Cognitive Risks of AI: Literacy, Trust, and Critical Thinking

This research explores the relationship between Artificial Intelligence (AI) literacy and trust in AI systems. As AI continues to be integrated into various aspects of daily life, understanding the connection between knowledge of AI and trust is crucial. The study develops a comprehensive AI literacy questionnaire, addressing key dimensions such as "Know and Understand AI," "Use and Apply AI," and "Evaluate and Create AI," alongside questions measuring the degree of trust in AI. The research finds a weak negative relationship between AI literacy and trust, which is different from existing literature that typically suggests a positive or neutral correlation. The results, though not statistically significant, highlight the importance of AI education and its potential role in shaping public trust. Despite limitations such as sample size and time constraints, the study offers valuable insights and contributes to the ongoing dialogue about AI literacy and trust. Future research, with a larger and more diverse sample, could help clarify these findings and further explore the interplay between AI literacy and trust across different populations and settings.

ABSTRACTThis study explores the impact of Artificial Intelligence (AI) literacy on trust in AI across critical sectors, including transportation, healthcare, and social relationships. An online survey of 300 participants was conducted to examine trust levels in six practical AI application scenarios. The findings revealed that individuals with advanced AI literacy consistently demonstrate higher trust across all scenarios. In contrast, those with intermediate AI literacy exhibit more skepticism, particularly in high‐stakes contexts such as transportation and healthcare. This result indicates that the disparities in AI literacy can significantly shape trust levels, and the context of AI use matters. Therefore, targeted educational programs are needed to improve AI literacy, rectify misconceptions, and promote broader acceptance and trust in AI technologies. Further research should expand the demographic scope to further validate these findings and optimize educational initiatives for inclusive and equitable AI integration.

In the context of the rapid integration of artificial intelligence (AI) into daily life, this study explores underexamined factors influencing adolescents’ intentions to adopt AI technology. Based on the stimulus-organism-response (SOR) framework, the study uses a structural equation model to investigate the relationships between parental mediation, AI literacy, trust in AI, perceived creepiness of AI, and intention to use AI. A sample of 853 adolescents participated in this study. The findings reveal that active parental mediation significantly enhances AI literacy, fosters trust in AI, and mitigates perceived creepiness of AI, thereby increasing adolescents’ intention to use AI technology. In contrast, restrictive parental mediation does not exhibit any significant effects on AI literacy or usage intentions. The findings highlight the critical role of family influence in adolescents’ adoption of AI, advocating for parents to take an active parental mediation to help adolescents benefit in the AI era.

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

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.

Many researchers have started using artificial intelligence (AI) tools in the different parts of their scientific research processes. A high level of AI literacy is required to utilize AI tools effectively. AI literacy also includes many cognitive and technical skills. These pre-requisite skills can impact researchers’ AI usage competencies. In this context, the current study aimed to investigate the determinants of researchers’ AI literacy from the demographic variables and twenty-first century skills. For this purpose, a model was created with 24 hypotheses and tested using data collected by 708 researchers from various universities in Türkiye. Non-experts AI literacy, digital literacy, data literacy, and computational thinking scales, and demographic information form were used as data collection tools. Structural Equation Modelling (SEM) was used to reveal the relationship among the variables. The results revealed that digital literacy and data literacy skills are the strongest predictors of AI literacy. In addition, digital literacy and data literacy have mediating roles between the some 21st skills and AI literacy. There are also some demographic variables such as English language level and frequency of AI use, which are the significant predictors of AI literacy.

As artificial intelligence-generated content (AIGC) is widely used, the interaction model between humans and artificial intelligence (AI) is undergoing a fundamental shift from “use” to “co-creation”. This study employed quantitative methods to survey 401 Chinese college students in order to investigate the factors that motivate college students to co-create with AI, as well as the relationship between AI co-creation behaviors and their AI literacy. The research findings indicate that students’ trust in AI, along with their Technology Readiness Index (TRI), as well as the factors of explainability and personalization of AI, have a significant positive impact on college students’ intention and behavior to co-create with artificial intelligence. Moreover, the act of co-creation significantly amplifies AI literacy among students. The study elucidates that a robust trust in AI, coupled with a high Technology Readiness Index (TRI), as well as the AI’s capacity for explainability and personalization, are key drivers propelling college students’ eagerness and engagement in co-creation with AI. Engaging in the process of co-creation not only enhances AI literacy but also acts as a critical conduit, mediating the impact of both individual traits and AI attributes on the acquisition of such literacy.

Artificial intelligence (AI) has transformed higher education. The research shows that university students use AI and acquire academic ideas to develop innovative solutions to their problems. However, there is limited research on how AI feedback helps students improve their idea implementation. This study was rooted in the Technology Acceptance Model (TAM) and the Social Cognitive Theory (SCT). The objective was to examine how the AI assessment feedback (AIAF), trust in AI (TAI) and AI literacy (AIL) influence university students' idea implementation skills (IIS). A descriptive survey design was used to collect data from 486 university students. The analysis revealed that AIAF significantly contributes to students' IIS at the university level. TAI was found to partially mediate the relationship between university students' AIAF and IIS. AIL was found to contribute to students' IIS individually. However, the interaction effect (AIAF*AIL) was not found to be a significant contributor to students' IIS. We found that the interplay among the study's variables (AIAF, TAI and AIL) positively enhances university students' IIS in solving their academic problems. It was recommended that students should use self‐regulated learning in AI feedback to improve their achievements. In addition, the current study has several practical, research and policy implications.

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.

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.

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.

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

AI literacy has emerged as a crucial aspect of digital literacy research in the field of education. Currently, there are limited studies about the implications of Artificial Intelligence (AI) in Early Childhood Education (ECE). Owing to the recent development of curricula for young learners in industrialized nations, developing countries are hesitant to adopt AI at the ECE level. A scoping review was undertaken on the content of fourteen research articles published between 2016 and 2023. This scoping review evaluates and reviews the contents of fourteen papers on the knowledge and comprehension of AI in ECE, which covers curriculum design, artificial intelligence tools, instructional methodologies, research designs, evaluation methods, findings, and various types of possibilities and problems linked to AI literacy and content. Several obstacles were identified, including (1) an insufficiently designed curriculum, (2) lacking instructors' understanding, experience, and trust in AI, and (3) the lack of an instruction manual. Engaging in reading can offer educational possibilities and foster the growth of AI literacy in young learners, encompassing AI concepts, actions, and perspectives. This study recommended AI literacy for the educators and learners of ECE to be suitable for their age group and level.

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.