搜索结果：Plagiarism

Since the authors have not responded to the editor’s requests to fulfill the editorial requirements, the article has been withdrawn. The Publisher apologizes to the readers of the journal for any inconvenience this may have caused. The Bentham Editorial Policy on Article Withdrawal can be found at https://benthamscience.com/editorial-policies-main.php It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript, the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

As artificial intelligence (AI) tools become increasingly integrated into optometric practice, higher education providers must adapt to these technological advancements and integrate teaching about AI into the optometry curriculum. Before AI can be taught in the optometry curriculum, educational priorities must be established. Therefore, this research aimed to explore a range of stakeholder views on the integration of AI into optometry education. Semi-structured interviews were conducted with a purposive sample of eyecare practitioners, students, educators, regulators, and AI technology experts. Interviews were conducted online, transcribed verbatim, and thematic analysis was conducted. Three themes emerged: (1) Curriculum design and delivery; (2) Facilitators and barriers to teaching; (3) AI as an educational tool. AI topics for inclusion in the optometry curriculum were explored, with "Interpreting outputs of AI tools," "Foundational understanding," "Ethical Considerations" and "Integrating AI into clinical workflows" most commonly identified as important to teach. Barriers to teaching were identified including lack of access to AI devices, funding and time constraints, and rapid developments in AI necessitating frequent curriculum updates. AI tools were already being used by some students as an aid to their studies. However, many participants felt that higher education providers held a negative view of AI and discouraged its use. Participants identified potential risks associated with AI usage within education including plagiarism and reduced critical thinking. The multidisciplinary participants of this study expressed perspectives on a range of issues relating to the integration of AI into optometry education. This research aimed to aid optometry higher education providers as they explore integrating AI literacy into their curricula. However, further research is required to test the implementation of these recommendations in real-world settings. Additionally, although the potential benefits of AI as an educational tool are vast, the long-term cognitive costs of using these tools are not yet known and require further study.

Instances of misconduct stoke public mistrust in the scientific enterprise. Efforts in the United States to decrease inappropriate professional behavior in science have included enhancing institutional accountability. Yet, the moments often missed are those in which academic institutions land in precarious situations because newly hired staff are discovered to have a checkered history. The offending scientist might have quietly resigned from a previous employer during or immediately after a scientific or professional misconduct investigation. Needless to say, the candidate did not share that information with a potential new employer. Things become even more problematic when the new hire repeats bad behaviors, whether it's research misconduct (fabrication, falsification, or plagiarism) or actions that deviate from professional standards (sexual harassment, bullying, or racist behavior, for example). Creating more awareness of misconduct involving such individuals could go a long way toward rebuilding the scientific community's credibility.

Accurate detection of semantically similar texts underpins applications such as plagiarism detection and content recommendation, yet remains challenging in low-resource languages like Urdu due to limited labeled data and noisy annotations. We introduce Siamese Graph-Based Semi-Supervised Learning (SGSSL), a framework that expands training diversity through back-translation and masked-word prediction, and learns from unlabeled data via confidence-aware pseudo-labeling with consistency regularization. At its core, SGSSL employs a Siamese Graph Neural Network that fuses Graph Convolutional Networks and Graph Attention Networks to capture fine-grained relational signals between sentences. A bottleneck feature-refinement layer and a Transformer Encoder with multi-head self-attention mitigate GNN over-smoothing while modeling long-range dependencies. We further leverage Urdu RoBERTa embeddings for deep contextual semantics. Our hybrid confidence-based filtering integrates probabilistic confidence, semantic similarity, and model uncertainty to select high-quality pseudo-labels. Evaluated on six benchmark datasets for text reuse and paraphrase detection (binary and ternary classification), SGSSL consistently outperforms strong baselines. On the USTRC dataset, SGSSL attains an accuracy of 0.722, an 8.1% absolute gain over the best baseline, demonstrating effectiveness for low-resource settings.

