미래 예비교사의 AI 리터러시와 교수 효능감 발달을 위한 교육청 캠프 운영 사례 연구

This study explored university students’ perceptions of artificial intelligence (AI) literacy and AI education. Specifically, the authors sought to understand the level of AI literacy among university students, the extent of their prior exposure to AI education, and the factors that affect their AI education. The study was conducted through an online survey distributed among 300 university students at a Korean university. The study findings indicate that most university students are interested in learning AI. Moreover, the results showed that students with prior experience with software (SW) education showed superior knowledge, a better grasp of AI concepts, and more confidence in using AI technologies, especially in computer coding skills. On the other hand, students with less exposure to prior AI education expressed a need for more learning opportunities and sufficient knowledge of computer coding skills. In addition, we found that students who had received SW education during their K-12 schooling showed better math skills than those who had only received short-term training in university. These findings underscore the importance of addressing the educational barriers that impede university students’ ability to harness this interest effectively. In conclusion, our study provides valuable insights into university students’ perceptions regarding AI literacy and the factors that affect their AI education.

Artificial intelligence (AI) literacy is a crucial aspect of media and information literacy (MIL), regarded not only as a human right but also as a fundamental requirement for societal advancement and stability. This study aimed to provide a comprehensive, cross-border perspective on AI literacy levels by surveying 1,800 university students from four Asian and African nations. The findings revealed significant disparities in AI literacy levels based on nationality, scientific specialization, and academic degrees, while age and gender did not show notable impacts. Malaysian participants scored significantly higher on the AI literacy scale than individuals from other countries. The results indicated that various demographic and academic factors influenced respondents’ perceptions of AI and their inclination to utilize it. Nationality and academic degree were identified as the most influential factors, followed by scientific specialization, with age and gender exerting a lesser influence. The study highlights the necessity of focusing research efforts on the detailed dimensions of the AI literacy scale and examining the effects of previously untested intervening variables. Additionally, it advocates for assessing AI literacy levels across different societal segments and developing the appropriate measurements.

Artificial intelligence (AI) and algorithmic systems influence health workers' access, interpretation, and action on clinical and public health information, positioning them as intermediaries between algorithmically mediated outputs and patients, communities, and decision makers. This study examines how AI and algorithmic literacy are conceptualized and measured among health workers through a digital health literacy (DHL) lens. Using Arksey and O'Malley's scoping review framework, we searched Ovid MEDLINE, Ovid Embase, Scopus, IEEE Xplore, ACM Digital Library, Europe PMC, and arXiv for English language sources published between January 2020 and May 2025. Two reviewers screened records and extracted data using a theory informed charting framework grounded in Nutbeam's model (functional: basic understanding and use; critical: evaluation and ethics; communicative: interacting with AI systems and explaining AI-mediated information). We synthesized findings using descriptive statistics and a narrative synthesis. Twelve studies published between 2021 and 2025 met inclusion criteria. Evidence was concentrated in health professions education (10/12), primarily among medical (6/12) and nursing students (2/12), with no studies exploring public health practice. Explicit, theory-grounded definitions of AI literacy were uncommon, and links to DHL were only implied. AI literacy was frequently operationalized through self-reported instruments, commonly the Artificial Intelligence Literacy Scale (AILS; 3 studies), Meta Artificial Intelligence Literacy Scale (MAILS; 2 studies) and the Scale for the Assessment of Non-Experts' AI Literacy (SNAIL), alongside self-developed tools. Only one study explicitly defined and measured algorithmic literacy as a distinct construct; in other studies, algorithmic considerations appeared indirectly through recognizing AI presence in systems or evaluating AI generated content. Across studies, competencies aligned mainly with functional and critical dimensions of DHL, particularly awareness, use, evaluation, and ethics, while communicative literacies were infrequently assessed. AI and algorithmic literacy among health workers is underdeveloped, weakly integrated with digital health literacy, and inconsistently measured. Research prioritizes AI literacy using non-health-specific self-report tools and largely overlooks communicative competencies essential to clinical and public health practice. These findings point to the need for clearer conceptual alignment, health-specific measurement, and systems-based approaches to workforce readiness as AI-enabled tools expand across healthcare and public health.

