Search Results (19)

This research aims to examine the effect of artificial intelligence (AI)-supported sustainable geography education on the preparation process for the International Geography Olympiad (IGEO). Research was designed according to the simultaneous triangulation design, which is one of the mixed-methods designs. The research is a quasi-experimental model in terms of revealing the effects of independent variables (IGEO) on dependent variables (artificial). In this study, a quasi-experimental design with a pre-test–post-test control group was used. In this mixed-method study, quantitative data were obtained from questionnaires and achievement tests, while qualitative data were obtained from semi-structured interviews with students and teachers. The quantitative data collection tools used in the study were a mapping literacy achievement test and a problem-solving skills perception scale. The data were obtained from students across various class sections of the same school. Qualitative data were collected through semi-structured individual interview forms, observation forms, participant diaries, and focus group interview forms. Hierarchical regression analysis and ANOVA were used to analyze the statistical data, and the inductive analysis technique was used to analyze the qualitative data. The findings show that AI-supported sustainable geography education improves spatial thinking skills, individualized learning, and learning motivation. In the IGEO exam, students answered the field questions. Full article

Generative AI tools such as ChatGPT are reshaping educational practice, yet first-year teacher-education students often lack the prompt-engineering skills and confidence required to use them responsibly. This pilot study examined whether a concise three-session clinic on prompt engineering could simultaneously boost AI literacy and reduce technology anxiety in prospective teachers. Forty-five freshmen in a Peruvian teacher-education program completed validated Spanish versions of a 12-item AI-literacy scale and a 12-item technology-anxiety scale one week before and after the intervention; normality-checked pre–post differences were analysed with paired-samples t-tests, Cohen’s d, and Pearson correlations. AI literacy rose by 0.70 ± 0.46 points (t (44) = −6.10, p < 0.001, d = 0.91), while technology anxiety fell by 0.58 ± 0.52 points (t (44) = −3.82, p = 0.001, d = 0.56); individual gains were inversely correlated (r = −0.46, p = 0.002). These findings suggest that integrating micro-level prompt-engineering clinics in the first semester can help future teachers engage critically and comfortably with generative AI and guide curriculum designers in updating teacher-training programs. Full article

Generative artificial intelligence (AI) is changing higher education. Understanding students’ perceptions, usage behaviour and ethical concerns is crucial for the responsible and sustainable use of AI in the academic environment. The aim of this study was to explore the perceptions, experiences and challenges of health sciences students in relation to the use of generative AI in their academic learning. A descriptive cross-sectional survey was conducted with 397 students enrolled in four undergraduate health-related degree programmes in Slovenia, including nursing, physiotherapy, dietetics and applied kinesiology. The data was collected using a validated 27-point scale. Students were generally favourable towards AI, especially in terms of its perceived usefulness, integration into their daily study routine and ethical considerations. Regression analyses revealed that frequency of AI use, duration of use, self-reported skill level and confidence in using AI significantly predicted perceived usefulness. Gender differences were found, with male students reporting higher perceived usefulness and fewer concerns. Students recognised the potential of generative AI but emphasised the importance of ethical guidance, digital literacy and equal access. Institutions should prioritise structured training and inclusive strategies to ensure meaningful, sustainable and responsible integration of AI into health education. Full article

A persistent challenge in training future philology educators is engaging students in deep textual analysis across historical periods—especially in large classes where limited resources, feedback, and assessment tools hinder the teaching of complex linguistic and contextual features. These constraints often lead to superficial learning, decreased motivation, and inequitable outcomes, particularly when traditional methods lack interactive and scalable support. As digital technologies evolve, there is increasing interest in how Artificial Intelligence (AI) can address such instructional gaps. This study explores the potential of conversational AI chatbots to provide scalable, pedagogically grounded support in philology education. Using a mixed-methods case study, twenty-six (n = 26) undergraduate students completed structured tasks using one of three AI chatbots (ChatGPT, Gemini, or DeepSeek). Quantitative and qualitative data were collected via usability scales, AI literacy surveys, and semi-structured interviews. The results showed strong usability across all platforms, with DeepSeek rated highest in intuitiveness. Students reported confidence in using AI for efficiency and decision-making but desired greater support in evaluating multiple AI-generated outputs. The AI-enhanced environment promoted motivation, autonomy, and conceptual understanding, despite some onboarding and clarity challenges. Implications include reducing instructor workload, enhancing student-centered learning, and informing curriculum development in philology, particularly for instructional designers and educational technologists. Full article

