Search Results (34)

The temperate Himalayan states of Jammu and Kashmir, Himachal Pradesh, Uttarakhand, Ladakh, Sikkim, and Arunachal Pradesh in India face unique agro-ecological challenges across agriculture and allied sectors, including pest and disease pressures, inefficient resource use, post-harvest losses, and fragmented supply chains. This review systematically examines 21 critical problem areas, with three key challenges identified per sector across agriculture, agricultural engineering, fisheries, forestry, horticulture, sericulture, and animal husbandry. Artificial Intelligence (AI) and Machine Learning (ML) interventions, including computer vision, predictive modeling, Internet of Things (IoT)-based monitoring, robotics, and blockchain-enabled traceability, are evaluated for their regional applicability, pilot-level outcomes, and operational limitations under temperate Himalayan conditions. The analysis highlights that AI-enabled solutions demonstrate strong potential for early pest and disease detection, improved resource-use efficiency, ecosystem monitoring, and market integration. However, large-scale adoption remains constrained by limited digital infrastructure, data scarcity, high capital costs, low digital literacy, and fragmented institutional frameworks. The novelty of this review lies in its cross-sectoral synthesis of AI/ML applications tailored to the Himalayan context, combined with a sector-wise revenue-loss assessment to quantify economic impacts and guide prioritization. Based on the identified gaps, the review proposes feasible, context-aware strategies, including lightweight edge-AI models, localized data platforms, capacity-building initiatives, and policy-aligned implementation pathways. Collectively, these recommendations aim to enhance sustainability, resilience, and livelihood security across agriculture and allied sectors in the temperate Himalayan region. Full article

Understanding how investors maintain positions during adverse market conditions, investment grip, is increasingly important as retail participation rises and information environments diversify. While prior research identifies demographic, psychological, and economic determinants of investment grip, little is known about how information sources influence investors’ tolerance for losses. This study examines the relationship between investment information channels and investment grip among Japanese retail investors using a large-scale dataset of 161,677 respondents from the 2025 Survey on Life and Money. Investment grip is measured through a hypothetical loss scenario, and ordered probit and probit models are used to analyze associations between loss tolerance, information sources, and investor characteristics. Results show that reliance on professional information sources such as outsourced independent financial advisors, one’s own securities company, other securities firms, and external financial experts is negatively associated with investment grip. Free information sources, including mass media and personal networks, are also linked to lower loss tolerance. In contrast, reliance on social media is consistently associated with higher investment grip. Financial literacy, wealth, and age increase investment grip, whereas risk aversion, short-term outlooks, and family responsibilities reduce it. These results have implications for policy design, advisory practices, and digital and AI-enhanced investment platforms. Full article

This study examines the effectiveness of an eight-week AI training program aimed at enhancing teacher candidates’ pedagogical competence and AI literacy in rapidly changing and evolving educational environments. As the modern world continues to change and develop, the transformation of education, which is one of the most important elements of our lives, cannot be ignored. Accordingly, the integration of teacher candidates, who constitute key education stakeholders, into technological developments is very important in terms of both efficiency and sustainability. The “parallel–simultaneous design”, one of the mixed research methods in which quantitative and qualitative research methods are used together, was employed. In line with the stated purpose, the study started with a needs analysis conducted with 33 teacher candidates studying in different branches at the faculty of education. As a result of the needs analysis, knowledge gaps, digital skill levels and readiness for integration of artificial intelligence tools in future classrooms were determined. Its application to teacher candidates, instead of teachers in the profession, was determined by the needs analysis. The results indicate that it would be more beneficial to apply the education of the future to the teachers of the future and that they will find it easier to adapt to such training. Accordingly, a pre-test–post-test design was applied to observe how the participants changed, and an artificial intelligence literacy scale was also used. QDA Miner Lite was used for the analysis of the qualitative data, and SPSS 29.0 was used for the analysis of the quantitative data. During the eight-week training, Gamma programs were used for the presentation, Suno for audio, Midjourney for visuals and ChatGPT-4 for a descriptive search in order to provide better quality education to the participants. While practicing with these applications, the aim is to provide more up-to-date education that reveals problem-solving skills that include critical thinking exercises. According to the results, the teacher candidates who expressed that they were undecided or had insufficient knowledge reached a sufficient level in the post-test. In the light of these results, it can be stated that artificial-intelligence-oriented education is effective in developing sustainable pedagogical skills, digital literacy, readiness and professional self-confidence. The study also offers evidence-based recommendations for the design of future teacher training programs. Full article

