Exploring Student and Educator Challenges in AI Competency Development: A Comparative Analysis

As artificial intelligence (AI) rapidly transforms the landscape of higher education, there is a critical need to develop AI competency among both educators and students. However, current AI policies and guidelines are often top-down and lack grassroots insights from key stakeholders. Drawing on the recently released UNESCO AI competency frameworks for educators and students (2024), this study presents findings from a global survey of over 600 students and educators. The results highlight significant disparities in AI engagement across groups, disciplines, and regions, as well as barriers such as inconsistent institutional guidance, limited access to hands-on training, and infrastructural constraints, particularly in Global South contexts. Drawing on these insights, the study offers practical, evidence-informed recommendations for higher education institutions, educators, and students to support equitable, sustainable, and context-sensitive AI competency development.

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Publication Details

Journal
Multimodal Technologies and Interaction
Published
2026-03-09
DOI
https://doi.org/10.3390/mti10030027
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
0.00
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article

Exploring Student and Educator Challenges in AI Competency Development: A Comparative Analysis

Xuanning Chen, Xin Zhao, Haoyu Xie, Fengchun Miao
Multimodal Technologies and Interaction
Online Learning and Analytics
article

Exploring Student and Educator Challenges in AI Competency Development: A Comparative Analysis

Xuanning Chen, Xin Zhao, Haoyu Xie, Fengchun Miao
article en

Abstract

As artificial intelligence (AI) rapidly transforms the landscape of higher education, there is a critical need to develop AI competency among both educators and students. However, current AI policies and guidelines are often top-down and lack grassroots insights from key stakeholders. Drawing on the recently released UNESCO AI competency frameworks for educators and students (2024), this study presents findings from a global survey of over 600 students and educators. The results highlight significant disparities in AI engagement across groups, disciplines, and regions, as well as barriers such as inconsistent institutional guidance, limited access to hands-on training, and infrastructural constraints, particularly in Global South contexts. Drawing on these insights, the study offers practical, evidence-informed recommendations for higher education institutions, educators, and students to support equitable, sustainable, and context-sensitive AI competency development.

Multimodal Technologies and InteractionVol. 10(3)
University of Cambridge (GB), Beijing Normal University (CN), University of Manchester (GB), Beijing Institute of Big Data Research (CN)
Openalex Percentile: Top 43%
Online Learning and Analytics
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