Article 2026

Development and validation of Generative AI Competence Scale (GenAIComp) among university students

Technology in Society
Journal · Vol. 84 · Art. 103059
Abstract

The rapid development of Generative Artificial Intelligence (Generative AI) across several sectors underscores the need for a systematic tool to evaluate AI competence. Current digital literacy frameworks lack AI-specific competencies, resulting in inconsistencies in the assessment of AI competence. This study aims to establish a standardized assessment framework for Generative AI competence by identifying key skill factors and empirically validating a structured evaluation tool called the Generative AI Competence Scale (GenAIComp). The proposed GenAIComp has five essential factors: Information and Data Literacy, Communication and Collaboration, Digital Content Creation, Safety and Ethics, and Problem-Solving. A quantitative approach was employed, incorporating expert validation, pilot testing, and extensive empirical evaluation involving 1000 participants, principally university students. The factor analysis confirmed a robust 5-factor structure with strong psychometric properties. The final model demonstrated excellent fit indices, confirming its reliability and validity in assessing Generative AI competence across the five key factors. Research demonstrates that educational background considerably impacts AI competence, with individuals from technical disciplines showing a greater aptitude for problem-solving and content generation. Gender-based disparities were noted, with males achieving marginally higher scores in several factors, but with minimal effect sizes. Correlation analysis indicated that perceived AI expertise and frequency of AI utilization significantly influenced competence, especially in data literacy and problem-solving, and exhibited less correlation with ethical awareness. GenAIComp provides a reliable tool for assessing AI competence, helping educators, industry experts, and policymakers to design AI training programs and integrate AI literacy into curricula and thereby AI technology advancement in society. Future research should explore its applicability across cultures and include performance-based assessments to enhance AI competence. © 2025 Elsevier Ltd

Keywords

Author Keywords

AI competence Digital literacy GenAIComp Generative AI

Index Keywords

Curricula Engineering education Personnel training training Students Ethical aspects Factor analysis Problem solving 'current AI competence Communication and collaborations Digital literacies Evaluation tool Generative AI competence scale Generative artificial intelligence Problem-solving Structured evaluation University students artificial intelligence correlation curriculum educational development ethics identification method information and communication technology literacy policy making student technological development technology adoption university sector
Author Affiliations
Department of Human-Computer Interaction, Hanyang University ERICA Campus, Ansan, Gyeonggi-do, South Korea
Research Institute of Engineering and Technology, Hanyang University ERICA Campus, Ansan, Gyeonggi-do, South Korea
Department of Industrial Engineering, Thammasat School of Engineering, Klong Luang, Pathum Thani, Thailand
Department of Safety Engineering, Pukyong National University, Busan, South Korea
Department of Industrial and Systems Engineering, Gyeongsang National University, Jinju, Gyeongsangnam-do, South Korea
Division of Media Communication, Hankuk University of Foreign Studies, Seoul, South Korea
Department of Safety Engineering, Seoul National University of Science and Technology, Seoul, South Korea
Funding & Acknowledgements
Ministry of Education, MOE
Funding text 1: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2022S1A5A8051103).; Funding text 2: This work was supported by Hankuk University of Foreign Studies Research Fund (2025).; Funding text 3: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2022S1A5A8051103). This work was supported by Hankuk University of Foreign Studies Research Fund (2025).
Hankuk University of Foreign Studies, HUFS
Funding text 1: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2022S1A5A8051103).; Funding text 2: This work was supported by Hankuk University of Foreign Studies Research Fund (2025).; Funding text 3: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2022S1A5A8051103). This work was supported by Hankuk University of Foreign Studies Research Fund (2025).
National Research Foundation of Korea, NRF
Grant: NRF-2022S1A5A8051103
Funding text 1: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2022S1A5A8051103).; Funding text 2: This work was supported by Hankuk University of Foreign Studies Research Fund (2025).; Funding text 3: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2022S1A5A8051103). This work was supported by Hankuk University of Foreign Studies Research Fund (2025).
National Research Foundation of Korea, NRF
Funding text 1: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2022S1A5A8051103).; Funding text 2: This work was supported by Hankuk University of Foreign Studies Research Fund (2025).; Funding text 3: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2022S1A5A8051103). This work was supported by Hankuk University of Foreign Studies Research Fund (2025).
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