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Search Results (364)

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Keywords = educational transparency

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24 pages, 6260 KiB  
Article
Transforming Product Discovery and Interpretation Using Vision–Language Models
by Simona-Vasilica Oprea and Adela Bâra
J. Theor. Appl. Electron. Commer. Res. 2025, 20(3), 191; https://doi.org/10.3390/jtaer20030191 (registering DOI) - 1 Aug 2025
Abstract
In this work, the utility of multimodal vision–language models (VLMs) for visual product understanding in e-commerce is investigated, focusing on two complementary models: ColQwen2 (vidore/colqwen2-v1.0) and ColPali (vidore/colpali-v1.2-hf). These models are integrated into two architectures and evaluated across various [...] Read more.
In this work, the utility of multimodal vision–language models (VLMs) for visual product understanding in e-commerce is investigated, focusing on two complementary models: ColQwen2 (vidore/colqwen2-v1.0) and ColPali (vidore/colpali-v1.2-hf). These models are integrated into two architectures and evaluated across various product interpretation tasks, including image-grounded question answering, brand recognition and visual retrieval based on natural language prompts. ColQwen2, built on the Qwen2-VL backbone with LoRA-based adapter hot-swapping, demonstrates strong performance, allowing end-to-end image querying and text response synthesis. It excels at identifying attributes such as brand, color or usage based solely on product images and responds fluently to user questions. In contrast, ColPali, which utilizes the PaliGemma backbone, is optimized for explainability. It delivers detailed visual-token alignment maps that reveal how specific regions of an image contribute to retrieval decisions, offering transparency ideal for diagnostics or educational applications. Through comparative experiments using footwear imagery, it is demonstrated that ColQwen2 is highly effective in generating accurate responses to product-related questions, while ColPali provides fine-grained visual explanations that reinforce trust and model accountability. Full article
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17 pages, 2439 KiB  
Article
Monte Carlo-Based VaR Estimation and Backtesting Under Basel III
by Yueming Cheng
Risks 2025, 13(8), 146; https://doi.org/10.3390/risks13080146 (registering DOI) - 1 Aug 2025
Abstract
Value-at-Risk (VaR) is a key metric widely applied in market risk assessment and regulatory compliance under the Basel III framework. This study compares two Monte Carlo-based VaR models using publicly available equity data: a return-based model calibrated to historical portfolio volatility, and a [...] Read more.
Value-at-Risk (VaR) is a key metric widely applied in market risk assessment and regulatory compliance under the Basel III framework. This study compares two Monte Carlo-based VaR models using publicly available equity data: a return-based model calibrated to historical portfolio volatility, and a CAPM-style factor-based model that simulates risk via systematic factor exposures. The two models are applied to a technology-sector portfolio and evaluated under historical and rolling backtesting frameworks. Under the Basel III backtesting framework, both initially fall into the red zone, with 13 VaR violations. With rolling-window estimation, the return-based model shows modest improvement but remains in the red zone (11 exceptions), while the factor-based model reduces exceptions to eight, placing it into the yellow zone. These results demonstrate the advantages of incorporating factor structures for more stable exception behavior and improved regulatory performance. The proposed framework, fully transparent and reproducible, offers practical relevance for internal validation, educational use, and model benchmarking. Full article
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23 pages, 1192 KiB  
Article
Multi-Model Dialectical Evaluation of LLM Reasoning Chains: A Structured Framework with Dual Scoring Agents
by Catalin Anghel, Andreea Alexandra Anghel, Emilia Pecheanu, Ioan Susnea, Adina Cocu and Adrian Istrate
Informatics 2025, 12(3), 76; https://doi.org/10.3390/informatics12030076 (registering DOI) - 1 Aug 2025
Abstract
(1) Background and objectives: Large language models (LLMs) such as GPT, Mistral, and LLaMA exhibit strong capabilities in text generation, yet assessing the quality of their reasoning—particularly in open-ended and argumentative contexts—remains a persistent challenge. This study introduces Dialectical Agent, an internally developed [...] Read more.