Scientific misconduct has various consequences, which must be addressed during the training of physicians, who will lead many fields of scientific research. To analyze the bibliometric characteristics of scientific output in Scopus regarding scientific misconduct in medical education. A bibliometric study was conducted in which a search strategy was developed to identify the articles indexed in the Scopus database from 2012 to 2021. Bibliometric parameters were estimated using the Scival tool. The number of publications ranged from 2 to 6 was between 2012 and 2021. International collaboration increased to 33.3% in 2021. The Romanian Journal of Morphology and Embryology was the journal with the highest output on the topic; however, PLoS ONE had the greatest impact in terms of quartile (Q1) ranking and number of citations. The author with the highest impact was Nedjat Saharnaz from Iran. Two clusters of recurrence were found, highlighting the terms "medical student" and "plagiarism" in the first, and "scientific misconduct" in the second. Publications on scientific misconduct in medical training are scarce each year, with limited international collaboration. Lower-impact journals are the most frequently chosen for dissemination, despite having fewer citations and lower CiteScores in 2021. Résumé Contexte:L’inconduite scientifique a diverses conséquences qui doivent être prises en compte lors de la formation des médecins, lesquels seront appelés à diriger de nombreux domaines de la recherche scientifique.Objectif:Analyser les caractéristiques bibliométriques de la production scientifique indexée dans Scopus concernant l’inconduite scientifique dans la formation médicale.Méthodes:Une étude bibliométrique a été menée, au cours de laquelle une stratégie de recherche a été élaborée pour identifier les articles indexés dans la base de données Scopus entre 2012 et 2021. Les paramètres bibliométriques ont été estimés à l’aide de l’outil Scival.Résultats:Le nombre de publications a varié de 2 à 6 entre 2012 et 2021. La collaboration internationale a augmenté pour atteindre 33,3% en 2021. Le Romanian Journal of Morphology and Embryology était la revue ayant publié le plus grand nombre d’articles sur le sujet; cependant, PLoS ONE a eu le plus grand impact en termes de classement dans le premier quartile (Q1) et de nombre de citations. L’auteur ayant eu le plus grand impact est Nedjat Saharnaz, d’Iran. Deux groupes de publications récurrentes ont été identifies: le premier met en évidence les termes « étudiant en médecine » et « plagiat », et le second « fraude scientifique ».Conclusion:Les publications sur la fraude scientifique dans la formation médicale sont rares chaque année, et la collaboration internationale reste limitée. Les revues à faible impact sont les plus fréquemment choisies pour la diffusion des résultats, malgré un nombre de citations et un CiteScore inférieurs en 2021.

The integration of artificial intelligence language models into medical literature requires rigorous evaluation of accuracy and reliability, especially in specialized domains. This study assessed ChatGPT-5's capacity to generate clinically accurate scientific content on penile prosthesis implantation. Using structured prompts, ChatGPT-5 produced a narrative review evaluated across four domains: (1) verification of factual statements, (2) reference validity via PubMed and Google Scholar, (3) plagiarism screening with iThenticate and Quetext, and (4) qualitative assessment using Scale for the Assessment of Narrative Review Articles and a peer-review rubric. ChatGPT-5 demonstrated high factual accuracy overall, correctly supporting most statements, although errors were identified in historical timelines and survival data. In contrast, reference analysis revealed significant weaknesses, with only about one-third of citations being fully accurate and several containing fabricated or incomplete bibliographic details. Text similarity rates were low. Overall quality was rated as good according to standardized assessment tools, with strong agreement between reviewers. Collectively, these findings indicate that ChatGPT-5 can produce clinically accurate, well-structured content but demonstrates important weaknesses in reference reliability and evidence synthesis. The results support a hybrid model in which artificial intelligence serves as a drafting aid under expert supervision rather than as a standalone author. Future work should prioritize strengthening citation validity to enhance reliability while safeguarding scientific integrity.

Concerns regarding academic dishonesty are a persistent problem in higher education, including health professions and pharmacy education. Students may respond to academic pressures and new policy or procedure restrictions with innovative ways to gain an unfair advantage through technological advances and old-school methods. Recent shifts to online and hybrid modalities, as well as advances in artificial intelligence, smartphones, watches, and other technologies, continue to escalate the arms race between educational programs and those seeking to circumvent the system. This manuscript seeks to remind faculty and administrators of common cheating modalities that students may use (including high- and low-technology approaches) while calling on members of the Academy to refrain from merely discussing academic integrity issues but rather to actively seek to minimize and address these concerns. This requires increased awareness of various types of cheating and academic integrity matters, understanding of cheating approaches, intentional reflection on academic integrity policies, and implementation of related risk-reduction strategies. It is our responsibility as educators to prevent and address developing complications and be aware of advances in our field. One of the best ways to address advancing concerns of cheating, plagiarism, and academic integrity is by informing ourselves of recent developments relative to academic integrity, as well as peer-reviewed testing strategies to reduce cheating and other types of academic integrity issues.