The development of generative artificial intelligence tools, including large language models, opens new opportunities for adult education while simultaneously posing the risk of deepening inequalities resulting from differences in digital competences and individual dispositions. The aim of this article is to examine how digital skills (DS) and personal innovativeness (PI) shape differentiated and advanced use of ChatGPT (UC) among adult learners in Poland, with particular attention to the moderating role of gender. The study was conducted using the CAWI method on a nationwide sample of 757 adult ChatGPT users engaged in upgrading their qualifications. Validated scales of DS, PI, and UC were applied, along with confirmatory factor analysis (CFA) and structural equation modeling (SEM) using the WLSMV estimator, as well as multigroup SEM for women and men. The results confirm that both digital skills (β ≈ 0.46) and personal innovativeness (β ≈ 0.37) significantly and positively predict advanced use of ChatGPT, jointly explaining approximately 41% of the variance in UC, with stronger effects observed among men than women. Attention is therefore drawn to the need to incorporate a gender perspective in further research on the use of GenAI in adult education The findings point to a stratification of GenAI use in adult education and underscore the need to incorporate critical digital competences and AI literacy into sustainable education policies in order to limit the reproduction of existing inequalities. Full article

This research-to-practice study examines how Generative Artificial Intelligence (GenAI) can be integrated into live case studies to enhance experiential learning in higher education. It explores GenAI’s potential as an agent to learn with scaffolding reflection and engagement and addresses gaps in existing applications that often focus narrowly on content generation. To explore GenAI’s agentive potential, the methodology illustrates this approach in a UK postgraduate operations management module. Students engaged in a live case study of a local ethnic restaurant to refine its business model and operations. The data sources used to examine students’ results included module materials, outputs, and feedback surveys. Thematic analysis was employed to assess how GenAI facilitated experiential learning. The findings suggest that GenAI integration facilitated exploration, reflection, conceptualisation, and experimentation. Students reported that the activity was engaging and relevant, facilitating critical decision-making and understanding of operations management. However, the outcomes varied according to GenAI literacy and student participation. Although GenAI-enriched learning is beneficial, human agency and contextual knowledge remain crucial. Overall, this study integrates GenAI as a cognitive partner throughout Kolb’s ELC. This study offers a transferable framework for active learning, illustrating how technology can enhance critical and reflective learning in authentic educational contexts. However, limitations include uneven student participation and engagement, resource constraints, overreliance on artificial intelligence outputs, differentiated impact on learning outcomes, and a single-case report, which must be addressed before the framework can be scaled up. Future research should test this through multi-case studies while developing GenAI literacy, measuring GenAI impact, and implementing ethical practices in the field. Full article

Atrial fibrillation (AF) is the most common sustained arrhythmia and a growing global health burden, yet conventional monitoring with Holter devices, event recorders and implantable loop recorders often fails to adequately capture recurrence. Rapid advances in digital health, wearable biosensors and artificial intelligence (AI) have transformed consumer smartwatches and wearables into potential clinical tools capable of continuous, real-world rhythm surveillance. This narrative review synthesizes contemporary evidence on smartwatch-based AF monitoring, spanning core technologies—photoplethysmography, single-lead electrocardiography and AI fusion algorithms—and validation studies across post-ablation follow-up. Compared with traditional modalities, smartwatch-based AF monitoring demonstrates improved detection of AF recurrence, enhanced characterization of AF burden, symptom–rhythm correlation, and greater patient engagement. At the same time, key limitations are critically examined, including motion artifacts, false-positive alerts, short recording windows, adherence dependence, digital literacy and access gaps, as well as unresolved issues around regulation, interoperability and data privacy. By integrating engineering advances with guideline-directed care pathways, smartwatch-based AF monitoring holds promise to complement, rather than immediately replace, established diagnostic tools and to enable more proactive, individualized AF management. Future work must focus on robust clinical validation, equitable implementation and clear regulatory frameworks to safely scale these technologies. Full article

