Application of artificial intelligence methods for educational organizations’ quality management systems (QMS) effectiveness improvement
Application of artificial intelligence methods for educational organizations' quality management systems (QMS) effectiveness improvement
Purpose – Distance learning mandates and other limitations imposed due to the COVID-19 outbreak have incentivized technological transformations of the higher education system. The ongoing changes caused by the pandemic underpin several significant tendencies, such as mass customization of education, decentralization and higher flexibility of the education system, the multi-aspect result analysis throughout the educational process, etc.
One of the principal aspects of present-day higher education is its integration into the stakeholding context, with education system stakeholders not engaged directly in goal-setting, control, and estimation activities. In other words, the social environment around higher education is expanding, especially given its current state of diversification. This paper aims to analyze the current status of quality management systems applied by educational institutions during the pandemic and the post- pandemic period, their further development, and ways to increase their efficiency via artificial intelligence methods.
Design/methodology/approach – The analysis involved monitoring the functioning of QMS- applying educational systems and studying the public data on higher and secondary education institutions and their activities in several countries before, during, and after the COVID-19 pandemic. The data are provided by the International Organization for Standardization, with the sample comprising over 2,347,791 facilities (the number of sites aggregated from “ISO Survey of certifications to management system standards – Full results 2020”). The purpose of the analysis was to find tendencies related to educational institutions. This study also includes the results of observation of AI implementation by educational institutions and provides insights based on the authors’ personal experience of applying those practices. One of the universities participating in the research provided the enrollment campaign results for AI method validation; the sample consists of 20,284 enrollment records. This sample was used to validate the algorithm developed within the framework of this study.
Findings – The study demonstrates that the COVID-19 pandemic had a significant global-scale impact on the functioning of educational organizations, creating a window of opportunity for the systemic introduction of AI methods to administrative, scientific, educational, auxiliary, and other processes.
Originality/value – The authors analyze the opportunities for managing educational organizations, specifically higher education institutions, from the QMS standpoint. This paper provides a promising approach to systematizing AI methods applied to educational management.
Paper type: Research paper