The rapid integration of artificial intelligence (AI) technologies into academic and professional practice has profoundly shaped the communication and public relations domain. However, there is limited empirical understanding of how individuals actually perceive and use these new technologies. Therefore, this article investigates Communication and Public Relations (PR) students’ perceptions of artificial intelligence (AI), specifically aiming to understand how literacy, psychological factors, and trust influence their AI word-of-mouth (WOM). We proposed a theoretical model, which we tested using a quantitative approach with SmartPLS analysis on data gathered from 402 online questionnaires administered to students across three major Romanian universities. The key findings indicate that Internet use fosters AI literacy, which subsequently enhances both AI self-efficacy and the perceived ease of AI use. Crucially, higher AI literacy leads to greater trust, promoting informed choices. While AI self-efficacy encourages appropriate reliance on the technology and positively impacts behavioral intention toward AI, higher trust surprisingly leads to lower privacy concerns. Conversely, heightened privacy concerns increase algorithm aversion, which in turn negatively impacts both behavioral intention and WOM communication about AI. We also confirm that a positive behavioral intention is a strong predictor of increased WOM communication. These findings have significant implications for academia, policymakers, and PR practitioners by highlighting the necessity of boosting AI literacy to mitigate aversion and foster responsible AI adoption among future communication professionals.

A systematic review on how educators teach AI in K-12 education

A descriptive survey was conducted to investigate whether an investment in artificial intelligence (AI) literacy predicts the enhancement of educators’ skills for them to be effective in an AI powered environment in state primary schools in Nigeria. Three research questions and null hypotheses were formulated at .05 level of significance for the study. 1000 teachers were recruited from across five schools located in five geopolitical zones of the country using cluster sampling. Artificial intelligence literacy and educators’ skills enhancement questionnaire (AILESEQ) was deployed to generate data. Based on the human capital development theory, data was analysed via the simple linear regression. Findings suggest that knowledge of AI, use of AI devices and understanding the ethical issues about AI technologies significantly predict the enhancement of educators’ skills in AI powered primary schools in Nigeria. It is recommended that government should organise training workshops on AI literacy for educators in schools; the policy on education needs review to produce a framework for the introduction of AI in education (AIIE) to benefit the teachers; the curriculum of teacher education has to be revised to feature AIIE; AI literacy should be a requirement for recruiting new teachers; schools have to produce guidelines for the application of AI devices; government can create a demonstration school to trial the AI initiative; government can commit one percent of value added tax to fund AIIE; awareness campaigns should be organised regularly via various media to educate relevant stakeholders about the benefits of AIIE.

Today, when digital transformation affects the field of social work, the integration of artificial intelligence technologies into professional practices gains importance. This study examines social work students' views on artificial intelligence with a phenomenological approach. The aim of the study is to determine students' perceptions of artificial intelligence, its potential role in professional practices and their concerns about ethics. The study included 14 third and fourth year social work students living in Bandırma. A semi-structured interview form was used as a data collection tool, and in-depth interviews lasted between 30-45 minutes. The analyses were carried out by thematic analysis method using MAXQDA software. In the findings, 8 sub-themes and 27 codes were identified under 3 main themes: 'Perceptions of Artificial Intelligence', 'Intersection of Social Work and Artificial Intelligence' and 'Artificial Intelligence Education and Access'. While students emphasise the benefits of artificial intelligence such as making life easier (f=10) and fast access (f=8), they have concerns about lack of empathy (f=8), privacy and data security (f=8). In addition, the integration of artificial intelligence into the curriculum (f=9) emerged as an important need. The results show that artificial intelligence in social work education should be positioned in line with professional values.