Background: Digital exclusion is a validated risk factor for cognitive decline in older adults. Digital interventions exhibit high dropout rates due to low digital literacy, technology anxiety, and limited adaptation to individual states, resulting in limited real-world transfer. Objective: This protocol aims to present the CTC Framework (Coach–Teacher–Companion), a tri-agent generative AI system proposed for exploring the feasibility of adaptive cognitive interventions in older adults with existing digital access. The protocol provides technical architecture, feasibility-stage implementation procedures, and methodological and ethical guidelines to assist clinicians in safely applying AI-based cognitive interventions in clinical research settings. Methods: The framework integrates three AI agents (Coach, Teacher, and Companion) designed to provide behavioral, cognitive, and emotional support. The system is designed to embed cognitive exercises in daily activities, monitor emotional states, and incorporate accessibility features for age-related limitations. Implementation safeguards include digital literacy assessment (MDPQ-16), technology anxiety monitoring (CARS), emotional safety protocols, and data privacy protections. The protocol specifies a six-week feasibility study (n$=14$, MMSE 18–25) to evaluate usability (System Usability Scale, primary outcome), user experience (UEQ-S), psychological needs satisfaction (BPNS), emotional safety (PANAS), adherence, and preliminary cognitive outcomes (MMSE, TMT-A/B, Digit Span). Conclusions: The CTC Framework is designed to provide methodological and ethical safeguards for clinical implementation, including standardized procedures for digital literacy assessment, technology anxiety management, emotional safety monitoring, and data privacy protections. Empirical validation of the framework’s feasibility and efficacy is required through future studies. Full article

The integration of generative artificial intelligence (AI) tools has become a game-changer in educational practices, particularly in collaborative academic writing. This study explores gender-based disparities in perceptions, emotions, and self-efficacy regarding students’ utilization of AI tools during a collaborative Wikibook writing project. Grounded in the Technology Acceptance Model (TAM), the research investigates how male and female undergraduates in Hong Kong perceive the usefulness and ease of use of ChatGPT 3.5 and Padlet AI image generation function, as well as their emotions and self-efficacy when engaging with these tools. Using a 5-point Likert scale questionnaire and an independent sample t-test, the study compares gender perspectives with a sample size of 140 undergraduates. The results reveal that (1) both genders found the AI tools beneficial for language polishing and essay reconstruction in academic writing; (2) both genders experienced a range of emotions, including enjoyment, satisfaction, frustration, anxiety and tension during the writing task; (3) both male and female students demonstrated AI literacy to critically evaluate AI-generated information. These findings underscore the importance of fostering an equitable and engaging approach to AI-supported learning environments for both genders. The study highlights the benefits of AI tools in enhancing learning outcomes and emphasizes the role of students’ AI literacy in ensuring the responsible and effective use of these tools as learning partners. Full article

Background/Objectives: Large language models (LLMs) show promise for patient education, yet their safety and efficacy for chronic diseases requiring lifelong management remain unclear. This study presents the first comprehensive comparative evaluation of three leading LLMs for celiac disease patient education. Methods: We conducted a cross-sectional evaluation comparing ChatGPT-4, Claude 3.7, and Gemini 2.0 using six blinded clinical specialists (four gastroenterologists and two dietitians). Twenty questions spanning four domains (general understanding, symptoms/diagnosis, diet/nutrition, lifestyle management) were evaluated for scientific accuracy, clarity (5-point Likert scales), misinformation presence, and readability using validated computational metrics (Flesch Reading Ease, Flesch-Kincaid Grade Level, SMOG index). Results: Gemini 2.0 demonstrated superior performance across multiple dimensions. Gemini 2.0 achieved the highest scientific accuracy ratings (median 4.5 [IQR: 4.5–5.0] vs. 4.0 [IQR: 4.0–4.5] for both competitors, p = 0.015) and clarity scores (median 5.0 [IQR: 4.5–5.0] vs. 4.0 [IQR: 4.0–4.5], p = 0.011). While Gemini 2.0 showed numerically lower misinformation rates (13.3% vs. 23.3% for ChatGPT–4 and 24.2% for Claude 3.7), differences were not statistically significant (p = 0.778). Gemini 2.0 achieved significantly superior readability, requiring approximately 2–3 fewer years of education for comprehension (median Flesch-Kincaid Grade Level 9.8 [IQR: 8.8–10.3] vs. 12.5 for both competitors, p < 0.001). However, all models exceeded recommended 6th–8th grade health literacy targets. Conclusions: While Gemini 2.0 demonstrated statistically significant advantages in accuracy, clarity, and readability, misinformation rates of 13.3–24.2% across all models represent concerning risk levels for direct patient applications. AI offers valuable educational support but requires healthcare provider supervision until misinformation rates improve. Full article

