Literacy is embedded in social practices that adopt socially constructed technologies. In the 20th century, the invention of the computer facilitated information literacy and computational thinking. Media literacy emerged alongside the development of media. In the 21st century, the accessibility of artificial intelligence (AI)-powered chatbots using conversational language has expanded significantly, creating a need for their integration in education. AI literacy is being studied within frameworks similar to those of its predecessors, requiring conceptualization and differentiation from related literacy concepts. As of 2025, teacher education regarding AI literacy has been identified as an under-researched area. This article examines AI literacy through the lens of pre-service teachers’ literacy competencies that will shape the literacy practices of future generations. During their own schooling, the current generation of prospective teachers encountered information, digital, and media literacy, as well as computational thinking; these are analyzed here as predictors of AI literacy, measured using the scale for the assessment of non-experts’ artificial intelligence literacy (SNAIL) instrument. Personality traits and mindset are also investigated as predictors of the prospective teachers’ professional competence. The study was conducted in 2024/25 with a sample of pre-service teachers. The research questions are: What is the structure of AI literacy in the sample? To what extent can AI literacy be predicted by components of information literacy? How much of AI literacy can be explained by various established technology-related literacies? To what extent do personality and mindset contribute to predicting AI literacy? The findings confirmed a two-factor solution for SNAIL, comprising technical understanding and critical application. Student perception of information literacy skills proved accurate in predicting the critical application of AI. In addition to information literacy, predictors included the media production subscale of media literacy and mindset. Technical understanding of AI was predicted by the media production subscale, the computational thinking subscales algorithmic thinking and cooperativity, and the personality trait of agreeableness. Establishing AI literacy as an essential teacher competency requires thorough research into AI’s educational impacts and strict alignment with pedagogical goals. Navigating the ethical and moral dimensions of AI usage provides the necessary foundation for this literacy. Consequently, training programs can intentionally foster AI competence by merging specialized literacy modules with algorithmic thinking, media production, and growth-mindset interventions.
Generative artificial intelligence (GenAI) recently appeared as an issue of its time which has been changing the educational landscape at a considerable rate and opening new pathways and challenges for educators and learners. Therefore, in terms of GenAI for education this bibliometric analysis will characterize the change and development by mapping trends and patterns, examining prevalent subject areas in academia interest. Following the established criteria, we ran a systematic search on Scopus database with the identification and analysis of 121 peer-reviewed documents (journal articles, review articles and conference papers; selection reported with reference to the PRISMA 2020 guideline) published between January 2023 and March 2025 related to GenAI applied in education. The thematic labels attached to the algorithmically generated VOSviewer clusters were assigned by the researchers, and interpretations of regional patterns are presented as tentative. The article features papers discussing ways to use GenAI in learner-centered teaching, curriculum development and assessment. The findings reveal a rapid increase in research output between 2023 (23 articles) and 2024 (67 articles), followed by 31 articles in the first quarter of 2025, and substantial international collaboration (34% of the articles). Moreover, the thematic analysis shows that pedagogical integration and instructional design (55.4% of the articles), assessment and academic integrity (44.6%) and ethical and societal implications (39.7%) are the most prominent themes, with a shift from ethical and assessment concerns in 2023 towards pedagogical integration and learning outcomes in 2024-2025. Research affiliated with the United States, the United Kingdom, China and Germany dominates the corpus, whereas research from Latin America and Africa appears under-represented. The review proposes future directions of research and spells out an array of issues that have until now gone unexamined by helping the reader enter the intellectual landscape of GenAI in education. The bibliometric evidence indicates growing scholarly attention to the potential of GenAI in education; this study did not appraise the quality of the evidence on learning outcomes. Nonetheless, designers are being asked to collectively conduct further research to address any potential negative effects of the technology, and create useful design frameworks, to implement this technology into the education system worldwide, without compromising on justice and morality.
This study investigates how a structured “paper-first” approach can support cognitive engagement during artificial intelligence (AI)-assisted academic work in higher education. Growing reliance on generative AI has increased concerns about surface-level learning, trial-and-error prompting, and reduced student ownership of academic writing. Using a two-year design-based research approach involving nine iterative cycles, the study developed and refined a framework that required students to construct manual logic diagrams before engaging with AI tools and expert system environments. Participants included a six-member interdisciplinary design team and 150 communication for academic purposes students from the faculty of information and communications technology at a South African university. Data were collected through iterative observations and focus group interviews and analyzed using grounded theory and constant comparative analysis. Findings indicate that the framework supported more structured reasoning, reduced inefficient prompting behaviors, and improved students’ ability to identify weaknesses in their own arguments before digital interaction. Students also reported increased confidence and greater ownership of their academic work. The study argues that structured pre-AI planning activities can strengthen critical thinking and academic literacy by positioning AI as a support tool rather than a substitute for cognitive effort. The findings contribute practical guidance for educators seeking to integrate AI into higher education while preserving student agency and deep learning.
The rapid diffusion of generative artificial intelligence (GenAI), particularly large language models such as ChatGPT, has reshaped academic workflows while raising questions about trust, misconceptions, and future expectations. This study examines an integrated framework grounded in the unified theory of acceptance and use of technology and the technology acceptance model to assess how foundational acceptance drivers, cognitive evaluations, misconceptions, and trust are associated with academics’ forward-looking expectations of ChatGPT. Survey data from 252 academics in Malaysian public and private universities were analyzed using partial least squares structural equation modelling. Performance expectancy, effort expectancy, and facilitating conditions were positively associated with perceived ease of use (PEOU) or perceived usefulness (PU), while PEOU was positively associated with trust. Social influence was not significantly associated with PU. Two unexpected patterns were observed: PU was positively associated with misconceptions, and misconceptions were positively associated with trust. These cross-sectional associations suggest that functional appreciation can coexist with inflated capability beliefs and potentially miscalibrated trust, but they do not establish a causal mechanism of belief amplification. The Malaysian setting is analytically relevant because higher education institutions are expanding digitalization while national initiatives increasingly emphasize responsible AI governance, institutional capability, and digital skills. The findings highlight the importance of AI literacy, transparent institutional guidance, verification practices, and calibrated reliance when integrating GenAI into higher education.
In contemporary digital society, artificial intelligence literacy (AI literacy) has become a key competence that enables citizens to understand and actively participate in increasingly automated environments. However, despite the rapid growth of scientific output, partly driven by tools such as ChatGPT, the literature reveals substantial conceptual fragmentation that hinders an integrated understanding of its educational and ethical dimensions. In this context, this study aimed to analyze the intellectual evolution and conceptual structure of research on AI literacy, based on Scopus-indexed literature. A bibliometric methodology was applied to a corpus of 2,645 documents using scientific mapping techniques to identify publication patterns, collaboration networks, and dominant thematic streams. The results reveal an accelerated expansion of the field and a shift from early approaches focused primarily on educational contexts such as K-12 education and computational thinking toward more complex frameworks that incorporate algorithmic ethics, psychological acceptance of technology, critical thinking, and academic integrity challenges associated with generative AI (GenAI). Overall, this study contributes to the educational technology literature by providing a longitudinal bibliometric interpretation of the evolution of AI literacy and by identifying emerging research agendas related to GenAI, ethical reasoning, and critical digital competencies in contemporary education.