The research titled “digital and AI literacy in teacher training” seeks to bolster the professional training of future educators in all phases of teacher education within Germany, with a particular emphasis on integrating digital and artificial intelligence (AI) literacy into contemporary educational practices. Recognizing the escalating importance of digital competencies—an urgency that the COVID-19 pandemic underscored globally—this initiative establishes a cohesive framework connecting universities, seminar leaders, and schools. Its core objective is to enable student teachers to adopt and implement digital methodologies in the classroom while providing continuous, contextually relevant training for in-service educators. Through this interconnected structure, the research aims to bridge educational theory and practice. Methods: The research applies a Design-Based Research (DBR) methodology, facilitating a dynamic process in which educational tools and approaches are developed, tested, and refined in real-world settings. To assess efficacy, the research utilizes online questionnaires aligned with established digital competence frameworks, such as the European DigCompEdu model, enabling educators at all stages of teacher training to self-assess their digital and AI literacy skills. The geographical context of Bavaria in southern Germany is specifically referenced, where the research pilot takes place to set a scalable example for broader implementation. Findings: Preliminary evaluations reveal that the module-based structure effectively enhances participants’ digital competencies. Teacher candidates report a higher degree of readiness to implement digital teaching tools, collaborate effectively online, and navigate AI-related resources in classroom contexts. This reflects an overall improvement in digital confidence and capability, particularly in areas like content creation and pedagogical communication. Conclusions: The research’s structured approach, fostering institutional collaboration and phased integration of digital competencies, highlights an effective model for embedding AI and digital literacy in teacher education. Continuous assessments and feedback loops ensure its relevance across training stages, enabling educators to remain adaptive and responsive to new educational technologies. Ultimately, this model may serve as a blueprint for other regions and countries aiming to update and enhance their teacher training frameworks in response to digital transformation demands.

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

This study aims to investigate the artificial intelligence (AI) literacy levels of pre-service teachers and their views on the use of AI in education. Adopting a mixed-methods approach, the study employed both quantitative and qualitative techniques to ensure comprehensive analysis. The quantitative data were collected using the “Artificial Intelligence Literacy Scale,” while qualitative data were gathered through open-ended questions developed by the researchers. The sample consisted of 323 pre-service teachers from different departments and grade levels at Nigde Omer Halisdemir University Faculty of Education. Statistical analyses, including independent samples t-tests and one-way ANOVA, were used to examine differences in AI literacy levels based on variables such as gender, age, grade level, field of study, parental education level, and use of AI technologies. Content analysis was applied to the qualitative data. The findings revealed that pre-service teachers generally possess high levels of AI literacy. Significant differences were observed based on personal factors such as having AI applications, receiving technology-related education, and using AI in academic tasks. However, no significant differences were found for gender, age, or parental education level. Qualitative findings indicated that pre-service teachers mostly use AI tools for academic purposes, recognize their benefits in terms of time-saving and knowledge access, but also express ethical concerns and the need for critical awareness. The study highlights the importance of embedding AI literacy into teacher education programs to prepare future educators for the digital age.

Artificial Intelligence (AI) is increasingly prevalent, permeating various aspects of and spreading across education. However, a comprehensive understanding of AI applications and how to define AI literacy is under-investigated. On this note, teaching and evaluating AI literacy necessitating educators to integrate it in course content. The implementation and utilization of AI are reshaping college students’ experiences mannerly. A limited efforts have been made to promote artificial intelligence (AI) literacy for individuals at academic institutions. This study aims to investigate college instructors’ perspectives towards AI integration in curriculum design at a higher education institution, Palestine technical university Kadoorie (PTUK). A mixed study approach is utilized with a pre-existing questionnaire containing 14 items in this study and 17 semi-structured interviews. The data were collected from nine different departments at PTUK. The target population consisted of college 176. The results revealed that college instructors are perceiving AI literacy at a weak degree in the curriculum. Three themes merged from qualitative data: positive perspectives, negative perspectives, and willingness of integration of AI literacy across the curriculum. Recommendations include: there is a necessity to incorporate AI literacy curriculum design and in higher education context in different disciplines.

The accelerated integration of artificial intelligence (AI) across K–12 education demands that school librarians evolve into critical AI leaders. This international, mixed-methods exploratory study investigated the AI literacy, confidence, and readiness of 319 school librarians from over 50 countries, seeking to map their preparedness. Quantitative findings revealed intermediate AI literacy, yet instructional confidence and readiness remained constrained. Strong correlations existed between AI familiarity, literacy, and confidence (r=.75 to .77,p<.01). Crucially, statistical analysis identified that formal library and information science educational qualification was the only demographic factor significantly predicting higher AI literacy. Qualitative thematic analysis showed that this limited readiness is driven by four structural barriers: institutional and policy constraints; educator attitudes and knowledge gaps; time, resource, and professional development limitations; and student misuse and academic integrity. Drawing on this synthesis, the study validates and extends the AI Leadership Framework (Hossain, 2025b) as a developmental model for professional growth. The results mandate targeted policy reforms within LIS education and institutional support to empower school librarians—and potentially other educators— to lead the preparation of future citizens for an AI-augmented world.

