Exploring AI Literacy and AI‐Induced Emotions among Chinese University English Language Teachers: The Partial Least Square Structural Equation Modeling (PLS‐SEM) Approach

As smart technology promotes the development of various industries, artificial intelligence (AI) has also become an important driving force for innovation and transformation in education. For teachers, how to skillfully apply AI in teaching and improve their AI literacy has become a necessary goal for their sustainable professional development. This research examines the correlations among the dimensions of AI literacy of teachers in order to promote the effectiveness of class teaching and the adoption of artificial intelligence literacy (AIL). Our findings are based on the analysis of 1013 survey results, where we tested the level of AI literacy of teachers, including Knowing and Understanding AI (KUAI), Applying AI (AAI), Evaluating AI Application (EAIA), and AI Ethics (AIE). We find that AAI had a significant, positive effect on the other three dimensions. Thus, based on the analysis, the government should take action to cultivate teachers’ AI literacy. In order to improve teachers’ AI literacy, the choice of curriculum, content, methods, and practical resources for special training should be diverse and committed to making AI literacy an essential enabler for teachers’ sustainable future development.

This study addresses the need for context-specific Artificial Intelligence (AI) literacy research in West Africa, confronting challenges such as interrelation of AI literacy dimensions, ethical concerns, and a scarcity of localized studies. It investigates AI literacy among university students in Ghana and Nigeria through a quantitative cross-sectional survey of 427 participants (n = 206 Ghana, n = 221 Nigeria). The investigation focuses on four interconnected dimensions from the ABCE framework: Affective (motivation, self-efficacy), representing emotional engagement with AI; Behavioral (collaboration, intentional use), reflecting active participation in AI-related tasks; Cognitive (knowledge, critical thinking), encompassing understanding and application of AI concepts; and Ethical, pertaining to awareness and commitment to AI's societal implications. Using partial least squares structural equation modeling (PLS-SEM), findings confirm that affective factors positively influence cognitive outcomes, mediated by behavioral engagement and ethical understanding. Notably, country differences do not significantly affect these relationships, thereby justifying the analysis of the combined dataset and highlighting shared patterns in AI literacy development across the two contexts. This consistency validates a common underlying mechanism for AI literacy development in these West African contexts. The study shows the importance of integrating technical AI skills with ethical principles, collaborative learning, and culturally appropriate strategies. Specifically, it offers actionable strategies for strengthening affective learning, designing collaborative behavioral interventions, embedding ethical reasoning into curricula, and contextualizing pedagogies for regional realities, thereby informing stakeholders on effective AI education in West Africa.

A critical review of teaching and learning artificial intelligence (AI) literacy: Developing an intelligence-based AI literacy framework for primary school education

As smart technology propels the advancement of various sectors, artificial intelligence (AI) has emerged as a pivotal driver of innovation and transformation in education. For educators, proficiency in AI and the enhancement of AI literacy are crucial for professional development. This research investigates the interrelationships among different facets of AI literacy to augment classroom teaching efficacy and foster the adoption of AI literacy. An analysis of 1013 survey responses forms the basis of our findings, concentrating on educators’ AI literacy levels, encompassing Knowing and Understanding AI (KUAI), Applying AI (AAI), Evaluating AI Application (EAIA), and AI Ethics (AIE). The results indicate that AAI significantly and positively influences the other three dimensions. Consequently, it is imperative for governments to implement measures aimed at enhancing teachers’ AI literacy (Bjola, 2022). To facilitate this development, diverse curricula, comprehensive content, innovative methods, and practical resources for specialized training must be provided, positioning AI literacy as a critical enabler for the sustainable professional growth of educators.

This study aims to develop a comprehensive competency framework for artificial intelligence (AI) literacy, delineating essential competencies and sub‐competencies. This framework and its potential variations, tailored to different learner groups (by educational level and discipline), can serve as a crucial reference for designing and implementing AI curricula. However, the research on AI literacy by target learners is still in its infancy, and the findings of several existing studies provide inconsistent guidelines for educational practices. Following the 2020 PRISMA guidelines, we searched the Web of Science, Scopus, and ScienceDirect databases to identify relevant studies published between January 2012 and October 2024. The quality of the included studies was evaluated using QualSyst. A total of 29 studies were identified, and their research findings were synthesized. Results show that at the K‐12 level, the required competencies include basic AI knowledge, device usage, and AI ethics. For higher education, the focus shifts to understanding data and algorithms, problem‐solving, and career‐related competencies. For general workforce, emphasis is placed on the interpretation and utilization of data and AI tools for specific careers, along with error detection and AI‐based decision‐making. This study connects the progression of specific learning objectives, which should be intensively addressed at each stage, to propose an AI literacy education pathway. We discuss the findings, potentials, and limitations of the derived competency framework for AI literacy, including its theoretical and practical implications and future research suggestions. Practitioner notesWhat is already known about this topic AI literacy is becoming increasingly important as AI technologies are integrated into various aspects of life and work. Research on AI literacy competencies across diverse learner groups and disciplines remains fragmented and inconsistent to guide educational practices. Studies providing a coherent pathway for AI literacy development throughout educational and working life are lacking. What this paper adds A comprehensive AI literacy competency framework consisting of 8 competencies and 18 sub‐competencies. Variations in AI literacy competencies with tailored configuration and prioritization across different learner groups by school levels and disciplines. A proposed pathway for developing AI literacy from K‐12 to higher education and workforce levels. Implications for practice and policy The framework can guide the design and implementation of AI curricula tailored to different learner characteristics and needs. Education should shift focus from teaching how to use AI to fostering competencies for critical, strategic, responsible and ethical integration of AI. Policies are needed to support a systematic pathway for lifelong AI literacy development from K‐12 education to workforce training.

