Internal and external perspective on gender equity in STEM field

Abstract

This article introduces an approach aided by Artificial Intelligence based on Large Language Model approaches designed to assess the status of the gender equity in Science, Technology, Engineering and Maths (STEM) subjects comparing data gathered from two types of surveys carried out among Academic and Non-Academic environments.  We conducted the Academic study in the Department of Engineering and Geology at the University “G. d’Annunzio" of Chieti-Pescara (UdA), Italy, and the Non-Academic one among a broader public audience mainly composed of common people and students who took part in the Researchers’ Night 2024 at the University Campus of Chieti.  The study adopted a dual approach: an Academic level analysis – named Internal Analysis - examines departmental data and dynamics, and a Non-Academic level analysis – called External Analysis, evaluates perceptions and awareness of gender equity among the participants to the Researchers’ Night 2024 at the Chieti Campus, who were involved in interactive games and questionnaires focused on gender representation in STEM fields. The outcomes illustrated and discussed hereinafter offer preliminary insights into gender gaps, stereotypes, and potential pathways to fostering greater inclusion and equity in working and living around the STEM world. Creating a more inclusive and equitable ground requires actively supporting the presence and interaction of diverse viewpoints, especially those shaped by different gender experiences. Men and women may bring distinct approaches, values, and ways of evaluating situations, encouraging an open dialogue between different perspectives that can enrich strategic thinking and lead to more robust and well-rounded outcomes both at institutional level and in human relationships.