AI Literacy and Critical Digital Literacy in School Practice: Collaborative Digital Writing as a Cognitive and Instructional Model for 21st Century Learning

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 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.

Nursing students' artificial intelligence (AI) literacy, AI self-efficacy and AI self-competency: A cross-sectional design and structural equation model analysis.

Enhancing teacher AI literacy and integration through different types of cases in teacher professional development

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.

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 notes What 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.

The rapid integration of artificial intelligence (AI) in education requires teachers to develop AI competencies while preparing students for a society influenced by AI. This study evaluates the impact of an online teacher training program on German in-service teachers’ AI literacy, usage behaviors, and attitudes toward AI. A pre-post design study was conducted with teachers (N = 436 for attitude assessment, among whom N L = 291 teachers for AI literacy) participating in the course. The program combined synchronous and asynchronous learning formats, including webinars, self-paced modules, and practical projects. The participants exhibited notable improvements across all domains: AI literacy scores increased significantly, and all attitude items regarding AI usage and integration demonstrated significant positive changes. Teachers reported increased confidence in AI integration. Structured teacher training programs effectively enhance AI literacy and foster positive attitudes toward AI in education.

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

The integration of Artificial Intelligence (AI) literacy into college curricula is a pressing but complex challenge, particularly in non-technical fields like business. This paper presents a case study of a pedagogical intervention designed to embed AI literacy within a required college mathematics course. The intervention employed a Problem-Based Learning (PBL) framework where 120 business students used tools like ChatGPT and matrix calculators to solve an authentic data-driven business problem. We analyzed data from the final assessment artifact – a digital magazine – to evaluate the development of specific AI literacy competencies. Initial findings from this pilot implementation indicate that the PBL approach was effective in developing students’ skills in data-driven argumentation, critical evaluation of AI-generated outputs, and the ability to connect abstract mathematical models to practical AI applications. The study demonstrates a promising replicable model for integrating AI literacy into foundational courses, but also highlights key challenges, including the need for explicit scaffolding in critical AI evaluation. This paper contributes empirical insights from an initial implementation, offering a practical framework and actionable lessons for educators designing AI literacy curricula.

This poster showcases a case study of an Australian higher education institution’s artificial intelligence (AI) literacy staff development program. It offers practical suggestions to ASCILITE attendees on how to empower academic and professional staff to navigate the unknown terrain of generative AI collaboratively and responsibly. Since the release of ChatGPT in November 2022, higher education institutions have been grappling with its impact on assessment, teaching and learning, and the world of work (CRADLE Blog, 2023) -culminating in the Tertiary Education Quality and Standards Agency (TEQSA) Request for Information (RFI) about how institutions will engage with AI and secure course integrity (TEQSA, 2024). Effective institutional responses to TEQSA’s RFI are predicated on staff at all levels rapidly developing their AI literacy in order to conceptualise and implement the curriculum and assessment changes required. AI literacy is generally accepted to include understanding of AI tools and how they work, discussion of ethical and societal implications and critical evaluation of their outputs, and competency in integration of AI ethically and effectively into daily practice (Chan & Colloton, 2024; Hibbert, Melanie et al., 2024; Hillier, 2023). This poses a significant challenge for institutions because of rapidly evolving AI tools and the diverse capabilities and starting points of large staff cohorts, including among third space support staff responsible for implementation. ECU's evolving strategy for building organisational capacity in AI literacy is outlined in this poster. The approach, which aligns with ECU’s Framework and Guidelines for Ethical and Productive Use of AI (Edith Cowan University, 2023), is designed to empower and enable staff. It intentionally incorporates connectivist and constructivist learning theories, informed by Fink's Taxonomy of Significant Learning (Fink, 2013) and Miller's Pyramid (Miller, 1990). This meant (a) providing essential foundational knowledge about AI, (b) developing practical skills through hands-on experience and exploration, and (c) fostering collective capability through sharing and collaboration. These efforts complemented initiatives to support student AI literacy through similar impactful interventions (Sullivan et al., 2024). In 2024, ECU implemented the following activities to support academic and professional staff: “AI 101” Canvas site: Covers how AI works, ethical and societal considerations, and AI in learning and teaching. “Explore AI” workshops: Focused on practical exploration of AI tools that generate both text and images, as well as ethics, research and assessment. “AI Digest” Viva Engage Community: Provides regular updates about AI. Generative AI tools: A series of tools for trials e.g., custom chatbots and image generators. Workshops co-designed with Schools: Explores generative AI in discipline-specific ways (including arts, humanities, business, law and performing arts) Despite currently being voluntary, these initiatives have received strong engagement and positive feedback to date. For example, all respondents to the Explore AI Session feedback forms said they would recommend the sessions to colleagues. 331 academic and professional staff have engaged with the AI 101 Canvas site so far, spending a median of 3 hours and 5 minutes in the course. 74% of the 50 respondents to the AI 101 evaluation form stated that their confidence levels improved after completing the course. ECU continues to iteratively improve its AI literacy offerings and expand staff engagement, collectively making sense of generative AI and its effects as an institution.