(1) Background and objectives: Large language models (LLMs) such as GPT, Mistral, and LLaMA exhibit strong capabilities in text generation, yet assessing the quality of their reasoning—particularly in open-ended and argumentative contexts—remains a persistent challenge. This study introduces Dialectical Agent, an internally developed modular framework designed to evaluate reasoning through a structured three-stage process: opinion, counterargument, and synthesis. The framework enables transparent and comparative analysis of how different LLMs handle dialectical reasoning. (2) Methods: Each stage is executed by a single model, and final syntheses are scored via two independent LLM evaluators (LLaMA 3.1 and GPT-4o) based on a rubric with four dimensions: clarity, coherence, originality, and dialecticality. In parallel, a rule-based semantic analyzer detects rhetorical anomalies and ethical values. All outputs and metadata are stored in a Neo4j graph database for structured exploration. (3) Results: The system was applied to four open-weight models (Gemma 7B, Mistral 7B, Dolphin-Mistral, Zephyr 7B) across ten open-ended prompts on ethical, political, and technological topics. The results show consistent stylistic and semantic variation across models, with moderate inter-rater agreement. Semantic diagnostics revealed differences in value expression and rhetorical flaws not captured by rubric scores. (4) Originality: The framework is, to our knowledge, the first to integrate multi-stage reasoning, rubric-based and semantic evaluation, and graph-based storage into a single system. It enables replicable, interpretable, and multidimensional assessment of generative reasoning—supporting researchers, developers, and educators working with LLMs in high-stakes contexts. Full article
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24 pages, 624 KiB  
Systematic Review
Integrating Artificial Intelligence into Perinatal Care Pathways: A Scoping Review of Reviews of Applications, Outcomes, and Equity
by Rabie Adel El Arab, Omayma Abdulaziz Al Moosa, Zahraa Albahrani, Israa Alkhalil, Joel Somerville and Fuad Abuadas
Nurs. Rep. 2025, 15(8), 281; https://doi.org/10.3390/nursrep15080281 (registering DOI) - 31 Jul 2025
Abstract
Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping [...] Read more.
Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article
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36 pages, 856 KiB  
Systematic Review
Is Blockchain the Future of AI Alignment? Developing a Framework and a Research Agenda Based on a Systematic Literature Review
by Alexander Neulinger, Lukas Sparer, Maryam Roshanaei, Dragutin Ostojić, Jainil Kakka and Dušan Ramljak
J. Cybersecur. Priv. 2025, 5(3), 50; https://doi.org/10.3390/jcp5030050 - 29 Jul 2025
Viewed by 381
Abstract
Artificial intelligence (AI) agents are increasingly shaping vital sectors of society, including healthcare, education, supply chains, and finance. As their influence grows, AI alignment research plays a pivotal role in ensuring these systems are trustworthy, transparent, and aligned with human values. Leveraging blockchain [...] Read more.
