Teacher AI Literacy in Digital Teaching Material Development: An Observational Study in Yogyakarta

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.

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.

Artificial Intelligence (AI) literacy has come to the spotlight, empowering individuals to adeptly navigate the modern digitalised world. However, studies on teacher AI literacy in the English as a Foreign Language (EFL) context remain limited. This study aims to identify intraindividual differences in AI literacy and examine its associations with age and years of teaching experience among 782 English teachers. Given the absence of a reliable instrument to measure teacher AI literacy, we first constructed and validated a scale encompassing five sub‐scales: AI Knowledge , AI Use , AI Assessment , AI Design , and AI Ethics . Subsequently, latent profile analysis (LPA) was conducted using Mplus 7.4, with the results revealing four distinct profiles: Poor AI literacy (C1: 12.1%), Moderate AI literacy (C2: 45.5%), Good AI literacy (C3: 28.4%), and Excellent AI literacy (C4: 14.1%). Multinomial logistic regression analyses indicated significant associations between teacher AI literacy and both age and years of teaching experience. Additionally, 32 respondents participated in semi‐structured interviews. The qualitative data analysed with MAXQDA 2022 triangulated the quantitative results and offered deeper insights into teachers’ perceptions of their AI literacy. This study provides both theoretical and practical implications for understanding teacher AI literacy in the Chinese EFL context.

With the fast, rapid, and expeditious integration of Artificial Intelligence (AI) technologies, particularly Generative AI and Augmented Analytics, organizations are presented with new opportunities to transform their operations and empower their workforce. This paper explores the intersection of AI literacy, Generative AI, and Augmented Analytics to propose strategies for fostering a culture of AI fluency among employees. This paper reviews the literature on AI literacy and its potential implications for employee experience (Ex) in digital workplaces. AI literacy and competency is the ability to understand, interact with, and thoughtfully assess the applications and ramifications of artificial intelligence (AI) across diverse domains. The paper argues that AI literacy is a key competence for employees in the digital era, as it enables them to leverage the potential of generative AI and augmented analytics, two of the most promising technologies for enhancing Ex. Generative AI refers to the use of AI to create novel and diverse outputs, such as text, images, music, or designs, while augmented analytics drives potential capability to make use of Artificial Intelligence technologies to automate and augment data analysis and decision-making. The paper discusses how these technologies can empower employees to be more creative, productive, collaborative, and engaged in their work and the challenges and risks they pose. This paper additionally highlights the obstacles, deficiencies, and proposed pathways for further research advancement concerning AI technological competency and literacy, aiming to enhance the employee experience significantly in digital workplaces.

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

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

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 study proposes an intelligence-based Artificial Intelligence (AI) literacy curriculum tailored for primary education. Grounded in the theory of AI thinking and intelligence-based AI literacy primary education framework, this curriculum employs a generative AI approach, and integrates six competencies into performative learning activities. The educational implication is that it empowers students with AI thinking as a new agent for critically creating, thinking, and evaluating AI outputs. This study contributes to a paradigm shift by introducing a performative pedagogy to teach AI literacy education. This innovative approach transforms the learning experience by allowing students to engage with AI through dialogic, arts-based activities and performative actions. The integration of generative AI not only democratizes AI literacy education but also provides students with a creative and dialogic learning environment.

Combining human and artificial intelligence for enhanced AI literacy in higher education

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.

Artificial intelligence (AI) literacy has emerged to equip students with digital skills for effective evaluation, communication, collaboration, and ethical use of AI in online, home, and workplace settings. Countries are increasingly developing AI curricula to support students' technological skills for future studies and careers. However, there is a lack of reviews summarizing AI education in secondary schools. This study aims to provide an up-to-date summary of the literature, informing researchers, policymakers, and educators on fostering students' AI literacy. A thematic analysis of 50 AI education studies from 2016 to 2022 was conducted, evaluating pedagogies, teaching tools, learning contents, and assessment methods among secondary students. Results were identified: (1) Collaborative project-based learning, involving interdisciplinary problem-solving through artifact creation, emerged as the most common pedagogical approach; (2) Teaching tools were categorized into hardware, software, intelligent agents, and unplugged tools; (3) Junior students focused on experiencing AI and basic concepts, while senior students explored advanced and technical components; (4) Assessments included knowledge tests, questionnaires, and qualitative analysis (e.g., videos, documents, presentations); (5) Students' learning effects were measured across affective, behavioral, cognitive, and ethical dimensions. The study identifies suggestions and challenges for implementing AI education in secondary schools, offering valuable insights and recommendations for educators and decision-makers.

The rapid advancement of artificial intelligence (AI), particularly generative AI (GenAI), has significantly reshaped education, offering opportunities for personalized learning and instructional efficiency while posing challenges such as ethical concerns and algorithmic biases. Teachers must transition from passive users to strategic participants in AI integration, necessitating the development of comprehensive AI literacy. This study addresses gaps in existing research by proposing a systematic framework for teachers' AI literacy, identifying four core components: technical literacy (understanding AI fundamentals and tools), pedagogical literacy (integrating AI into teaching practices), ethical and societal literacy (navigating AI’s ethical implications), and reflective and adaptive literacy (continuous learning and adaptation). A structured training framework is designed to cultivate these competencies through modular, hands-on, and ethically grounded professional development. Challenges such as resistance to change, institutional barriers, and resource disparities are analyzed, alongside mitigation strategies like flexible training models and policy advocacy. The paper underscores the urgency of fostering teachers' AI literacy to ensure equitable, responsible, and pedagogically sound AI adoption in education.

Artificial Intelligence (AI) technology, particularly generative AI, has positively impacted education by enhancing mathematics instruction with personalized learning experiences and improved data analysis. Nonetheless, variations in AI literacy, trust in AI, and dependency on these technologies among mathematics teachers can significantly influence their development of 21st-century skills such as self-confidence, problem-solving, critical thinking, creative thinking, and collaboration. This study aims to identify distinct profiles of AI literacy, trust, and dependency among mathematics teachers and examines how these profiles correlate with variations in the aforementioned skills. Using a cross-sectional research design, the study collected data from 489 mathematics teachers in China. A robust three-step latent profile analysis method was utilized to analyze the data. The research revealed five distinct profiles of AI literacy and trust among the teachers: (1) Basic AI Engagement; (2) Developing AI Literacy, Skeptical of AI; (3) Balanced AI Competence; (4) Advanced AI Integration; and (5) AI Expertise and Confidence. The study found that an increase in AI literacy and trust directly correlates with an increase in AI dependency and a decrease in skills such as self-confidence, problem-solving, critical thinking, creative thinking, and collaboration. The findings underscore the need for careful integration of AI technologies in educational settings. Excessive reliance on AI can lead to detrimental dependencies, which may hinder the development of essential 21st-century skills. The study contributes to the existing literature by providing empirical evidence on the impact of AI literacy and trust on the professional development of mathematics teachers. It also offers practical implications for educational policymakers and institutions to consider balanced approaches to AI integration, ensuring that AI enhances rather than replaces the critical thinking and problem-solving capacities of educators.

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.