In the same way that smart technology is boosting growth in many different sectors, artificial intelligence (AI) is becoming a major factor boosting change and innovation in the educational system. Improving one's AI literacy and learning how to effectively incorporate AI into the classroom are now essential objectives for educators seeking long-term success in their careers. In an effort to increase the efficacy of classroom instruction and the widespread use of AI, this study investigates the relationships between several aspects of teachers' AI literacy. Our research is based on an examination of 280 survey responses that assessed instructors' AI literacy in four areas: AI understanding and knowledge, AI application, AI evaluation, and AI ethics. All three of these other variables were positively and significantly impacted by AI Application (AAI). The findings suggest that the government should support initiatives that increase educators' knowledge of artificial intelligence. Making AI literacy a crucial enabler for teachers' sustainable future development requires a broad curriculum, material, techniques, and practical support for special training that aims to promote teachers' AI literacy.

This study examines the influence of digital trust and Artificial Intelligence (AI) literacy on the sustainability of micro, small, and medium-sized enterprises (MSMEs), with innovation capability serving as a mediating variable. As key drivers of Indonesia’s economy, MSMEs continue to face challenges such as uneven digital adoption and limited AI comprehension, both of which hinder long-term competitiveness. This research adopts a quantitative explanatory design, using survey data from 120 MSMEs in Surabaya and Malang, analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that the model explains 77.4% of the variance in innovation capability and 82.2% of the variance in business sustainability, with a strong predictive level. Innovation capability was found to mediate 34.1% of the influence of digital trust and 28.4% of the influence of AI literacy on sustainability. These findings confirm that digital trust and AI literacy play an important role in driving innovation, which in turn enhances business competitiveness and resilience. This research enriches the technology acceptance model and dynamic capability framework by incorporating digital trust and AI literacy into the study of MSMEs' sustainability. Beyond its academic contribution, the study also provides empirical evidence from the underexplored Indonesian MSME, while offering practical recommendations for policymakers and business practitioners to strengthen digital trust, enhance AI competencies, and design innovation-based strategies that support sustainable growth.

The latest developments in artificial intelligence (AI) have paved the way for new AI-integrated pedagogical skills and competencies in language teaching. AI literacy and AI competency are related terms; however, they differ in educational contexts. AI literacy refers to the skills to understand and evaluate AI technologies, as well as to engage with them. In contrast, AI competency extends beyond conceptual proficiency and is related to the practical application of AI knowledge in educational domains. Considering the significance of effective and ethical AI use in language teaching, this study focused on the role of AI literacy training in teacher education programs and aimed to investigate pre-service EFL teachers’ perceptions of their AI literacy and competency as well as their experiences regarding AI literacy training in EFL teacher education programs. A small-scale exploratory approach was adopted in this qualitative study, which was conducted at the English Language Teaching Department at a state university in Türkiye. The participants were 15 pre-service EFL teachers. Semi-structured interviews were utilized to gather qualitative data, which was analyzed using thematic analysis. The study results revealed that pre-service EFL teachers’ perceptions of AI use in language teaching were positive; however, they were unconfident about their AI knowledge and skills. Also, the study enlightened a lack of exposure to AI education, including AI knowledge, practices, and ethical issues in EFL teacher education programs. Based on pre-service EFL teachers’ needs and expectations, the results of this study may provide beneficial insights for teacher educators, practitioners, and policymakers.