Artificial intelligence (AI) agents are increasingly shaping vital sectors of society, including healthcare, education, supply chains, and finance. As their influence grows, AI alignment research plays a pivotal role in ensuring these systems are trustworthy, transparent, and aligned with human values. Leveraging blockchain technology, proven over the past decade in enabling transparent, tamper-resistant distributed systems, offers significant potential to strengthen AI alignment. However, despite its potential, the current AI alignment literature has yet to systematically explore the effectiveness of blockchain in facilitating secure and ethical behavior in AI agents. While existing systematic literature reviews (SLRs) in AI alignment address various aspects of AI safety and AI alignment, this SLR specifically examines the gap at the intersection of AI alignment, blockchain, and ethics. To address this gap, this SLR explores how blockchain technology can overcome the limitations of existing AI alignment approaches. We searched for studies containing keywords from AI, blockchain, and ethics domains in the Scopus database, identifying 7110 initial records on 28 May 2024. We excluded studies which did not answer our research questions and did not discuss the thematic intersection between AI, blockchain, and ethics to a sufficient extent. The quality of the selected studies was assessed on the basis of their methodology, clarity, completeness, and transparency, resulting in a final number of 46 included studies, the majority of which were journal articles. Results were synthesized through quantitative topic analysis and qualitative analysis to identify key themes and patterns. The contributions of this paper include the following: (i) presentation of the results of an SLR conducted to identify, extract, evaluate, and synthesize studies on the symbiosis of AI alignment, blockchain, and ethics; (ii) summary and categorization of the existing benefits and challenges in incorporating blockchain for AI alignment within the context of ethics; (iii) development of a framework that will facilitate new research activities; and (iv) establishment of the state of evidence with in-depth assessment. The proposed blockchain-based AI alignment framework in this study demonstrates that integrating blockchain with AI alignment can substantially enhance robustness, promote public trust, and facilitate ethical compliance in AI systems. Full article
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46 pages, 1185 KiB  
Review
Shared Producer Responsibility for Sustainable Packaging in FMCG: The Convergence of SDGs, ESG Reporting, and Stakeholder Engagement
by Fotios Misopoulos and Priyanka Bajiraj
Sustainability 2025, 17(14), 6654; https://doi.org/10.3390/su17146654 - 21 Jul 2025
Viewed by 366
Abstract
Packaging waste is a major environmental issue, making the transition to sustainable solutions imperative. This article proposes the concept of Shared Producer Responsibility (SPR) as a key approach to advancing sustainable packaging in the fast-moving consumer goods (FMCG) sector. The study explores how [...] Read more.
Packaging waste is a major environmental issue, making the transition to sustainable solutions imperative. This article proposes the concept of Shared Producer Responsibility (SPR) as a key approach to advancing sustainable packaging in the fast-moving consumer goods (FMCG) sector. The study explores how the United Nations Sustainable Development Goals (SDGs), environmental, social, and governance (ESG) reporting, and stakeholder engagement converge to support this transition. The research identifies current trends, challenges, and gaps in sustainable packaging practices through a systematic literature review (SLR) and analysis of sustainability and ESG reports from leading FMCG and packaging companies. The findings highlight the need for standardised reporting frameworks and improved stakeholder cooperation to enhance transparency and accountability in sustainability efforts. This study proposes a conceptual framework for accelerating sustainable packaging adoption through combining strategies like consumer education, regulatory incentives, and clear product labelling. The proposal to implement the concept of Shared Producer Responsibility emphasises the shared accountability of FMCG companies and packaging manufacturers in managing the full environmental lifecycle of packaging materials. This approach is crucial for achieving SDG 12 (responsible consumption and production) and SDG 13 (climate action) and driving more effective and sustainable packaging practices across the FMCG industry. Full article
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24 pages, 327 KiB  
Article
Trust in Generative AI Tools: A Comparative Study of Higher Education Students, Teachers, and Researchers
by Elena Đerić, Domagoj Frank and Marin Milković
Information 2025, 16(7), 622; https://doi.org/10.3390/info16070622 - 21 Jul 2025
Viewed by 619
Abstract
Generative AI (GenAI) tools, including ChatGPT, Microsoft Copilot, and Google Gemini, are rapidly reshaping higher education by transforming how students, educators, and researchers engage with learning, teaching, and academic work. Despite their growing presence, the adoption of GenAI remains inconsistent, largely due to [...] Read more.