The rapid evolution of the Fourth Industrial Revolution has significantly transformed educational practices, necessitating the integration of advanced technologies into higher education to address contemporary sustainability challenges. This study explores the integration of interactive metaverse environments and generative artificial intelligence (GAI) in promoting the green digital economy and developing e-entrepreneurship skills among graduate students. Grounded in a quasi-experimental design, the research was conducted with a sample of 25 postgraduate students enrolled in the “Computers in Education” course at King Khalid University. A 3D immersive learning environment (FrameVR) was combined with GAI platforms (ChatGPT version 4.0, Elai.io version 2.5, Tome version 1.3) to create an innovative educational experience. Data were collected using validated instruments, including the Green Digital Economy Scale, the e-Entrepreneurship Scale, and a digital product evaluation rubric. The findings revealed statistically significant improvements in students’ awareness of green digital concepts, entrepreneurial competencies, and their ability to produce sustainable digital products. The study highlights the potential of immersive virtual learning environments and AI-driven content creation tools in enhancing digital literacy and sustainability-oriented innovation. It also underscores the urgent need to update educational strategies and curricula to prepare future professionals capable of navigating and shaping green digital economies. This research provides a practical and replicable model for universities seeking to embed sustainability through emerging technologies, supporting broader goals such as SDG 4 (Quality Education) and SDG 9 (Industry, Innovation, and Infrastructure). Full article

With the rapid development of generative artificial intelligence, its application in the workplace has shown significant innovative potential and practical value. However, the existing literature lacks a systematic and widely applicable definition and measurement framework for Generative AI Literacy. Based on the existing literature and following a rigorous scale development process, this study constructs a Generative AI Literacy measurement framework that covers five core dimensions, basic technical competence, prompt optimization, content evaluation, innovative application, and ethical and compliance awareness, and validates its reliability and validity. Furthermore, based on the Ability–Motivation–Opportunity (AMO) theory, this study explores the mechanism through which Generative AI Literacy influences employee job performance and examines the mediating role of Creative Self-Efficacy. The results show that Generative AI Literacy has a significant positive impact on job performance (β = 0.680, p < 0.001), with Creative Self-Efficacy playing a partial mediating role (indirect effect = 0.537). The developed five-dimensional framework demonstrates strong psychometric properties and provides empirical evidence for AI literacy’s role in enhancing workplace performance through Creative Self-Efficacy mechanisms. This study provides an effective measurement tool for research on the application of Generative AI Literacy in workplace settings and offers practical insights for organizations to optimize performance and promote the responsible use of AI. Full article

In recent decades, Japan has faced rural depopulation due to urban migration, resulting in widespread property abandonment, the “Akiyas”. This paper presents RE-HAK (Refurbish to Host in Akiyas), a blockchain-based framework promoting a circular economy (CE). RE-HAK enables occupants to live rent-free in Akiyas by completing AI-managed refurbishment milestones via smart contracts. Each milestone—waste removal, structural repairs, or energy upgrades—is verified and recorded on the blockchain. Benefits include: (1) rural economic revival through restoration incentives; (2) sustainable CE adoption; (3) preserving property values by halting deterioration; (4) safeguarding cultural heritage via traditional architecture restoration; and (5) transparent management through automated contracts, minimizing disputes. Findings from three case studies demonstrate RE-HAK’s adaptability across skill levels and project scales, though limitations such as rural digital literacy gaps and reliance on government support for scalability are noted. The framework advances Japan’s revitalization goals while offering a replicable model for nations facing depopulation and property abandonment, contingent on addressing technological and policy barriers. Full article

(1) Background: In the era of generative AI (GenAI), assessing AI literacy is essential for understanding how effectively non-expert users can interact with AI. However, existing assessment tools primarily focus on users’ understanding of AI principles or rely on self-reported scales, neglecting critical thinking and actual interaction capabilities. To address this gap, this study aims to design and validate evaluation indicators targeting the behavioral process of human–GenAI interactions and analyze the impact of critical thinking. (2) Methods: Grounded in information literacy and critical thinking frameworks, this study operationalized human–AI interaction capabilities into behavioral indicators and rubrics through observation, surveys, and pilot studies. Data were collected from 121 undergraduates completing two real-world tasks with GenAI, and their interaction processes were documented and evaluated. (3) Results: The indicators showed acceptable inter-rater and internal consistency reliability. Exploratory and Confirmatory Factor Analysis confirmed a three-dimensional structure. Further analysis showed that interaction capabilities varied across gender, academic background, AIGC use frequency, critical thinking disposition levels, and question chain logic. (4) Conclusions: The developed evaluation indicators are reliable and valid. Further analysis reveals that a high critical thinking disposition can offset the disadvantage of lower usage frequency. This highlights the significance of critical thinking in enhancing human–GenAI interaction capabilities. Full article