This study aimed to evaluate the awareness, usage habits, ethical perceptions, and future expectations of orthopedic and traumatology residents of Türkiye regarding artificial intelligence (AI) technologies. A multicenter cross-sectional descriptive survey was conducted among orthopedic and traumatology residents across Türkiye between July and October 2025. Data were collected using a validated 30-item online questionnaire covering demographics, AI use and awareness, theoretical education and academic utilization, ethical perceptions, and future expectations. Descriptive statistical analyses were performed using frequencies and percentages, and the data were analyzed using IBM SPSS Statistics version 30.0 (IBM Corp., Armonk). A total of 534 residents participated (96.6% male; mean age: 29.36 ± 4.93 years; mean residency year: 2.81 ± 1.36). Most participants (71.9%) had not received formal AI training, and half reported rarely or never using AI in daily clinical practice. In contrast, 56.2% reported using AI in academic studies, primarily for literature searches (55.1%), summarization (46.1%), and draft preparations (37.1%). Ethical concerns were prominent: 62.5% believed that AI could generate inaccurate information, and 44.9% emphasized plagiarism risk. Despite these concerns, 67% anticipated increased AI use in orthopedics, particularly in academic research, surgical planning, and data analysis. While AI tools are increasingly utilized in academic work, their clinical adoption among orthopedic residents remains limited. Structured AI-based training modules and ethical frameworks are essential for improving residents' competence, confidence, and responsible use of AI in orthopedic education and practice.

The integration of generative artificial intelligence (AI) into higher education has introduced powerful tools to support student learning. In nursing education, platforms such as ChatGPT are increasingly used to assist with brainstorming, outlining, and improving academic writing. Although these technologies offer meaningful benefits, their misuse is now creating challenges that threaten the scholarly foundations of nursing education: the misuse of AI to fabricate citations, misattribute authorship, and bypass critical scholarly engagement. The author argues that AI-driven citation fabrication represents a distinct academic integrity challenge that differs from traditional plagiarism in critical ways: it is often unintentional, nearly undetectable by standard tools, and reveals fundamental gaps in students' understanding of evidence-based scholarship. Drawing on a real-world case in undergraduate nursing education, this author examines how a student submitted fabricated journal articles and digital object identifiers (DOIs) alongside misattributed references, mistaking an authored scholarly article for an organizational publication. These errors were not minor oversights; they revealed a lack of understanding of evidence-based scholarship and research ethics. The paper explores how these practices undermine the principles of academic integrity and conflict with the professional standards outlined in the American Association of Colleges of Nursing (AACN) Essentials. This issue reflects not just individual misconduct but a broader pedagogical challenge: preparing nursing students to engage critically and ethically with generative AI technologies. The author argues that nursing education must respond proactively by establishing AI literacy frameworks, revising academic integrity policies, and embedding source verification and citation skills into curricula. Without a clear and enforceable ethical framework, generative AI threatens to erode the scholarly standards essential to both academic rigor and professional nursing practice. This paper contributes to international conversations on AI and academic misconduct in health professions education and calls for a coordinated response to protect the credibility of nursing scholarship and the ethical formation of future nurse leaders.

Research misconduct and questionable research practices compromise the quality of scientific research. The Netherlands Code of Conduct for Research Integrity 2018 outlines five core principles and 61 standards for good research practices intended to guide researchers and research integrity committees. For the code to function, its 61 standards must be perceived as clear and relevant by those who perform the research practices. These include researchers including PhD candidates, who contribute about 40% of Dutch research full-time equivalents. This study examines how PhD candidates in the Science and Medicine faculties at Leiden University perceive these 61 standards. A total of 332 PhD candidates (73% participation proportion) evaluated the standards in the Dutch code as part of a mandatory research integrity course at Leiden University and consented to share their responses for research purposes. Each participant rated half of the standards using Likert-type scales for clarity, relevance, frequency, and seriousness of non-adherence. In addition, open-text responses were collected and categorized according to the code's five core principles. Overall, 83% of clarity ratings and 77% of relevance ratings scored 4 or 5, indicating a generally positive perception of the standards. However, the perceived frequency of non-adherence varied across standards (5-34%). There were discrepancies in severity assessments, where standards that should represent minor shortcomings were perceived as serious misconduct and the other way around. Open-text responses primarily emphasized honesty and transparency, while independence was rarely mentioned, and participants also highlighted values not explicitly covered by the current code, such as equality and ethical collaboration. PhD candidates generally regarded the Dutch Code's 61 standards as clear and relevant, but their ratings of how serious non-adherence should be considered often diverged from the code's a priori three-tier severity framework. Several standards labelled as "minor shortcomings", particularly those related to ethics approvals, authorship, and transparent reporting, were frequently classified as research misconduct, while one plagiarism-related standard (practice 34) was more often seen as a questionable practice. These results suggest the need to refine currently ambiguous formulations and reconsider the a priori severity categories.