This study explores variables associated with teachers’ Artificial Intelligence (AI) literacy, a key competency for effective and responsible AI integration in education. A total of 270 teachers completed an online survey including measures of AI literacy, AI acceptance, computational thinking, AI anxiety, and digital divide. Results revealed that all AI acceptance variables were positively associated with AI literacy, with hedonic motivation and willingness to use AI emerging as the strongest predictors. Computational thinking, AI anxiety, and digital divide also showed significant associations with AI literacy. The findings highlight the central role of teachers’ attitudes and motivational variables over technical and demographic variables. The study contributes to the understanding of how teachers engage with AI technologies and provides practical implications for designing professional development programs that enhance AI literacy and reduce barriers to AI adoption in educational contexts.

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.

The integration of artificial intelligence (AI) in healthcare necessitates AI literacy within medical education.As AI's role in health care expands, understanding algorithm transparency, accountability and bias is crucial.However, incorporating AI education into an already dense curriculum poses challenges.A structured, efficient course covering both technical and ethical aspects of AI is essential to prepare future clinicians for AI-enabled health care. | WHAT WAS TRIED?We developed a one-credit, 18-hour AI literacy course for medical students, balancing theoretical foundations with experiential learning.The course structure comprised a 3-hour lecture on fundamental AI concepts, two 6-hour hands-on workshops where students worked in groups of three to four and a concluding 3-hour discussion and reflection session.These sessions were strategically designed to ensure engagement while accommodating students' demanding schedules.Shorter, more frequent sessions were considered but deemed impractical due to scheduling constraints and the challenge of effectively conducting hands-on activities in a fragmented format.The course was initially introduced in 2020 and 2021 for secondyear medical students, attracting 11 and 13 students, respectively (23% of the cohort).Based on student feedback, it was revised in 2022 to target senior students (fifth-and sixth-year), increasing participation to 33%.In the workshops, students developed and deployed AI models (e.g., knee fracture detection, wound segmentation), guided by a data scientist and a clinician with expertise in the AI topic, fostering interdisciplinary collaboration.Key topics like privacy, bias, data security and patient autonomy were integrated into projects, prompting reflection on social impacts such as ethical AI use and healthcare disparities.Project themes were selected based on faculty expertise and contemporary AI applications, ensuring clinical relevance.Student learning was assessed using a 17-competency framework, 1 measuring AI literacy before and after the course to evaluate effectiveness and inform future improvements.3 | WHAT LESSONS WERE LEARNED?Transitioning the course to senior medical students enhanced engagement and comprehension, aligning AI concepts with clinical applications.Quantitative assessments showed substantial improvements in AI literacy, particularly in 'AI's strengths and weaknesses' (RS 1.6), 'data literacy' (RS 1.3), 'critically interpreting data' (RS 1.15) and 'ethics' (RS 1.15).Constructive feedback from students, collected via structured surveys, highlighted the value of hands-on experience, interdisciplinary learning and real-world AI applications.The design and implementation of our 18-hour AI literacy course provide insights into integrating AI education within medical training.First, while AI education programmes vary in length-from brief workshops to full-semester courses-our approach demonstrates that an intensive yet feasible structure enables medical students to develop key competencies within a compact timeframe.Second, curricula should emphasize hands-on learning, guiding students through real-world AI challenges and ethical considerations.Third, AI literacy training may best target senior medical students with more clinical experience, preparing them to use AI independently.Lastly, challenges included diverse student technical backgrounds and the rapid evolution of AI, requiring continuous faculty upskilling.These challenges highlight the need for adaptive AI curricula that evolve with technological advancements and learner needs.

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 notes What 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.

The rapid advancements in artificial intelligence (AI) technologies are fundamentally transforming mathematics teaching processes and offering new pedagogical opportunities within instructional environments. However, the effective use of these technologies is closely related to mathematics teachers' levels of knowledge, awareness, attitudes, and skills regarding AI. The purpose of this study is to examine the relationship between mathematics teachers' AI literacy and AI anxiety, to conduct an in-depth analysis of their perceptions regarding the integration of AI into mathematics education, and to evaluate the effects of variables such as watching AI-related films, technology use, and age on this process. This study employed a mixed-methods design. In the quantitative phase, a predictive correlational model was employed, while in the qualitative phase, a case study approach was utilized. Data were collected from 251 mathematics teachers working in various regions of Türkiye. The quantitative data were analyzed using a range of statistical analysis techniques, whereas the qualitative data were evaluated through content analysis. The findings indicate that mathematics teachers' levels of AI literacy are above average, whereas their levels of AI anxiety are below average. A significant and negative relationship was found between AI literacy and AI anxiety. Furthermore, the level of technology use in mathematics instruction was identified as the strongest predictor of both AI literacy and AI anxiety. The results also revealed that mathematics teachers' most prominent anxiety is that the excessive use of AI tools may weaken students' independent thinking and problem-solving skills. In addition, anxiety regarding the potential weakening of the teaching role and the possibility that AI could replace teachers were also noteworthy. Professional development programs should encompass not only the fundamental technological features of AI but also its pedagogical contributions to mathematics instruction. Mathematics teachers should be provided with opportunities to observe how AI supports key instructional processes such as differentiated instruction, formative assessment, and conceptual visualization. Furthermore, training modules should aim to develop teachers' abilities to critically evaluate AI-generated mathematical content in terms of accuracy and pedagogical appropriateness. Through such targeted training, teachers can enhance their AI literacy and create safe and pedagogically meaningful digital learning environments for their students.