This study investigates the evolving landscape of Artificial Intelligence (AI) literacy, acknowledging AI's transformative impact across various sectors in the twenty-first century. Starting from AI's inception to its current pervasive role in education, everyday life, and beyond, this paper explores the relevance and complexity of AI literacy in the modern world. To evaluate the current state of the literature on AI literacy, a systematic literature review was conducted with the objective of identifying thematic and recent research trends. Through a rigorous selection process involving 323 records from databases such as Web of Science, SCOPUS, ERIC, and IEEE Xplore, 87 high-quality studies have been analysed to identify central themes and definitions related to AI literacy. Our findings reveal that AI literacy extends beyond technical proficiency to encompass ethical considerations, societal impacts, and practical applications. Key themes identified include the ethical and social implications of AI, AI literacy in K-12 education, AI literacy curriculum development, and the integration of AI in education and workplaces. The study also highlights the importance of AI literacy models and frameworks for structuring education across diverse learning environments, as well as the significance of AI and digital interaction literacy. Additionally, our analysis of publication trends indicates a strong growth in AI literacy research, particularly in China and the United States, reflecting the global urgency of addressing AI literacy in policy and education. Conclusively, the research underscores the importance of an adaptable, comprehensive educational paradigm that incorporates AI literacy, reflecting its diverse interpretations and the dynamic nature of AI. The study advocates for interdisciplinary collaboration in developing AI literacy programs, emphasizing the need to equip future generations with the knowledge, skills, and ethical discernment to navigate an increasingly AI-driven world.

Examining artificial intelligence literacy among pre-service teachers for future classrooms

Many researchers have started using artificial intelligence (AI) tools in the different parts of their scientific research processes. A high level of AI literacy is required to utilize AI tools effectively. AI literacy also includes many cognitive and technical skills. These pre-requisite skills can impact researchers’ AI usage competencies. In this context, the current study aimed to investigate the determinants of researchers’ AI literacy from the demographic variables and twenty-first century skills. For this purpose, a model was created with 24 hypotheses and tested using data collected by 708 researchers from various universities in Türkiye. Non-experts AI literacy, digital literacy, data literacy, and computational thinking scales, and demographic information form were used as data collection tools. Structural Equation Modelling (SEM) was used to reveal the relationship among the variables. The results revealed that digital literacy and data literacy skills are the strongest predictors of AI literacy. In addition, digital literacy and data literacy have mediating roles between the some 21st skills and AI literacy. There are also some demographic variables such as English language level and frequency of AI use, which are the significant predictors of AI literacy.

Artificial intelligence (AI) literacy plays a significant role in effectively communicating with AI generative chatbots for seeking health information or advice, as well as communicating with healthcare professionals and public health officials. The study aims to identify the factors influencing AI literacy and its subsequent influence on consumers’ behavioral intentions toward seeking health information using generative AI chatbots. A nine-factor measurement model (INS, ISE, IFO, AIH, ANX, AVD, AIL, BI, AIC) was developed with the help of the Theory of Planned Behavior (TPB) and tested using structural equation modeling. The results of SEM analysis demonstrate that IFO (β = 0.138, CR = 2.731, p < .05), ANX (β = 0.175, CR = 3.103, p < .05), and AVD (β = 0.158, CR = 3.855, p < .05) had a statistically positive significant influence on AI literacy. Similarly, BI (β = 0.128, CR = 2.423, p < .05) had a statistically positive significant influence on AIC. A positive but non-significant effect of INS on AIL was also observed (β=0.058, CR = 1.006, p > .05). AIL also had a positive but non-significant effect on BI (β=0.050, CR = 1.598, p > .05). Additionally, the results of model fit indices show adequate model fit values: χ2 = 1.679 DF = 1867; p = .000; IFI = 0.954; TLI = 0.951; CFI = 0.953; RMSEA = 0.031. The findings conclude that information overload, anxiety, and avoidance significantly influence AI literacy. On the other hand, information source exposure, AI hallucination, and information seeking do not significantly influence consumers’ AI literacy level. The study suggests that health professionals and staff could be equipped with better knowledge and skills related to AI applications in healthcare settings.

Artificial Intelligence (AI) is transforming education, workforce development, and daily life, necessitating a comprehensive understanding of AI literacy. This study explores the dimensions of AI literacy, its integration into educational and professional settings, and the challenges associated with its implementation. Using a systematic review and qualitative synthesis, this study examines research published between 2019 and 2024, identifying six key dimensions of AI literacy: technical literacy, ethical and societal awareness, critical AI literacy, AI in everyday life, human-AI collaboration, and AI pedagogical literacy. Findings indicate that AI literacy is increasingly embedded in K-12 education, higher education, and workforce training, though disparities in accessibility, ethical concerns, and inconsistent policies persist. Key challenges include the digital divide, lack of teacher training, and lack of standardized AI literacy assessment tools. Opportunities lie in interdisciplinary learning, project-based education, and AI-driven adaptive learning environments. This study makes several unique contributions, including a comprehensive framework for AI literacy, integration of AI literacy across education and workforce domains, identification of policy gaps, and a call for standardized AI literacy assessment tools. It also emphasizes the need for ethical AI engagement and responsible AI education. Policymakers and educators should prioritize integrating AI literacy into curricula, professional development for teachers, and establishing regulatory frameworks to ensure equitable AI education. Future research should focus on longitudinal studies, cross-cultural AI literacy comparisons, and developing adaptive AI learning models to enhance AI education globally.