Generative AI (GenAI) tools, including ChatGPT, Microsoft Copilot, and Google Gemini, are rapidly reshaping higher education by transforming how students, educators, and researchers engage with learning, teaching, and academic work. Despite their growing presence, the adoption of GenAI remains inconsistent, largely due to the absence of universal guidelines and trust-related concerns. This study examines how trust, defined across three key dimensions (accuracy and relevance, privacy protection, and nonmaliciousness), influences the adoption and use of GenAI tools in academic environments. Using survey data from 823 participants across different academic roles, this study employs multiple regression analysis to explore the relationship between trust, user characteristics, and behavioral intention. The results reveal that trust is primarily experience-driven. Frequency of use, duration of use, and self-assessed proficiency significantly predict trust, whereas demographic factors, such as gender and academic role, have no significant influence. Furthermore, trust emerges as a strong predictor of behavioral intention to adopt GenAI tools. These findings reinforce trust calibration theory and extend the UTAUT2 framework to the context of GenAI in education. This study highlights that fostering appropriate trust through transparent policies, privacy safeguards, and practical training is critical for enabling responsible, ethical, and effective integration of GenAI into higher education. Full article
(This article belongs to the Section Artificial Intelligence)
20 pages, 535 KiB  
Article
Ethical Perceptions and Trust in Green Dining: A Qualitative Case Study of Consumers in Missouri, USA
by Lu-Ping Lin, Pei Liu and Qianni Zhu
Sustainability 2025, 17(14), 6493; https://doi.org/10.3390/su17146493 - 16 Jul 2025
Viewed by 349
Abstract
This qualitative case study explores Missouri-based consumers’ ethical beliefs regarding restaurant sourcing from minority farmers. Guided by the Hunt–Vitell theory of ethics (H-V model), it applies the model in a new context: culturally inclusive restaurant sourcing. Based on 15 semi-structured interviews conducted between [...] Read more.
This qualitative case study explores Missouri-based consumers’ ethical beliefs regarding restaurant sourcing from minority farmers. Guided by the Hunt–Vitell theory of ethics (H-V model), it applies the model in a new context: culturally inclusive restaurant sourcing. Based on 15 semi-structured interviews conducted between September 2024 and October 2024, the study explores how ethical beliefs shape dining intentions. Participants generally viewed support for minority farmers as ethically appropriate. Thematic analysis revealed six key themes: (1) community-oriented social values (e.g., social responsibility toward local businesses); (2) cultural identity (e.g., traditional farming methods); (3) consumer values—food-oriented (e.g., quality); (4) consumer values—people-oriented (e.g., financial support for ethical sourcing); (5) trust-building mechanisms (e.g., sourcing transparency); and (6) barriers (e.g., lack of awareness). These findings highlight limited consumer awareness of minority farmers and the need for transparent communication and cultural education. The study contributes theoretically by extending the H-V model to the intersection of ethics, culture, and restaurant sourcing. Practically, it offers guidance for restaurant managers, marketers, and policymakers to support minority farmers, build trust, and promote inclusive and socially responsible dining. One key limitation of this study is its reliance on a small, Missouri-based consumer sample, which limits generalizability and excludes perspectives from other stakeholders. However, as a regional case study, it provides important depth and contextual insight into an underexplored aspect of sustainable sourcing. This study also highlights the need for multi-stakeholder engagement to advance equity in the food system. Full article
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22 pages, 1642 KiB  
Article
Artificial Intelligence and Journalistic Ethics: A Comparative Analysis of AI-Generated Content and Traditional Journalism
by Rimma Zhaxylykbayeva, Aizhan Burkitbayeva, Baurzhan Zhakhyp, Klara Kabylgazina and Gulmira Ashirbekova
Journal. Media 2025, 6(3), 105; https://doi.org/10.3390/journalmedia6030105 - 15 Jul 2025
Viewed by 647
Abstract
This article presents a comparative study of content generated by artificial intelligence (AI) and articles authored by professional journalists, focusing on the perspective of a Kazakhstani audience. The analysis was conducted based on several key criteria, including the structure of the article, writing [...] Read more.