This study examined the utilization of artificial intelligence (AI) in inquiry-based science learning among gifted and non-gifted students. The participants included 484 students (197 gifted and 287 non-gifted; 226 males and 233 females) who completed three validated questionnaire instruments: the AI-Assisted Scientific Inquiry Learning Questionnaire (AASILQ), the AI-Assisted Science Learning Questionnaire (AASLQ), and the AI Literacy Questionnaire (AILQ). Factor analyses confirmed four latent constructs in the AASILQ, two in the AASLQ, and four in the AILQ, with all scales demonstrating strong internal consistency. Group comparisons were conducted according to educational placement and gender. The results indicated significant differences regarding educational placement: gifted students reported lower levels of AI-Assisted Scientific Inquiry Learning yet demonstrated higher AI literacy and greater confidence in the safe use of AI. Gender analyses revealed that female students expressed heightened concern regarding privacy issues. These findings extend the literature on AI integration in science education by highlighting nuanced differences in how gifted and non-gifted learners engage with AI, thereby offering implications for the design of equitable and responsive AI-supported learning environments. Full article

To address the pressing challenges facing global agriculture—including resource constraints, structural labour shortages, and climate change adaptation—exploring pathways for digital transformation is crucial for safeguarding regional food security and advancing sustainable agricultural development. Taking China’s Yangtze River Economic Belt as a case study, this research aims to dissect the interplay between the digital economy, new-quality agricultural productivity, and agricultural modernisation. Utilising panel data from 11 provinces and municipalities spanning 2013–2023, the study employs an entropy-weighted approach to construct a composite indicator system for these three core variables. Panel data analysis comprehensively employs random effects models, mediation effect tests, robustness checks, and heterogeneity analyses. Empirical results indicate that the digital economy exerts a significant positive driving effect on new-quality agricultural productivity. Mediation tests further reveal that agricultural modernisation plays a crucial mediating role in this relationship. Heterogeneity analysis finds that the promotional effect of the digital economy exhibits distinct regional gradient characteristics, being most pronounced in growth zones, followed by leading zones, and weakest in starting zones. These findings support the formulation of differentiated agricultural digitalization policies: Leading areas should focus on deep integration of AI and agricultural big data; growth zones require investments in scaling intelligent irrigation and UAV plant protection; and start-up areas should prioritize digital infrastructure and large-scale farmer digital literacy training to establish transformation foundations. Full article

The paper presents research and preliminary findings aimed at improving curricula so that digital competencies are aligned with the required Artificial Intelligence (AI) literacy. The research was conducted at the Faculty of Technical Sciences in Čačak, University of Kragujevac (Serbia). The participants in the research were future computer science teachers and IT engineering students. The research tool for self-evaluation of AI literacy was a questionnaire based on the Serbian version of the AILS (Artificial Intelligence Literacy Scale), while digital competencies, based on the DigComp framework, were determined by objective testing. The research took into account the socioeconomic status of the students, demographic characteristics, and English language proficiency. Preliminary results indicated the persistence of significant relationships between certain digital competencies (such as programming, digital signal processing, and creative thinking) and all four constructs of AI literacy. The research findings highlight the impact of AI literacy on data analysis performance and problem solving. Full article