Artificial intelligence (AI) literacy is at the top of the agenda for education today in developing learners' AI knowledge, skills, attitudes and values in the 21st century. However, there are few validated research instruments for educators to examine how secondary students develop and perceive their learning outcomes. After reviewing the literature on AI literacy questionnaires, we categorized the identified competencies in four dimensions: (1) affective learning (intrinsic motivation and self‐efficacy/confidence), (2) behavioural learning (behavioural commitment and collaboration), (3) cognitive learning (know and understand; apply, evaluate and create) and (4) ethical learning. Then, a 32‐item self‐reported questionnaire on AI literacy (AILQ) was developed and validated to measure students' literacy development in the four dimensions. The design and validation of AILQ were examined through theoretical review, expert judgement, interview, pilot study and first‐ and second‐order confirmatory factor analysis. This article reports the findings of a pilot study using a preliminary version of the AILQ among 363 secondary school students in Hong Kong to analyse the psychometric properties of the instrument. Results indicated a four‐factor structure of the AILQ and revealed good reliability and validity. The AILQ is recommended as a reliable measurement scale for assessing how secondary students foster their AI literacy and inform better instructional design based on the proposed affective, behavioural, cognitive and ethical (ABCE) learning framework. Practitioner notesWhat is already known about this topic AI literacy has drawn increasing attention in recent years and has been identified as an important digital literacy. Schools and universities around the world started to incorporate AI into their curriculum to foster young learners' AI literacy. Some studies have worked to design suitable measurement tools, especially questionnaires, to examine students' learning outcomes in AI learning programmes. What this paper adds Develops an AI literacy questionnaire (AILQ) to evaluate students' literacy development in terms of affective, behavioural, cognitive and ethical (ABCE) dimensions. Proposes a parsimonious model based on the ABCE framework and addresses a skill set of AI literacy. Implications for practice and/or policy Researchers are able to use the AILQ as a guide to measure students' AI literacy. Practitioners are able to use the AILQ to assess students' AI literacy development.

BACKGROUND Artificial intelligence (AI) literacy is increasingly essential for medical students. However, without systematic characterization of the subsidiary components and relevant drivers, designing targeted medical education interventions may be challenging. OBJECTIVE Systematically describe (1) the levels of and (2) the drivers of multidimensional AI literacy among Chinese medical students. METHODS A cross-sectional, descriptive analysis was conducted using data from a nationwide survey of Chinese medical students (n = 80,335) across 109 medical schools in 2024. AI literacy was assessed with a multidimensional instrument comprising three domains: knowledge, evaluating students’ self-reported proficiency in core areas of medical AI applications; attitude, reflecting their views on using AI for teaching and learning; and behavior, capturing the frequency and patterns of AI use. Factors associated with AI literacy included individual factors (i.e., demographic characteristics, family background, and enrollment motivation) and environmental factors (i.e., educational phase, type of education program, and tier of education program). RESULTS Respondents showed moderate to high levels of AI knowledge (mean, 76.0 [SD, 26.9]), followed by moderate AI attitude scores (mean, 71.6 [SD, 24.4]). In contrast, AI behavior scores were much lower (mean, 32.5 [SD, 28.5]), indicating little usage of AI tools. Of the individual factors, male students reported higher levels of AI attitude and behavior; both intrinsic and extrinsic motivation were positively associated with all three dimensions; advantaged family background was positively related to AI attitude and behavior, but not knowledge. Among the environmental factors, attending prestigious Double First-Class universities was positively associated with higher AI usage. Enrollment in long-track medical education programs was associated with higher AI attitude and behavior, while being in the clinical phase was negatively associated with both AI knowledge and behavior. Environmental factors moderated the associations between individual characteristics and AI literacy, potentially attenuating disparities. CONCLUSIONS Medical students reported moderate to high AI knowledge, moderate AI favorability, and low AI use. Individual characteristics and environmental factors were significantly associated with AI literacy, and environmental factors moderated the associations. The moderate AI literacy overall highlights the need for AI-related medical education, ideally with practical use and nuanced by drivers of inequitable distribution. CLINICALTRIAL This study is a cross-sectional observational analysis and does not involve a clinical trial; therefore, trial registration is not applicable.