This article presents a comparative study of content generated by artificial intelligence (AI) and articles authored by professional journalists, focusing on the perspective of a Kazakhstani audience. The analysis was conducted based on several key criteria, including the structure of the article, writing style, factual accuracy, citation of sources, and completeness of the information. The study spans a variety of topics, such as politics, economics, law, sports, education, and social issues. The results indicate that AI-generated articles tend to exhibit greater structural clarity and neutrality. On the other hand, articles written by journalists score higher in terms of factual accuracy, analytical depth, and the use of verified sources. Furthermore, the research explores the significance of journalistic ethics in ensuring transparency and information completeness in content production. Ultimately, the findings emphasize the importance of upholding rigorous journalistic standards when integrating AI into media practices. Full article
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36 pages, 1120 KiB  
Article
Triple-Shield Privacy in Healthcare: Federated Learning, p-ABCs, and Distributed Ledger Authentication
by Sofia Sakka, Nikolaos Pavlidis, Vasiliki Liagkou, Ioannis Panges, Despina Elizabeth Filippidou, Chrysostomos Stylios and Anastasios Manos
J. Cybersecur. Priv. 2025, 5(3), 45; https://doi.org/10.3390/jcp5030045 - 12 Jul 2025
Viewed by 464
Abstract
The growing influence of technology in the healthcare industry has led to the creation of innovative applications that improve convenience, accessibility, and diagnostic accuracy. However, health applications face significant challenges concerning user privacy and data security, as they handle extremely sensitive personal and [...] Read more.
The growing influence of technology in the healthcare industry has led to the creation of innovative applications that improve convenience, accessibility, and diagnostic accuracy. However, health applications face significant challenges concerning user privacy and data security, as they handle extremely sensitive personal and medical information. Privacy-Enhancing Technologies (PETs), such as Privacy-Attribute-based Credentials, Differential Privacy, and Federated Learning, have emerged as crucial tools to tackle these challenges. Despite their potential, PETs are not widely utilized due to technical and implementation obstacles. This research introduces a comprehensive framework for protecting health applications from privacy and security threats, with a specific emphasis on gamified mental health apps designed to manage Attention Deficit Hyperactivity Disorder (ADHD) in children. Acknowledging the heightened sensitivity of mental health data, especially in applications for children, our framework prioritizes user-centered design and strong privacy measures. We suggest an identity management system based on blockchain technology to ensure secure and transparent credential management and incorporate Federated Learning to enable privacy-preserving AI-driven predictions. These advancements ensure compliance with data protection regulations, like GDPR, while meeting the needs of various stakeholders, including children, parents, educators, and healthcare professionals. Full article
(This article belongs to the Special Issue Data Protection and Privacy)
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24 pages, 237 KiB  
Article
Student Perceptions of Sustainability in the HoReCa Sector: Awareness, Engagement, and Challenges
by Marian Mocan, Larisa Ivascu, Timea Agache and Andrei Agache
Sustainability 2025, 17(14), 6384; https://doi.org/10.3390/su17146384 - 11 Jul 2025
Viewed by 312
Abstract
The HoReCa (Hotels, Restaurants, and Cafes) sector plays a pivotal role in the economy due to its strong connections with various other industries, including agriculture, food and beverage, construction, packaging, waste management, water, and textiles. Given its broad impact, understanding the perceptions of [...] Read more.
The HoReCa (Hotels, Restaurants, and Cafes) sector plays a pivotal role in the economy due to its strong connections with various other industries, including agriculture, food and beverage, construction, packaging, waste management, water, and textiles. Given its broad impact, understanding the perceptions of students—emerging consumers and future professionals—could provide valuable insights for businesses seeking to enhance sustainable practices in ways that resonate with younger generations and improve their competitiveness. However, there is still limited understanding of how students perceive and engage with sustainability in this sector. This study explores student perceptions of sustainability practices within the HoReCa sector, examining their awareness levels, expectations, and behavior. The objective is to assess how effectively current business approaches align with student values regarding sustainability initiatives and identify key factors influencing their engagement. A structured questionnaire was distributed among university students, and the collected data was analyzed using statistical techniques to identify meaningful trends and correlations. Findings revealed a notable disconnect between students’ professed sustainability values and their actual behavior. Primary obstacles included price sensitivity, skepticism toward environmental marketing claims, and insufficient access to clear sustainability information from businesses. Despite supporting sustainable initiatives in principle, students often struggle to translate their values into purchasing decisions. The research suggests that greater business transparency, enhanced sustainability education, and incentive programs could foster increased student engagement. Full article
21 pages, 2063 KiB  
Article
Designing a Generalist Education AI Framework for Multimodal Learning and Ethical Data Governance
by Yuyang Yan, Hui Liu, Helen Zhang, Toby Chau and Jiahui Li
Appl. Sci. 2025, 15(14), 7758; https://doi.org/10.3390/app15147758 - 10 Jul 2025
Viewed by 516
Abstract
The integration of artificial intelligence (AI) into education requires frameworks that are not only technically robust but also ethically and pedagogically grounded. This paper proposes the Generalist Education Artificial Intelligence (GEAI) framework—a conceptual blueprint designed to enable privacy-preserving, personalized, and multimodal AI-supported learning [...] Read more.