Purpose The purpose of this study is to investigate generative artificial intelligence (AI) literacy integration within library and information science (LIS) educational programs, examining how academic curricula prepare future professionals to engage critically and competently with AI technologies. Design/methodology/approach A dual methodological approach combining a systematic scoping review of AI literature in LIS and a detailed analysis of syllabi from leading international LIS programs is used. The review identifies emerging trends and frameworks, while the syllabi analysis examines practical implementation. Findings The investigation reveals significant pedagogical fragmentation and global inequities in the integration of AI literacy in LIS programs, characterized by a lack of consensus around the frameworks used, institutional disparities and false dichotomies between technical and critical pedagogies. Research limitations/implications This study highlights the risks of stratification, where some programs offer sophisticated interdisciplinary training, while others provide only technical instruction, potentially fragmenting the global LIS community, at a time when coordinated responses to AI’s implications are crucial. Practical implications While many programs have implemented basic AI literacy, there is uneven global integration of critical AI literacy, which more closely aligns with the profession’s values of social justice and democratic well-being (ALA, 2023; IFLA, 2025). Social implications Research addresses the profession’s obligation to serve as ethical information stewards in an algorithmic age, emphasizing that graduates must leverage AI capabilities while critically examining implications for information equity, privacy and democratic participation (ALA, 2023; IFLA, 2025). Originality/value This research bridges the gap between conceptual developments in LIS research and practical educational implementation, providing the first comprehensive analysis of AI literacy operationalization across international LIS curricula.

Artificial intelligence (AI) technologies are advancing swiftly, both in terms of quantity and quality. Students must possess a solid understanding, abilities, and skills to successfully employ them. Therefore, the purpose of this quantitative study is to examine students’ AI literacy, including AI technological understanding, its practical application, critical appraisal, and its effects on students’ AI output quality, self-efficacy, and academic performance. Moreover, this research examines the effects of students’ AI self-efficacy and output quality on their academic achievement. An online survey was used to gather responses from 286 Austrian university students. Structural equation modeling was performed to test the research model. The findings indicate that AI technical understanding and the practical application significantly and positively influenced AI self-efficacy, while AI practical application had a significant and positive influence on both, AI self-efficacy and AI output quality. AI critical appraisal significantly and negatively impacted AI self-efficacy and AI output quality, whereas AI technical understanding, critical appraisal, practical application, self-efficacy, and output quality had an insignificant effect on students’ academic performance. The findings of this study may deepen the understanding of students’ AI literacy, contribute to the literature in this relatively new academic field, and help clarify the contradictory findings on the effects of AI-powered technology use on students’ learning outcomes. This research can also help integrate AI-powered tools into education effectively and improve students’ AI literacy so they can use them ethically and effectively. The results may also help revise and create new AI policies, curricula, and educational initiatives.

This research aims to explore the level of artificial intelligence (AI) literacy and ethical awareness among university students in Indonesia, focusing on their understanding of AI as well as the ethical challenges associated with its use in education and daily life. Using a mixed-methods approach, this study collected quantitative data through a survey involving 300 students from various universities in Indonesia and qualitative data through in-depth interviews with 30 selected respondents. The results show that although AI literacy among students from engineering majors is quite high, students from non-engineering majors show lower understanding. In addition, there are significant concerns regarding ethical issues such as data privacy, algorithmic bias, and the social impact of AI. The study also identified several key challenges, including the lack of integration of AI literacy in higher education curricula and limited training related to AI ethics. Based on these findings, this study recommends the need to strengthen a curriculum that includes AI literacy and comprehensive ethics education in all departments. This research contributes to the development of a more inclusive and responsible education policy in facing the digital era in Indonesia.