Search Results (398)

Cultural organizations, like museums, libraries, and archives, are trusted spaces for discovery, innovation, interrogation, and encouraging agency and exploration. They go beyond the physicality of objects, focusing on dissemination of knowledge as the very mission of the organization. By adopting this mission around using collection items, crowdsourcing projects, and programming to expose digital literacy concepts like bias, algorithms, and more, institutions have a new opportunity to become essential to life-long learning geared towards this second quarter of the 21st century. Through hands-on experiences that expose the bias and choice in all description and knowledge creation, these organizations can reinsert human-in-the-loop frameworks into machine learning and artificial intelligence platforms. Full article

This conceptual paper revisits the learning styles debate in the context of generative and adaptive artificial intelligence in higher education. It does not seek to rehabilitate classical learning style taxonomies, whose prescriptive claims have been widely challenged as a neuromyth. Instead, it argues that AI-mediated learning creates new conditions under which patterns of regulation, delegation, verification, iteration, epistemic control, and ethical responsibility may become more observable and pedagogically relevant. Drawing on research on learning styles, neuromyths, AI literacy, adaptive learning, assessment, self-regulated learning, metacognition, and epistemic agency, the paper proposes a conceptual framework for regulation and agency styles in AI-mediated learning. In this framework, style is not treated as a fixed psychological trait or as a category into which learners should be sorted, but as a situated and modifiable profile of decisions and actions distributed across learners, tasks, and intelligent systems. Three analytical dimensions are proposed: epistemic control and metacognitive orchestration, adaptive regulation and generative iteration, and socio-algorithmic agency and ethical governance. The paper concludes with implications for task design, assessment, teacher education, and equitable AI integration. Full article

Background: The rapid digitalisation of healthcare, the expanding use of artificial intelligence, and the development of cross-border medical services have significantly transformed medical practice. These developments have also intensified medico-legal risks and professional liability exposure for healthcare professionals. Despite these changes, legal education remains insufficiently integrated into medical training, creating a gap between regulatory requirements and clinical practice. Methods: This study is based on a narrative analysis of the international literature addressing medico-legal challenges in healthcare, including medical errors, malpractice litigation, patient safety, digital health, and artificial intelligence. Drawing on international regulatory standards and educational frameworks, a conceptual model was developed to support the integration of legal education into medical curricula. Results: The analysis highlights persistent deficiencies in legal literacy among healthcare professionals, regardless of speciality or level of experience. Evidence from the reviewed literature suggests an association between lower levels of legal knowledge and increased professional vulnerability, including reported malpractice litigation and patient safety incidents. The proposed conceptual framework outlines a structured and longitudinal approach to integrating legal education into medical training, based on horizontal and vertical curricular integration, alignment with regulatory standards, and continuous professional development. Conclusions: Integrating legal education into medical curricula may represent a relevant strategy for supporting patient safety, strengthening medico-legal risk awareness, and promoting professional accountability. The proposed framework offers a conceptual and adaptable structure for medical schools and healthcare institutions. Full article

Generative artificial intelligence (GenAI) is rapidly reshaping the audience’s relationship with journalism, particularly through AI-generated summaries. Building on the observed patterns of limited visibility of sources, condensed summary presentation, and reduced contextual depth, we introduce the idea of news sufficiency, in which people encounter summarised content that feels enough to satisfy their immediate informational needs, thereby reducing the incentive to access full news articles. Empirically, this study examines differences between ChatGPT, Gemini, and Google Search when queried in European Portuguese, using three prompts: (a) asking for the main news of the day, (b) the latest on a specific news event, and (c) using only a basic keyword for that event. Drawing on a content analysis, we analysed 72 queries submitted by eight independent users. The results showed that platforms differ markedly in source attribution, summary structure, and contextual awareness. ChatGPT consistently provided hyperlinks and often clickable news images, while Gemini offered summaries only in text without citations. Both chatbots generated diverse and coherent news headlines capable of satisfying curiosity yet frequently failed to infer news intent from the basic keyword prompt, providing outdated or irrelevant information. The absence of Google’s AI Overview results during the observation window produced a contrast with a classic Google Search, which preserves user agency by requiring clicks on links. These findings have significant implications for journalistic authorship, editorial gatekeeping, and the economic sustainability of media, highlighting the need for AI literacy and platform governance that safeguards information pluralism. Full article