The integration of artificial intelligence (AI) into education requires frameworks that are not only technically robust but also ethically and pedagogically grounded. This paper proposes the Generalist Education Artificial Intelligence (GEAI) framework—a conceptual blueprint designed to enable privacy-preserving, personalized, and multimodal AI-supported learning in educational contexts. GEAI features a Trusted Domain architecture that supports secure, voluntary multimodal data collection via multimedia registration devices (MM Devices), edge-based AI inference, and institutional data sovereignty. Drawing on principles from constructivist pedagogy and regulatory standards such as GDPR and FERPA, GEAI supports adaptive feedback, engagement monitoring, and learner-centered interaction while addressing key challenges in ethical data governance, transparency, and accountability. To bridge theory and application, we outline a staged validation roadmap informed by technical feasibility assessments and stakeholder input. This roadmap lays the foundation for future prototyping and responsible deployment in real-world educational settings, positioning GEAI as a forward-looking contribution to both AI system design and education policy alignment. Full article
(This article belongs to the Special Issue ICT in Education, 2nd Edition)
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20 pages, 4752 KiB  
Article
Designing an AI-Supported Framework for Literary Text Adaptation in Primary Classrooms
by Savvas A. Chatzichristofis, Alexandros Tsopozidis, Avgousta Kyriakidou-Zacharoudiou, Salomi Evripidou and Angelos Amanatiadis
AI 2025, 6(7), 150; https://doi.org/10.3390/ai6070150 - 8 Jul 2025
Viewed by 567
Abstract
Background/Objectives: This paper introduces a pedagogically grounded framework for transforming canonical literary texts in primary education through generative AI. Guided by multiliteracies theory, Vygotskian pedagogy, and epistemic justice, the system aims to enhance interpretive literacy, developmental alignment, and cultural responsiveness among learners aged [...] Read more.
Background/Objectives: This paper introduces a pedagogically grounded framework for transforming canonical literary texts in primary education through generative AI. Guided by multiliteracies theory, Vygotskian pedagogy, and epistemic justice, the system aims to enhance interpretive literacy, developmental alignment, and cultural responsiveness among learners aged 7–12. Methods: The proposed system enables educators to perform age-specific text simplification, visual re-narration, lexical reinvention, and multilingual augmentation through a suite of modular tools. Central to the design is the Ethical–Pedagogical Validation Layer (EPVL), a GPT-powered auditing module that evaluates AI-generated content across four normative dimensions: developmental appropriateness, cultural sensitivity, semantic fidelity, and ethical transparency. Results: The framework was fully implemented and piloted with primary educators (N = 8). The pilot demonstrated high usability, curricular alignment, and perceived value for classroom application. Unlike commercial Large Language Models (LLMs), the system requires no prompt engineering and supports editable, policy-aligned controls for normative localization. Conclusions: By embedding ethical evaluation within the generative loop, the framework fosters calibrated trust in human–AI collaboration and mitigates cultural stereotyping and ideological distortion. It advances a scalable, inclusive model for educator-centered AI integration, offering a new pathway for explainable and developmentally appropriate AI use in literary education. Full article
(This article belongs to the Special Issue AI Bias in the Media and Beyond)
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39 pages, 4950 KiB  
Systematic Review
Large Language Models’ Trustworthiness in the Light of the EU AI Act—A Systematic Mapping Study
by Md Masum Billah, Harry Setiawan Hamjaya, Hakima Shiralizade, Vandita Singh and Rafia Inam
Appl. Sci. 2025, 15(14), 7640; https://doi.org/10.3390/app15147640 - 8 Jul 2025
Viewed by 690
Abstract
The recent advancements and emergence of rapidly evolving AI models, such as large language models (LLMs), have sparked interest among researchers and professionals. These models are ubiquitously being fine-tuned and applied across various fields such as healthcare, customer service and support, education, automated [...] Read more.
