https://academic-publishing.org/index.php/ejel <p><strong>The Electronic Journal of e-Learning (EJEL)</strong> is an open access journal that provides pedagogical, learning and educational perspectives on topics relevant to the study, implementation and management of e-learning initiatives. EJEL has published regular issues since 2003 and averages between 5 and 6 issues a year.<br /><br />The journal contributes to the development of both theory and practice in the field of e-learning. The Editorial team consider academically robust papers and welcome empirical research, case studies, action research, theoretical discussions, literature reviews and other work which advances learning in this field. All papers are double-blind peer reviewed.</p> Academic Publishing International en-US Electronic Journal of e-Learning 1479-4403 <p><strong>Open Access Publishing</strong></p> <p>The Electronic Journal of e-Learning operates an Open Access Policy. This means that users can read, download, copy, distribute, print, search, or link to the <em>full texts</em> of articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, is that authors control the integrity of their work, which should be properly acknowledged and cited.</p> https://academic-publishing.org/index.php/ejel/article/view/3990 <p>Artificial intelligence (AI) is having a significant impact on contemporary lives, especially in learning and instruction design. The exploration of AI literacy in teacher education is an essential foundation for the redesign of instructional approaches to enhance pre-service teachers’ AI literacy. This study aimed to develop the scale of AI literacy by employing exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to develop the items and validate a self-assessment AI literacy scale for pre-service teachers for practical implementation for teacher development courses in undergraduate curricula. In this study, AI literacy, synthesized from relevant studies and drawing on experts in educational technology, includes four constructs: 1) recognition, 2) fundamental comprehension, 3) pedagogy, and 4) ethical use of AI, offering a comprehensive and versatile instrument for the measurement of AI literacy in teaching professional development. The instrument’s reliability and construct validity were confirmed using statistical analyses of data collected from 1,673 undergraduate pre-service students studying teaching and education at Thai universities, including both public and private universities. The findings indicated that the four constructs proposed had a good fit and showed excellent internal consistency (α = 0.94). The average variance extracted, and composite reliability (CR) values met the criteria for validity. In the EFA, the items were reduced from 42 items to 39 items, which had a Kaiser-Meyer-Olkin of 0.993 and a significant test of sphericity (p-value &lt; .0001). The CFA results revealed a chi-Square value of 480 (p &lt; 0.001), an RMSEA of 0.035, an SRMR of 0.022, a comparative fit index (CFI) of 0.974, and a GFI of 0.974. Thus, the AI literacy scale for pre-service teachers developed in this study is a valid and reliable instrument for assessing pre-service teachers’ AI literacy. Although it was not yet implemented in classroom settings, the established validity and reliability of the scale provide a foundation for future research and practical applications in teacher education.</p> Pawarit Pingmuang Prakob Koraneekij Jintavee Khlaisang Copyright (c) 2025 Pawarit Pingmuang, Prakob Koraneekij , Jintavee Khlaisang https://creativecommons.org/licenses/by/4.0 2025-12-09 2025-12-09 24 1 01 18 10.34190/ejel.24.1.3990