Artificial Intelligence (AI) is redefining journalism, transforming news production, distribution, and consumption. Despite the growing adoption of AI in newsrooms, academic research remains fragmented and lacks a comprehensive overview of global trends, particularly regarding its implications for media literacy and disinformation. This article addresses this gap through a systematic literature review of publications on AI in journalism between 2020 and 2024, based on Scopus and Web of Science. The analysis shows a significant increase in research output since 2023, alongside a strong geographical concentration in Europe and North America, with limited representation from the Global South. The results identify six key thematic areas: automation of news production, algorithmic personalization, newsroom integration, ethical and regulatory challenges, disinformation, and journalism education. Across these themes, the findings reveal a shift from a predominantly technocentric perspective toward a competency-based approach, with increasing attention to media literacy. While AI is associated with efficiency and innovation, the literature consistently highlights concerns related to transparency, bias, editorial accountability, and the transformation of professional roles, as well as risks linked to misinformation and declining public trust. Importantly, the review shows that media literacy—particularly AI literacy—emerges as a transversal dimension, emphasizing the need for competencies that enable journalists and audiences to critically understand, evaluate, and engage with algorithmic systems. The study also identifies key gaps, including limited longitudinal research, scarce audience-centered approaches, insufficient interdisciplinary collaboration, and persistent deficiencies in AI-related training. By foregrounding literacy as a central analytical dimension, this article advances a more holistic understanding of AI in journalism and contributes to the development of a globally inclusive and competency-oriented research agenda. Full article

This study investigated the implications of integrating artificial intelligence (AI) technologies into fashion design education and their relationship with supporting sustainable design practices. A descriptive–analytical approach was adopted, and an online questionnaire was administered to a sample of 404 female students and graduates specializing in fashion design in Saudi Arabia. The study examined the perceived usefulness of AI technologies in supporting sustainable design, the level of sustainable design self-efficacy, the relationship between these two variables, and differences associated with selected demographic variables. In addition, two optional open-ended questions provided complementary qualitative insights into participants’ perceptions of AI in sustainable fashion design. The findings revealed a high level of perceived usefulness of AI technologies in supporting sustainable design practices, as well as a high level of sustainable design self-efficacy among the participants. Statistically significant differences were found in perceived usefulness according to participants’ level of experience with AI tools and frequency of use, in favor of those with greater experience and more frequent use. The results also showed a strong positive relationship between perceived usefulness and sustainable design self-efficacy, along with statistically significant differences according to academic status, educational level, and geographic region. The qualitative findings further highlighted the role of AI in enhancing creativity, improving design efficiency, and reducing waste, while also pointing to challenges related to AI literacy, intellectual property, and ethical use. The study contributes empirical evidence from fashion design education in Saudi Arabia, a context that remains underrepresented in previous research, and emphasizes the importance of integrating AI technologies into fashion design education through educational and ethical frameworks that promote responsible AI use and sustainability-oriented design competencies. Full article

This systematic review examines AI literacy in English language education through a social–ecological perspective. Guided by Bronfenbrenner’s ecological systems theory, the review synthesizes 51 empirical studies (published since 2024) to examine how AI literacy is conceptualized, operationalized, and related to English language learning and teaching outcomes. The findings indicate predominantly positive relationships between AI literacy and language outcomes, including improvements in L2 writing proficiency, motivation, willingness to communicate, and teacher efficacy. However, the literature is dominated by microsystem-level studies, while meso-, exo-, and macro-level influences remain underexplored. This review contributes to the field by identifying critical gaps in multi-level research and advancing AI literacy as an ecologically embedded construct. Full article