The recent advancements and emergence of rapidly evolving AI models, such as large language models (LLMs), have sparked interest among researchers and professionals. These models are ubiquitously being fine-tuned and applied across various fields such as healthcare, customer service and support, education, automated driving, and smart factories. This often leads to an increased level of complexity and challenges concerning the trustworthiness of these models, such as the generation of toxic content and hallucinations with high confidence leading to serious consequences. The European Union Artificial Intelligence Act (AI Act) is a regulation concerning artificial intelligence. The EU AI Act has proposed a comprehensive set of guidelines to ensure the responsible usage and development of general-purpose AI systems (such as LLMs) that may pose potential risks. The need arises for strengthened efforts to ensure that these high-performing LLMs adhere to the seven trustworthiness aspects (data governance, record-keeping, transparency, human-oversight, accuracy, robustness, and cybersecurity) recommended by the AI Act. Our study systematically maps research, focusing on identifying the key trends in developing LLMs across different application domains to address the aspects of AI Act-based trustworthiness. Our study reveals the recent trends that indicate a growing interest in emerging models such as LLaMa and BARD, reflecting a shift in research priorities. GPT and BERT remain the most studied models, and newer alternatives like Mistral and Claude remain underexplored. Trustworthiness aspects like accuracy and transparency dominate the research landscape, while cybersecurity and record-keeping remain significantly underexamined. Our findings highlight the urgent need for a more balanced, interdisciplinary research approach to ensure LLM trustworthiness across diverse applications. Expanding studies into underexplored, high-risk domains and fostering cross-sector collaboration can bridge existing gaps. Furthermore, this study also reveals domains (like telecommunication) which are underrepresented, presenting considerable research gaps and indicating a potential direction for the way forward. Full article
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19 pages, 3176 KiB  
Article
Deploying an Educational Mobile Robot
by Dorina Plókai, Borsa Détár, Tamás Haidegger and Enikő Nagy
Machines 2025, 13(7), 591; https://doi.org/10.3390/machines13070591 - 8 Jul 2025
Viewed by 655
Abstract
This study presents the development of a software solution for processing, analyzing, and visualizing sensor data collected by an educational mobile robot. The focus is on statistical analysis and identifying correlations between diverse datasets. The research utilized the PlatypOUs mobile robot platform, equipped [...] Read more.
This study presents the development of a software solution for processing, analyzing, and visualizing sensor data collected by an educational mobile robot. The focus is on statistical analysis and identifying correlations between diverse datasets. The research utilized the PlatypOUs mobile robot platform, equipped with odometry and inertial measurement units (IMUs), to gather comprehensive motion data. To enhance the reliability and interpretability of the data, advanced data processing techniques—such as moving averages, correlation analysis, and exponential smoothing—were employed. Python-based tools, including Matplotlib and Visual Studio Code, were used for data visualization and analysis. The analysis provided key insights into the robot’s motion dynamics; specifically, its stability during linear movements and variability during turns. By applying moving average filtering and exponential smoothing, noise in the sensor data was significantly reduced, enabling clearer identification of motion patterns. Correlation analysis revealed meaningful relationships between velocity and acceleration during various motion states. These findings underscore the value of advanced data processing techniques in improving the performance and reliability of educational mobile robots. The insights gained in this pilot project contribute to the optimization of navigation algorithms and motion control systems, enhancing the robot’s future potential in STEM education applications. Full article
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