure to the field (93.6%→100%), which means that the test continues to discriminate between students based on their skills but not on their backgrounds.

International Journal of Innovative Research in TechnologyVol. 13(5)
G.S. Science, Arts And Commerce College (IN)
Openalex Percentile: Top 2%
Higher Education and Employability
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