Background and Objectives: Generative artificial intelligence (AI) chatbots have entered medical education faster than guidance for their responsible use. Although a rapidly expanding 2024–2026 literature has examined generative AI adoption, attitudes, and AI literacy among healthcare students, comparatively little is known about which specific tools medical students prefer or whether reliance on them carries measurable cognitive risks. We characterized adoption patterns, tool preferences, perceived benefits, and determinants of cognitive overdependence among medical students. Methods: A single-center cross-sectional survey was administered to 141 medical students across preclinical and clinical years at a single institution. A 28-item instrument captured usage patterns, perceived learning benefit, output trust, verification behavior, and AI overdependence risk. Analyses included t-tests, ANOVA, chi-square tests, Pearson correlations, and hierarchical regression. Results: Unless otherwise specified, values are reported as group mean scores on 1–5 Likert agreement scales or as percentages of respondents. ChatGPT was the primary tool for 69.5% of respondents, followed by Claude (12.1%). Daily users reported greater perceived learning benefit than infrequent users (4.14 vs. 3.36; p < 0.001). Clinical students verified AI outputs more often than preclinical students (3.69 vs. 3.21; p < 0.001), while preclinical students showed higher reliance (p = 0.002); verification moderated overdependence risk across academic years (interaction p = 0.041). AI familiarity (β = 0.31) and verification habit (β = −0.22) were the strongest predictors of integration acceptance (R² = 0.34). Conclusions: Reliance and verification habits diverge by training stage; curricula should pair AI literacy with explicit verification training to mitigate overdependence. As a single-center, self-report study, these findings require multi-center confirmation; nonetheless, to our knowledge, this is among the first studies to jointly profile students’ tool-specific reliability perceptions and to identify verification behavior as a moderator that buffers familiarity-driven overdependence. Full article

Generative artificial intelligence (AI) is rapidly transforming higher education, yet comparative evidence from diverse international settings remains limited. This cross-national study examined undergraduate students’ adoption and perceived academic impact of generative AI tools at institutions in Indonesia, Tajikistan, and the United States. A harmonized online survey was administered to 584 undergraduates during Spring 2026 (Indonesia n = 235; Tajikistan n = 226; United States n = 123). The survey assessed AI familiarity, adoption patterns, frequency and purpose of use, perceived academic impact, and factors encouraging AI engagement. AI adoption differed significantly across sites, with the highest adoption observed in Indonesia (84.3%), followed by Tajikistan (67.7%) and the United States (47.2%). These differences remained significant after adjustment for demographic and educational variables. Self-rated AI familiarity was the strongest predictor of both adoption and perceived academic benefit across all three settings. Differences across sites persisted even after accounting for participants’ academic discipline. The perceived-impact scale demonstrated excellent internal consistency (Cronbach’s α = 0.907). Brainstorming, writing assistance, and broader task diversification were associated with higher perceived benefit. Exploratory analyses found limited evidence linking learning-style preferences to AI use patterns. The findings highlight the importance of institutional context, self-rated AI familiarity, and curriculum integration in shaping student engagement with generative AI. Full article

