Identifying the Dimensions, Components, and Indicators of Artificial Intelligence Functions in Higher Education with a Meta-Synthesis Approach

Document Type : Fall 2025 Special Issue

Authors

1 Associate Professor of Distance Education Planning, Department of Educational Sciences, Payame_ Noor University, Iran

2 PhD student, Department of Distance Education Planning, Payame_ Noor University, Emirates Branch

Abstract

The present research aims to identify the dimensions, components, and indicators of the functions of artificial intelligence in higher education. This study is applied in nature and qualitative in terms of data, and it was conducted using a meta-synthesis method. The study population includes all documents, theoretical foundations, and literature related to the functions of artificial intelligence in higher education from Iranian databases (1395-1403) and international databases (2010-2025). Non-random purposive sampling was employed, and the sample size was determined based on systematic elimination according to the PRISMA flowchart. The data collection tools included data extraction and systematic literature review, and to assess validity, a 27-item checklist based on the PRISMA model was used, while Cohen’s Kappa coefficient was employed to assess reliability. Data analysis was conducted using MaxQDA2018 software and thematic analysis method. The findings indicate that the functions of artificial intelligence in higher education encompass dimensions such as personalized learning, educational data analytics, teaching and learning support, and assessment and success prediction. The personalized learning dimension includes components such as identifying learning needs, diverse educational resources, and self-directed learning; the educational data analytics dimension comprises components such as collecting learning data, advanced analysis, and reporting and visualization; the teaching and learning support dimension includes components such as intelligent teaching tools, support for instructors, and collaborative learning; and the assessment and success prediction dimension consists of components such as automated assessment, predicting student success, evaluating educational quality, and continuous improvement. These dimensions are closely interconnected and contribute to enhancing the quality of education and learning in higher education. These transformations can lead to improved educational quality and increased student success.

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History

  • Receive Date: 15 January 2025
  • Revise Date: 26 January 2025
  • Accept Date: 27 January 2025
  • First Publish Date: 27 January 2025
  • Publish Date: 22 November 2025

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