Computational thinking and AI literacy integration in mathematics education: design principles for curricular and instructional innovations

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

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.

A guide to computational thinking education, with a focus on artificial intelligence literacy and the integration of computing and physical objects. Computing has become an essential part of today's primary and secondary school curricula. In recent years, K–12 computer education has shifted from computer science itself to the broader perspective of computational thinking (CT), which is less about technology than a way of thinking and solving problems—“a fundamental skill for everyone, not just computer scientists,” in the words of Jeanette Wing, author of a foundational article on CT. This volume introduces a variety of approaches to CT in K–12 education, offering a wide range of international perspectives that focus on artificial intelligence (AI) literacy and the integration of computing and physical objects. The book first offers an overview of CT and its importance in K–12 education, covering such topics as the rationale for teaching CT; programming as a general problem-solving skill; and the “phenomenon-based learning” approach. It then addresses the educational implications of the explosion in AI research, discussing, among other things, the importance of teaching children to be conscientious designers and consumers of AI. Finally, the book examines the increasing influence of physical devices in CT education, considering the learning opportunities offered by robotics. Contributors Harold Abelson, Cynthia Breazeal, Karen Brennan, Michael E. Caspersen, Christian Dindler, Daniella DiPaola, Nardie Fanchamps, Christina Gardner-McCune, Mark Guzdial, Kai Hakkarainen, Fredrik Heintz, Paul Hennissen, H. Ulrich Hoppe, Ole Sejer Iversen, Siu-Cheung Kong, Wai-Ying Kwok, Sven Manske, Jesús Moreno-León, Blakeley H. Payne, Sini Riikonen, Gregorio Robles, Marcos Román-González, Pirita Seitamaa-Hakkarainen, Ju-Ling Shih, Pasi Silander, Lou Slangen, Rachel Charlotte Smith, Marcus Specht, Florence R. Sullivan, David S. Touretzky

These days’ educational landscape forces teachers to adapt to changing demands and embrace innovations. In this study, Artificial Intelligence (AI) literacy was analyzed as how it mediates the association between Computational Thinking Skills (CTS) and Organizational Agility (OA) among secondary teachers. A quantitative causal mediation analysis design was utilized in this study. Standardized AI literacy test, CTS Test, and OA test instruments were utilized to gather pertinent data among 305 respondents. The test instruments were first subjected to confirmatory factor analysis for model fitness. Path and mediation analysis through structural equation modeling revealed that CTS significantly predicts AI literacy, AI Literacy significantly predicts OA, and CTS significantly predicts OA. It was found out that AI literacy partially mediates the relationship between CTS and OA among teachers. This recommends that schools should conduct a comprehensive training program to enhance teachers' CTS and AI proficiency for schools' sustained agility. Keywords: AI literacy, computational thinking skills, organizational agility, structural equation modeling, mediation analysis

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

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) has emerged as a critical subject in global educational contexts, not only within computing majors but also across all academic disciplines. This shift mirrors the rise of digital literacy in the late 20th century, positioning AI literacy as a needed skill for future generations. Despite its importance, there is ongoing debate about what exactly AI literacy entails and the skills it requires. While previous research has explored how computational thinking and varying educational levels affect AI literacy, there is a gap in related research on the impact of coding experience and the age at which students first learn about AI, especially for university students in non-computer-based majors. This exploratory study revealed that South Korean university students with prior coding experience consistently demonstrated significantly greater AI literacy than did those without prior coding experience. However, the age when students first learn about AI does not seem to play a major role in their overall perception of AI. These results suggest that the idea that coding experience might not be necessary for AI literacy needs additional investigation. However, further research is needed to fully understand the factors that contribute to AI literacy in non-computer-based university majors.

Does intrinsic motivation mediate perceived artificial intelligence (AI) learning and computational thinking of students during the COVID-19 pandemic?

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.