Background/Objectives: Patient-facing artificial intelligence chatbots are increasingly used as informal digital health education tools. In orthodontics, eating and drinking advice may directly affect appliance integrity, oral hygiene, enamel demineralization, caries risk, and clear aligner use. This study compared the understandability, actionability, overall quality, and potential harmfulness of responses generated by free and paid versions of ChatGPT and Gemini to patient-oriented orthodontic dietary questions. Methods: This cross-sectional comparative observational study evaluated 160 responses generated from 40 Turkish patient-oriented orthodontic eating and drinking questions across five clinically relevant categories. Each question was submitted separately to ChatGPT free version, ChatGPT Plus, Gemini free version, and Gemini Pro on 1 May 2026, using newly opened independent chat sessions without prompt engineering, follow-up prompts, response regeneration, or manual editing. Responses were anonymized, randomly coded, and independently evaluated by two specialist dentists. PEMAT-P actionability was defined as the primary outcome. PEMAT-P understandability, Global Quality Score, and potentially harmful advice classification were secondary outcomes. Repeated-measures comparisons were performed using Friedman tests and Bonferroni-adjusted Wilcoxon signed-rank tests. Results: PEMAT-P understandability was high across all groups, with median scores of 100.0 in every group. Significant group differences were found for understandability, actionability, and Global Quality Score. ChatGPT Plus achieved the highest actionability score and Global Quality Score and produced no responses classified as potentially harmful. Potentially harmful responses were identified in ChatGPT free version, Gemini free version, and Gemini Pro. For the primary outcome, PEMAT-P actionability, the overall group difference was statistically significant with a small effect size (χ² = 20.527, p < 0.001, Kendall’s W = 0.171), while the largest effect was observed for GQS with a moderate effect size (χ² = 46.520, p < 0.001, Kendall’s W = 0.388). Conclusions: All chatbot groups generated highly understandable responses; however, actionability, overall quality, and safety varied across systems. ChatGPT Plus showed the strongest overall performance under the specific interface, subscription, language, and date conditions tested; however, this finding should be interpreted as a time-specific benchmark rather than evidence that paid chatbot systems are intrinsically safer or more clinically reliable. Structured evaluation, transparent reporting, digital health equity considerations, and professional oversight remain necessary before AI-generated orthodontic dietary advice can be integrated into routine patient education. Full article

This qualitative study, grounded in a phenomenological-interpretive design, aimed to explore university students’ experiences regarding digital hyperconnectivity, emotional well-being, and the balance between academic and personal life at a private university in Viña del Mar, Chile. The sample consisted of 18 Occupational Therapy students (mean age: 22.4 years; 61.1% female, 27.8% male, 5.6% non-binary; representing a diverse range of undergraduate academic years from second to fifth). Data were collected through individual semi-structured interviews and analyzed using open, axial, and selective coding following grounded theory guidelines, with the assistance of Atlas.ti v25 software, to identify emerging categories and establish conceptual relationships. Findings were organized into two analytical categories. The first, Experiences and perceptions of digital hyperconnectivity, portrays hyperconnectivity as a taken-for-granted everyday condition shaped by intensive device use and the predominance of social media, streaming platforms, and artificial intelligence tools; within this category, a tension emerges between recognized practical/pedagogical usefulness and distracting or compulsive patterns of use. The second, Emotional well-being and balance in academic life, highlights the impact on emotions, time management, sleep, and relationships, as well as disconnection strategies to restore balance (time limits, mental breaks, and offline activities). In conclusion, digital hyperconnectivity is significantly associated with emotional well-being and the academic-personal life balance. Students’ narratives suggest that technology can enrich learning when accompanied by regulation, media literacy, and deliberate disconnection practices. Therefore, context-sensitive institutional interventions are needed to promote conscious disconnection, digital identity education, and a pedagogical design that integrates digital tools through a critical and ethical approach. Full article

The use of Artificial Intelligence (AI) in Formative and Shared Assessment (F&SA) processes refers to the application of AI-based technologies to support formative and continuous assessment in Higher Education (HE). F&SA systems involve the ongoing monitoring of students’ learning, the provision of feedback that enables them to regulate and improve their performance, and the collection of information that informs the continuous improvement of teaching practice. In this context, AI can serve a dual purpose: when orientated towards students, it enhances learning outcomes; when directed at educators, it supports the development of their pedagogical expertise through tools designed to assist in the creation of assessment instruments, the generation of automated feedback, the analysis of learning data, and the design of simulation environments that foster the development of professional competencies. The integration of AI into F&SA practices holds considerable potential to transform traditional assessment approaches by enabling more personalised, adaptive, and timely feedback for both students and educators. In this shared assessment framework, students may likewise draw on AI applications to support specific dimensions of their learning, including academic writing, knowledge organisation, and the generation of educational content, thereby becoming active participants in their own assessment processes. However, the incorporation of AI into F&SA also requires careful consideration of the pedagogical, ethical, and institutional challenges it entails, particularly those related to academic integrity, cognitive offloading, and the responsible use of AI tools. It is therefore essential to promote AI literacy in HE among both faculty members and students, fostering a critical and informed engagement with these technologies that ensures the pedagogical relationship, along with the shared, formative nature of assessment, remains at the core of meaningful learning processes. Full article