Weeds cause significant yield and economic losses by competing with crops and increasing production costs. Compounding these challenges are labor shortages, herbicide resistance, and environmental pollution, making weed management increasingly difficult. In response, precision weed control (PWC) technologies, such as robots and unmanned aerial vehicles (UAVs), have emerged as innovative solutions. These tools offer farmers high precision (±1 cm spatial accuracy), enabling efficient and sustainable weed management. Herbicide spraying robots, mechanical weeding robots, and laser-based weeders are deployed on large-scale farms in developed countries. Similarly, UAVs are gaining popularity in many countries, particularly in Asia, for weed monitoring and herbicide application. Despite advancements in robotic and UAV weed control, their large-scale adoption remains limited. The reasons for this slow uptake and the barriers to widespread implementation are not fully understood. To address this knowledge gap, our review analyzes 155 articles and provides a comprehensive understanding of PWC challenges and needed interventions for scaling. This review revealed that AI-driven weed mapping in robots and UAVs struggles with data (quality, diversity, bias) and technical (computation, deployment, cost) barriers. Improved data (collection, processing, synthesis, bias mitigation) and efficient, affordable technology (edge/hybrid computing, lightweight algorithms, centralized computing resources, energy-efficient hardware) are required to improve AI-driven weed mapping adoption. Specifically, robotic weed control adoption is hindered by challenges in weed recognition, navigation complexity, limited battery life, data management (connectivity), fragmented farms, high costs, and limited digital literacy. Scaling requires advancements in weed detection and energy efficiency, development of affordable robots with shared service models, enhanced farmer training, improved rural connectivity, and precise engineering solutions. Similarly, UAV adoption in agriculture faces hurdles such as regulations (permits), limited payload and battery life, weather dependency, spray drift, sensor accuracy, lack of skilled operators, high initial and operational costs, and absence of standardized protocol. Scaling requires financing (subsidies, loans), favorable regulations (streamlined permits, online training), infrastructure development (service providers, hiring centers), technological innovation (interchangeable sensors, multipurpose UAVs), and capacity building (farmer training programs, awareness initiatives). Full article

This paper summarizes the findings and main conclusions from the first presentation of the module “CETD23: Cómo entrenar a tu dragón: la inteligencia artificial generativa como herramienta para mejorar el aprendizaje en entornos online e híbridos”. This is an optional module accredited through the ECTS (European Credit Transfer System) and delivered as part of the “Plan de Formación de Docencia y Personal Investigador 2021–2025” of the Universidad de Las Palmas de Gran Canaria (ULPGC). The Plan de Formación is a development programme offered by Spanish universities to new and existing teaching staff, aimed at improving the quality of their teaching practises in line with Aneca’s Docencia regulations (like the PGCERT and PGCAPT programmes in the UK). The aim of module CETD23 is to explore the use of Generative AI (GenAI) to enhance learning and teaching and to build the AI literacy of ULPGC’s teaching staff. The module received high student satisfaction, with an average score of 4.84 on the Likert Scale, and achieved a 100% completion rate for the final summative project. The final conclusions highlight the need for universities to establish reglamentos (policies and guidance) on how to use GenAI to enhance learning and assessment, as well as to involve students as equal partners in the design and assessment of methods that use AI. Full article

Background/Objectives: The evolving capabilities of large language models, such as generative pre-trained transformers (ChatGPT), offer new avenues for disseminating health information online. These models, trained on extensive datasets, are designed to deliver customized responses to user queries. However, as these outputs are unsupervised, understanding their quality and accuracy is essential to gauge their reliability for potential applications in healthcare. This study evaluates responses generated by ChatGPT addressing common patient concerns and questions about cleft lip repair. Methods: Ten commonly asked questions about cleft lip repair procedures were selected from the American Society of Plastic Surgeons’ patient information resources. These questions were input as ChatGPT prompts and five board-certified plastic surgeons assessed the generated responses on quality of content, clarity, relevance, and trustworthiness, using a 4-point Likert scale. Readability was evaluated using the Flesch reading ease score (FRES) and the Flesch–Kincaid grade level (FKGL). Results: ChatGPT responses scored an aggregated mean rating of 2.9 out of 4 across all evaluation criteria. Clarity and content quality received the highest ratings (3.1 ± 0.6), while trustworthiness had the lowest rating (2.7 ± 0.6). Readability metrics revealed a mean FRES of 44.35 and a FKGL of 10.87, corresponding to approximately a 10th-grade literacy standard. None of the responses contained grossly inaccurate or potentially harmful medical information but lacked citations. Conclusions: ChatGPT demonstrates potential as a supplementary tool for patient education in cleft lip management by delivering generally accurate, relevant, and understandable information. Despite the value that AI-powered tools can provide to clinicians and patients, the lack of human oversight underscores the importance of user awareness regarding its limitations. Full article