Artificial intelligence tools are widely used by Chinese medical students, yet systematic evidence on usage patterns, critical literacy gaps, and influencing factors remains limited, particularly from large-scale multi-institution studies. This study investigates whether a hypothesised structural "artificial intelligence adoption paradox"-high perceived benefits coexisting with weak information verification competence and unclear academic integrity norms-exists among Chinese medical students. A cross-sectional survey was conducted among 3,194 medical students, with 90.4% from Shanxi and supplementary samples from three additional institutions in Beijing and Hunan. A purpose-built twenty-five-item questionnaire assessed artificial intelligence usage, critical evaluation, academic writing, and ethical attitudes. Internal consistency was confirmed for multi-item domains (Cronbach's alpha range: 0.71-0.84). Descriptive statistics, Spearman correlations, logistic regression, and K-means cluster analysis were applied. Artificial intelligence adoption was pervasive, with 49.8% of students using artificial intelligence often or always in daily life and 84.5% perceiving efficiency gains. However, in cross-sectional comparison across year groups, confidence in information verification was 47.2% among Year 1 students compared to 35.0% among Year 5 students, and ethical reflection showed a similar pattern in this cross-sectional sample (Year 1: 69.8% vs. Year 4: 48.8%; Year 5: 51.0%). These cross-sectional differences may reflect cohort effects rather than within-person change. Daily artificial intelligence use correlated with perceived efficiency but not with discernment confidence. Males demonstrated higher discernment confidence than females, a difference that warrants further investigation given potential confounding with academic year distribution. Cluster analysis identified three exploratory user profiles: low-intensity adopters, intensive all-domain users, and study-selective users (mean silhouette coefficient = 0.31, indicating moderate cluster separation). A 21.5-percentage-point gap between verification intent and confidence revealed a "knowing-doing gap." Only 37.0% of students considered direct use of artificial intelligence-generated content as plagiarism, indicating substantial normative uncertainty. This multi-institution cross-sectional study of 3,194 undergraduate Chinese medical students provides empirical support for the hypothesised artificial intelligence adoption paradox: high artificial intelligence use coexists with limited discernment confidence and declining ethical reflection across year groups in this cross-sectional sample. However, the cross-sectional design precludes causal inference, and the observed patterns may reflect cohort differences rather than within-person change. The three identified profiles suggest potential avenues for differentiated pedagogical strategies. Medical curricula should integrate critical artificial intelligence literacy, verification skills, and clear integrity norms from the earliest academic year.

With the emergence of generative AI models such as ChatGPT, a new phase of scientific work is also beginning in orthopedics and trauma surgery. As a language-based deep learning model (LLM), ChatGPT offers a wide range of possible applications-especially in the creation, translation, and optimization of scientific texts. It supports authors in finding ideas, linguistic elaboration, and can even be used to check for plagiarism. It is a particularly valuable tool for non-native speakers. However, despite all the opportunities, its use involves considerable risks; studies show a high rate of incorrect or invented references. In addition, journals are sometimes flooded due to mass publication as a result of easier text generation. The scientific discourse, therefore, calls for clear rules on the use of LLM-particularly with regard to transparency, authorship, and the integrity of scientific work. Mit dem Aufkommen generativer KI-Modelle wie ChatGPT beginnt auch in der Orthopädie und Unfallchirurgie eine neue Phase wissenschaftlichen Arbeitens. Als sprachbasiertes Deep-Learning-Modell (LLM) bietet ChatGPT vielfältige Anwendungsmöglichkeiten – insbesondere bei der Erstellung, Übersetzung und Optimierung wissenschaftlicher Texte. Es unterstützt Autoren bei der Ideenfindung, der sprachlichen Ausarbeitung und kann selbst bei der Plagiatsprüfung eingesetzt werden. Besonders für Nichtmuttersprachler stellt es ein wertvolles Hilfsmittel dar. Doch trotz aller Chancen birgt der Einsatz erhebliche Risiken: Studien belegen eine hohe Rate fehlerhafter oder erfundener Quellenangaben. Zudem kommt es teilweise zu einer Überflutung der Journals aufgrund von Massenpublikation durch die leichtere Textgenerierung. Der wissenschaftliche Diskurs fordert daher klare Regeln zur Nutzung von LLM – insbesondere im Hinblick auf Transparenz, Autorenschaft und Integrität der wissenschaftlichen Arbeit.