The rapid adoption of generative artificial intelligence (GenAI) has created a practical problem for virtual higher education: universities must distinguish legitimate AI-supported learning from undisclosed delegation of academic work, while maintaining valid, fair, and privacy-sensitive assessment. This study compared student and faculty perceptions of AI use and academic integrity at the Technical University of Machala, Ecuador. A descriptive-comparative cross-sectional survey was administered to 1660 students and 34 faculty members during the second academic semester of 2024. The student questionnaire examined AI-use frequency, perceived academic benefit, readiness for non-assisted assessment, observation of dishonest online practices, perceived efficacy of virtual assessment, and attitudes toward proctoring. The faculty questionnaire examined suspected AI-generated submissions, responses to suspected use, perceived assessment efficacy, training, control tools, ethical judgments and proctoring. Findings indicate a transitional integrity landscape: students view AI mainly as a useful academic support, whereas faculty interpret it primarily through authorship, evidence and assessment-security concerns. Both groups report limitations in current virtual assessment, suggesting the need for AI-resilient assessment design, explicit disclosure rules, faculty development, student AI literacy, and proportional use of proctoring. The article argues against both blanket prohibition and permissive ambiguity, proposing a governance model grounded in transparent policy, authentic assessment, due process and human-centered AI literacy. Full article

The rise in political deepfakes threatens news credibility and media literacy in polarized higher education contexts; yet, Latin American students remain understudied. This study examined the digital media literacy of Peruvian university students (n = 5854) in assessing the credibility of political news and identifying AI-generated content. Using a quantitative, non-experimental, cross-sectional design, PROCESS Models 4 and 5 were applied to analyze the relationship between AI literacy and news credibility assessment ability. Awareness of political deepfakes and political news verification strategies were examined as parallel mediators, while the type of university was evaluated as a moderator. Previous exposure to AI, digital media consumption, and sociodemographic characteristics were included as control variables. AI literacy was positively associated with news credibility assessment ability, with awareness of political deepfakes and verification strategies contributing to this relationship. The analysis also examined institutional differences between public and private universities. These findings support the implementation of systematic AI literacy programs across disciplines and institutions to strengthen news credibility and democratic resilience in politically polarized contexts. Full article

As artificial intelligence (AI) rapidly permeates entrepreneurial ecosystems, understanding how technological literacy relates to entrepreneurial team well-being has become an urgent priority. Drawing on conservation of resources (COR) theory, this study examines the relationship between entrepreneurial teams’ AI literacy and team well-being (distinguishing between hedonic and eudaimonic well-being). Furthermore, it investigates the mediating roles of entrepreneurial team efficacy and collective AI anxiety. Data were collected from a survey of 271 entrepreneurial teams across four major economic zones in China. This study relies on team leaders as primary informants to report team-level perceptions. The results show that AI literacy is positively related to team hedonic well-being, but exhibits no significant direct relationship with team eudaimonic well-being. However, AI literacy is indirectly associated with entrepreneurial team well-being through two parallel pathways: entrepreneurial team efficacy and collective AI anxiety. The findings shed light on how technological literacy is linked to team mental health via the dual mechanisms of cognitive resource gain and emotional loss prevention. This study broadens our understanding of well-being at the entrepreneurial team level and offers insights into cultivating literacy, enhancing efficacy, and managing emotions to improve entrepreneurial team well-being. Full article