Developing an AI-Assisted Methodology for Analyzing Documented Procedural Openness in Academic Recruitment Announcements

Walery Okulicz-Kozaryn, Аrtem Аrtyukhov, Nadiia Аrtyukhova

Information, 2026

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.3390/info17090924
Data confidence 70 medium
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Abstract

Academic recruitment announcements are the primary public source of information on competitive procedures. However, their unstructured nature significantly complicates the systematic analysis of procedural characteristics and comparison of academic recruitment announcements published by different universities and in different scientific disciplines. This study aims to develop an AI-assisted research methodology to analyze unstructured texts in academic recruitment announcements, grounded in procedural and ethical criteria. The methodology enables the formation of a standardized procedural profile for each academic recruitment announcement, the identification of procedural and ethical risks, and the presentation of the results in a unified analytical format. The assigned scores are verified by the researcher for consistency with the original text and the uniform application of the criteria. Additionally, independent expert validation of 40 analytical decisions from five disciplinary corpora showed that 38 of 40 classifications (95.0%) were confirmed without changes. To provide an integral characteristic of an individual academic recruitment announcement, the Procedural Openness Score (POS) indicator is proposed to reflect the proportion of criteria classified as low risk. The application of the methodology is demonstrated on five independent samples of academic recruitment announcements from various scientific disciplines. The empirical part demonstrates the applicability of a unified analytical architecture to various corpora of academic recruitment announcements. The interpretability of the results is ensured by comparing the original fragments of academic recruitment announcements with the assigned procedural risk scores. The proposed methodology expands the potential for using artificial intelligence in research practice by combining transparent analysis rules, a standardized procedural academic recruitment announcement profile, and a scalable procedural transparency metric. Although the methodology was tested on academic recruitment announcements, its architecture allows for application to the analysis of other types of organizational and regulatory documents.