Artificial intelligence (AI) has gained widespread public interest in recent years. However, as AI literacy remained excluded from the standard academic curricula, AI education in the US was predominantly offered through extra-curricular activities, which limited AI learning exposure to only a select group of students. Given these limitations, the need to integrate AI literacy education into the standard curricula is increasingly evident. This study investigated the integration of AI learning in an advanced biology course. Thirty-seven students participated in four lessons embedding AI learning in biology contexts. The interplay of students’ AI learning and biology knowledge was examined from the quantitative measure of conceptual understanding and qualitative analysis of interdisciplinary reasoning. This concurrent triangulation research design utilized results from both quantitative and qualitative analyses to develop a comprehensive understanding of students’ AI learning in the biology context. The results of the study showed a significant improvement in students’ AI concepts. Students’ biology knowledge had a slight increase, but it was not statistically significant. Both quantitative and qualitative results underscored a close connection between students’ AI learning and their biology knowledge, though the quantitative findings were not conclusive in some lessons. The article concluded with a discussion of the potential reasons for those discrepancies. In addition, suggestions were provided for future research and practitioners who are interested in integrating AI education across curricula.

Existing research on Artificial Intelligence (AI) literacy primarily focuses on educational, STEM, business, and institutional settings. A research gap exists in terms of how AI concepts are framed in the public sphere. This study analyzes seventeen (N=17) TED talks from 2020 to 2025 to explore how experts publicly communicate core ideas related to AI. It draws upon a four-factor model of AI literacy that includes the dimensions of knowing and understanding, using and applying, creating and evaluating, and AI ethics. Thematic analysis is used to examine how these four dimensions are reflected in public-facing expert discourse. Results showed that in addition to the four core factors, there were two additional identifiable themes: reflect on and critique AI, and advocacy and lifelong learning. Themes and subthemes are illustrated with examples and quotes capturing how speakers convey key ideas and concerns. This study provides novel insights on how expert-led discourse on AI contributes to deepening public AI literacy.

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

The increasing prevalence of artificial intelligence (AI) in everyday life has intensified the emphasis on teaching AI literacy to children. However, there is no consensus on the specific knowledge and skills that constitute children’s AI literacy, resulting in varied AI learning materials for young people. We systematically searched for educational practices for children’s AI learning in both formal and informal settings and examined the AI learning content taught to children. Our findings led to the development of a holistic AI literacy framework for children, which contains three high-level dimensions and eight content areas of AI literacy: AI awareness (AI definition, AI application, and AI history), AI mechanics (AI input, learning procedure, and AI output), and AI impacts (AI implication and responsible practice). Theoretically, we contribute a research-based, comprehensive, and current framework for children’s AI literacy, advancing its conceptualization in early life stages. Practically, our framework can guide researchers and practitioners in promoting AI education for the next generation.

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.

BackgroundAs the significance of artificial intelligence (AI) continues to increase, there is a need for effective scaffolding and support for novice learners. Educators have encountered challenges in effectively scaffolding novice learners AI concepts, and providing appropriate motivational support. Research evidence has shown the potential of game‐based approaches to fostering secondary school students' AI literacy and motivation to learn AI.ObjectivesThis study developed an online platform TreasureIsland to gamify ebooks and investigated whether and how students playing with it can effectively enhance their AI literacy. This study aims to contribute an empirical and theoretical basis for AI literacy education and promote the use of gamification that would be broadly applied in other schools.MethodsA quasi‐experiment was conducted to evaluate the effects of the proposed gamified approach, which included a control group using an ebook with playful resources. To triangulate the quantitative results obtained from the pre and post‐test, focus group interviews were also conducted.ResultsThe platform was effective in improving students' motivation, self‐efficacy, career interest, and understanding of AI concepts and ethics, but did not enhance their confidence of using AI, and high cognition of applying, evaluating and creating AI. TreasureIsland players demonstrated significant improvement in all affective and cognitive domains, except for the ability to apply, evaluate, and create AI. Interviews revealed that the gamified approach could promote students' AI literacy by adhering to guidelines, including (1) creating a competitive and motivating learning environment through game mechanics, (2) providing scaffolding modules and feedback, and (3) visualising complex AI concepts via simulations. Feedback collected from the study suggested adding pedagogical elements such as flipped classrooms and project‐based learning in future research to improve the instructional design, and enable students to reach a higher level of cognition.ConclusionsThis study concludes that the use of gamification can provide affective and cognitive support and an enjoyable experience for fostering learners' AI literacy. It helps instructional designers and teachers enrich the pedagogical knowledge related to gamified platform and AI literacy.