Background/Objectives: The COVID-19 pandemic highlighted how infodemics—an excessive amount of both accurate and misleading information—undermine health responses. Artificial intelligence (AI) and digital tools have been increasingly applied to monitor, detect, and counter health misinformation online. This scoping review aims to systematically map digital and AI-based interventions, describing their applications, outcomes, ethical and equity implications, and policy frameworks. Methods: This review followed the Joanna Briggs Institute methodology and was reported according to PRISMA-ScR. The protocol was preregistered on the Open Science Framework . Searches were conducted in PubMed/MEDLINE, Scopus, Web of Science, and CINAHL (January 2017–March 2025). Two reviewers independently screened titles/abstracts and full texts; disagreements were resolved by a third reviewer. Data extraction included study characteristics, populations, technologies, outcomes, thematic areas, and domains. Quantitative synthesis used descriptive statistics with 95% confidence intervals. Results: A total of 63 studies were included, most published between 2020 and 2024. The majority originated from the Americas (41.3%), followed by Europe (15.9%), the Western Pacific (9.5%), and other regions; 22.2% had a global scope. The most frequent thematic areas were monitoring/surveillance (54.0%) and health communication (42.9%), followed by education/training, AI/ML model development, and digital engagement tools. The domains most often addressed were applications (63.5%), responsiveness, policies/strategies, ethical concerns, and equity/accessibility. Conclusions: AI and digital tools provide significant contributions in detecting misinformation, strengthening surveillance, and promoting health literacy. However, evidence remains heterogeneous, with geographic imbalances, reliance on proxy outcomes, and limited focus on vulnerable groups. Scaling these interventions requires transparent governance, multilingual datasets, ethical safeguards, and integration into public health infrastructures. Full article

Artificial Intelligence (AI) tools have transformed academic writing and literacy development in higher education. Students can now receive instant feedback on grammar, coherence, style, and argumentation using AI-powered writing assistants, like Grammarly, ChatGPT, and QuillBot. Moreover, these writing assistants can quickly produce completed essays and papers, leaving little else for the student to do aside from reading and perhaps editing the content. Many teachers are concerned that this erodes critical thinking skills and undermines ethical considerations since students are not performing the work themselves. This study addresses this concern by synthesizing and evaluating peer-reviewed literature on the effectiveness of AI in supporting writing pedagogy. Studies were selected based on their relevance and scholarly merit, following the Scale for the Assessment of Narrative Review Articles (SANRA) guidelines to ensure methodological rigor and quality. The findings reveal that although AI tools can be detrimental to the development of writing skills, they can foster self-directed learning and improvement when carefully integrated into coursework. They can facilitate enhanced writing fluency, offer personalized tutoring, and reduce the cognitive load of drafting and revising. This study also compares AI-assisted and traditional writing approaches and discusses best practices for integrating AI tools into curricula while preserving academic integrity and creativity in student writing. Full article

This study examines the relationship between attitudes toward artificial intelligence (AI) and AI dependency among Ecuadorian university students. A cross-sectional design was used, applying two validated instruments: the Artificial Intelligence Dependence Scale (DAI) and the General Attitudes Toward Artificial Intelligence Scale (GAAIS), with a sample of 540 students. Structural equation modeling (SEM) assessed how both positive and negative attitudes predict dependency levels. Results indicate a moderate level of AI dependency and an ambivalent attitudinal profile. Both attitudinal dimensions significantly predicted dependency, suggesting dual-use behaviors shaped by perceived utility and ethical concerns. Urban students reported higher dependency and greater sensitivity to AI-related risks, highlighting digital inequalities. Although the SEM model showed adequate comparative fit (CFI = 0.976; TLI = 0.973), residual indicators (RMSEA = 0.075) suggest further refinement is needed. This study contributes to underexplored Latin American contexts and emphasizes the need for equity-driven digital literacy strategies in higher education. Findings support pedagogical frameworks promoting critical thinking, ethical reasoning, and responsible AI use. The study aligns with Sustainable Development Goals 4 (Quality Education) and 10 (Reduced Inequalities), reinforcing the importance of inclusive, learner-centered approaches to AI integration. Full article