This exploratory study (N = 310) investigates the relationship between students’ attitudes toward artificial intelligence (AI), their attitudes toward AI ethics, and their media and digital literacy levels. This study’s specific objectives were to examine students’ (a) general attitudes toward AI, (b) attitudes toward AI ethics, (c) the relationship between the two, and (d) whether attitudes toward AI are associated with media and digital literacy. Participants, drawn from a convenience sample of university students, completed an online survey including four scales: (a) a general attitude toward AI scale (including two subscales, positive and negative attitudes), (b) an attitude toward AI ethics scale (including two subscales, attitudes toward accountable and non-accountable AI use), (c) a media literacy scale, and (d) a digital literacy scale, alongside demographic information. The findings revealed that students held moderate positive attitudes toward AI and strong attitudes favoring accountable AI use. Interestingly, media literacy was positively related to accountable AI use and negatively to positive attitudes toward AI, whereas digital literacy was positively related to positive attitudes, and negatively to negative attitudes toward AI. These findings carry significant theoretical implications by highlighting the unique relationship of distinct literacies (digital and media) with students’ attitudes. They also offer practical insights for educators, technology designers, and administrators, emphasizing the need to address ethical considerations in AI deployment. Full article

With the rapid advancement of Artificial Intelligence (AI) in early childhood education (ECE), young children face the challenge of learning to use AI ethically and appropriately. Developing AI education programs requires an age- and culturally-appropriate AI literacy framework. This study addresses this fundamental gap by creating a Chinese framework for early childhood AI literacy through an expert interview study with a grounded theory approach. Seven Chinese experts, including ECE and AI professors, kindergarten principals, and Directors of ECE Information Departments, were purposely sampled and interviewed, representing scholars, policymakers, and practitioners. The synthesis of the transcribed evidence generated five dimensions of young children’s AI literacy, namely Safety, Identity, Attitude, Cognition, and Capability, collectively forming a holistic framework titled the ‘SIACC’ framework. The Chinese definition of early childhood AI literacy was also reported. This study introduces the Chinese framework of AI literacy and provides a scientific basis for policymakers to establish AI literacy standards for young children. Additionally, it offers a conceptual structure for developing systematic indicators and scales within AI literacy in ECE. Full article

With the increasing use of large-scale language model-based AI tools in modern learning environments, it is important to understand students’ motivations, experiences, and contextual influences. These tools offer new support dimensions for learners, enhancing academic achievement and providing valuable resources, but their use also raises ethical and social issues. In this context, this study aims to systematically identify factors influencing the usage intentions of text-based GenAI tools among undergraduates. By modifying the core variables of the Unified Theory of Acceptance and Use of Technology (UTAUT) with AI literacy, a survey was designed to measure GenAI users’ intentions to collect participants’ opinions. The survey, conducted among business students at a university in South Korea, gathered 239 responses during March and April 2024. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS software (Ver. 4.0.9.6). The findings reveal that performance expectancy significantly affects the intention to use GenAI, while effort expectancy does not. In addition, AI literacy and social influence significantly influence performance, effort expectancy, and the intention to use GenAI. This study provides insights into determinants affecting GenAI usage intentions, aiding the development of effective educational strategies and policies to support ethical and beneficial AI use in academic settings. Full article

Various articles suggest that artificial intelligence (AI) in libraries can enhance the learning experience, promote lifelong learning, and strengthen digital literacy. However, it is unclear if practicing librarians agree with these benefits. Malaysia and Indonesia, neighboring countries with similar library practices, may have differing or similar views on AI support for learning, lifelong learning, and digital literacy. To this effect, this study was conducted with the aim of assessing librarian perspectives on the support provided by AI in enhancing learning experiences, fostering lifelong learning, and advancing digital literacy initiatives. Additionally, it seeks to compare these perspectives between Malaysia and Indonesia. Using a survey research methodology and an online questionnaire as the data collection technique, the results of the analysis showed that librarians assessed the AI support for learning experiences, lifelong learning, and digital literacy favorably. It was also found that there was no significant difference in the assessments of librarians from these two countries. The contribution of this study is that it has provided empirical evidence regarding AI support in libraries, and developed a scale or measurement for assessing AI support for learning experiences, lifelong learning, and digital literacy. This instrument can be used as a guide when it comes to investing in AI technologies for libraries. Full article