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For fair academic recruitment at universities and research institutions,determination of the right measure based on globally accepted academic qualityfeatures is a highly delicate, challenging, but quite important problem to beaddressed. In a series of two papers, we consider the modeling part foracademic quality quantification in the first paper, in this paper, and the casestudies part in the second paper. For academic quality quantification modeling,we develop two computational frameworks which can be used to construct adecision-support tool: (i) an optimization-based framework and (ii) a Siamesenetwork (a type of artificial neural network)-based framework. The output ofboth models is a single index called Academic Quality Index (AQI) which is ameasure of the overall academic quality. The data of academics from first-classand average-class world universities, based on Times Higher Education WorldUniversity Rankings and QS World University Rankings, are assumed as thereference data for tuning model parameters.

Optimization- and AI-based approaches to academic quality quantification for transparent academic recruitment: part 1-model development