The global classroom: vision-driven XR across the time-space matrix

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 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.

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.

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.

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.

Teacher education students play double role as present learners and future educators. Hence, they need targeted training to navigate the growing influence of Generative Artificial Intelligence (GenAI) on teaching, learning and professional identity. However, existing artificial intelligence (AI) literacy programmes predominantly emphasize technical AI knowledge and pre‐GenAI tools or are offered by GenAI platforms that focus on their own technologies' features, thereby lacking pedagogically structured frameworks that address non‐technical dimensions such as ethics, contextual understanding and the development of human‐centered, critical and lifelong learning mindsets to adapt to rapid technology changes. Furthermore, limitation of time in teacher education syllabus and faculty's lack of AI literacy require targeted intervention. This design‐based research (DBR) aims to fill this gap by developing and evaluating a set of design principles for GenAI literacy training in teacher education. Integrating contemporary AI competency frameworks for learners and teachers, the study implements these principles in a workshop that serves as the research prototype. The prototype was first piloted with 14 master's students and then evaluated with 29 teacher education (TE) students. Results show significant gains in participants' AI competence self‐efficacy, attitudes towards GenAI and commitment to critical, ethical and pedagogical engagement with GenAI tools. The findings highlight the need for teacher education programmes to integrate GenAI literacy that supports teachers' evolving roles as reflective practitioners, co‐creators and lifelong learners in an AI‐driven world. Practitioner notes What is already known about this topic Students' self‐efficacy and mindset influence their adoption of GenAI in learning and teaching. GenAI competency is essential for teacher education students, encompassing technological, ethical and sociocultural dimensions. Implementing AI literacy training faces challenges such as educators' limited AI literacy, the lack of pedagogical frameworks and the need to integrate technical knowledge with ethical reasoning, self‐regulated learning and metacognitive skills. Transformative learning is crucial in preparing teacher education students as co‐creators and facilitators in AI‐enhanced education. What this paper adds This study presents the design and evaluation of an AI competency training programme to enhance teacher education students' self‐efficacy, critical and human‐centered AI mindsets and human–AI interaction skills. Using design‐based research, it identifies key principles for AI literacy training, demonstrating how transformative, active and experiential learning approaches can prepare teacher education students for evolving roles in AI‐enhanced education. Implications for practice and/or policy Educational technologists can apply the proposed design principles to develop GenAI tools and AI systems that enhance AI competency and self‐regulated learning (SRL). Educators can integrate these AI training principles into their teaching strategies and enhance their task assignments and assessments to foster deep learning, self‐regulated learning and metacognitive skills in the age of GenAI. Policymakers should update teacher education curriculum to include AI competency, strengthen content and pedagogical knowledge, especially in social emotional learning, formative and authentic assessment methods and establish guidelines for selecting GenAI tools that support human intelligence and mitigate over‐reliance.

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.

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.

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.

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.

This study aims to develop and evaluate a Moodle-based digital learning model to improve the artificial intelligence (AI) literacy of non-STEM students in a management program. To promote enactiveness, we designed ADDIE-based learning model with integrated case-based learning, project-based assignments, interactive H5P content and Gamification features. A one-group pretest–posttest design was used on 50 undergraduate students. Data was collected through AI Literacy Tests, Learning Analytics and Questionnaires. Before hypothesis testing using the Shapiro–Wilk test, normality of data were assessed. Results of present t-test showed that students significantly reported improvement in their competence for AI literacy after education intervention (p <0.001) with a large effect size. Learning analytics uncovered high engagement, and a strong positive relationship with time spent on the learning modules that significantly correlated with posttest scores. The qualitative analysis also indicated that students of the learning environment perceived as relevant, motivating and encouraging the development of practical skills. While the absence of control group means these results have limitations as a basis for causative inference, they indicate that such an organically structured learning model based around Moodle could work feasibly in supporting AI literacy development across non-STEM higher education cohorts. This study provides a guide about possible integration of pedagogical techniques and interactive technologies concerning AI literacy education.

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.

In order to promote the application of artificial intelligence (AI) literacy in the nursing field in China, this study reviewed relevant domestic and international research to summarize the concept, measurement tools, frameworks, and current applications of AI literacy. Currently, AI literacy has been widely applied abroad in areas such as nursing career planning, assisted diagnosis, optimization of nursing workflows, electronic health records, and enhancement of patient awareness. However, challenges still remains, including relatively low literacy levels among certain group, and domestic researches in this area are in a preliminary stage. With the rapid advancement of digital technologies, enhancing AI literacy among nursing professionals has become an important trend. Researchers still face challenges such as insufficient operational proficiency, limited depth of understanding, and ethical concerns regarding data privacy and accountability. It is essential to strengthen the connection between nursing education and clinical practice. Through on-the-job training, simulated exercises, and public education initiatives, the AI literacy of nursing staff can be comprehensively improved, fostering its deeper integration into nursing practice to deliver safer and more efficient patient care. (为促进人工智能素养在我国护理领域的应用, 通过综述国内外相关研究, 总结人工智能素养的概念、测量工具、框架及其应用现状。目前, 国外已在护理职业规划、辅助诊断、护理流程优化、电子病历及患者认知提升等方面广泛应用人工智能素养, 取得一定成效, 但仍存在部分群体素养水平偏低的问题, 而国内相关研究尚处于起步阶段。随着数字技术快速发展, 提升护理人员人工智能素养已成为重要趋势。然而目前仍面临操作熟练度不足、理解深度有限、数据隐私与责任界定等伦理挑战。未来应加强护理教育与临床实践的衔接, 通过在职培训、模拟实训与科普教育等途径, 全面提升护理人员的人工智能素养, 推动其与护理实践深度融合, 以更安全、高效地服务患者。)

Artificial intelligence literacy, readiness, and innovativeness: Exploring interrelationships in undergraduate nursing students.