Social media now allow the evaluation and documentation of people's reputations online (e.g. upvotes and downvotes). The psychological mechanisms (Deservingness, Rivalry) associated with such evaluation were probed by considering reactions to vignettes outlining the success or failure of an honest or plagiarising professor. 109 participants completed the Tall Poppy scale, and provided their reactions to the vignettes on 5 point Likert scales, rating their Satisfaction, Amusement as well as attributions of Responsibility and Fairness, and willingness to support (Upvote or Donate). In a 2 × 2 Outcome (success/fail) by Context (original/plagiarised work) Multivariate Analysis of Variance there was significant disapproval of the successful plagiarist, and approval when the plagiarist failed. The strongest reactions elicited involved dissatisfaction and attributions of unfairness. Upvoting was less likely when plagiarism was reported. Strength of effects indicated that satisfaction and attributions of unfairness could be important components of people's reactions. Upvoting and willingness to donate in support were greater when the Professor was honest and outcomes were unfair. People were more concerned when a vignette suggested a breakdown of the social order.

Generative artificial intelligence (AI) is rapidly reshaping academic, educational, and scientific practices. Within health faculties, its deployment raises significant pedagogical, ethical, and regulatory challenges related to governance, accountability, and data protection. In response, the French Conference of Deans of Medical Faculties (CDD) initiated a French national charter to provide stewardship and oversight for the responsible use of generative AI in academic activities, including academic work, reports, theses, and dissertations in health education. An institutional, consensus-based governance process was conducted in three sequential phases: scoping and documentary analysis, iterative co-drafting with human oversight, and broad consultation followed by formal validation and official adoption. All participants were university professors and hospital practitioners, the majority of whom were deans of faculties of medicine. The charter is structured around six core components: (i) general principles emphasizing complementary use, transparency, traceability, and human accountability; (ii) authorized and regulated uses, including text structuring, synthesis, linguistic editing, translation, and supervised code generation; (iii) prohibited uses, notably data fabrication or manipulation, plagiarism, substitution for critical reasoning, and the entry of personal or sensitive data into non-secure or non-sovereign systems; (iv) clearly defined responsibilities and accountability of students, supervisors, and institutions; (v) oversight mechanisms and proportionate sanctions to ensure academic integrity; and (vi) forward-looking perspectives and capacity-building measures. The final version of the charter was unanimously adopted by the CDD in plenary session on November 26, 2025, and is currently being disseminated nationally through formal adoption by each faculty council. The development of this charter demonstrates the collective capacity of faculties of medicine to exercise stewardship in response to a major technological innovation. Through a collegial and transparent process, it reconciles innovation with pedagogical relevance, scientific integrity, human oversight, and data protection. The charter now constitutes an evolving national reference framework to support responsible, accountable, and ethically grounded integration of generative AI in health sciences education. Contexte : L’intelligence artificielle (IA) générative transforme rapidement les pratiques académiques, pédagogiques et scientifiques. Dans les facultés de santé, son usage comporte des enjeux et soulève des défis pédagogiques, éthiques et réglementaires. Pour répondre à ces défis, la Conférence des doyennes et des doyens des facultés de médecine (CDD) s’est donnée pour objectif d’élaborer une charte nationale française encadrant l’utilisation de l’IA générative pour la conduite des travaux et la rédaction des rapports, thèses d’exercice et mémoires d’étude en santé. Méthodes : Consensus institutionnel conduit en trois phases : cadrage et analyse documentaire, corédaction itérative, consultation élargie et adoption officielle définitive. Les participants étaient tous des enseignants-chercheurs PU-PH, et la plupart doyennes et doyens de faculté de médecine. Résultats : La charte se structure en six volets : principes généraux (usage complémentaire, transparence, traçabilité) ; usages autorisés (structuration, synthèse, relecture linguistique, traduction, code sous supervision) ; usages interdits (fabrication ou altération de données, plagiat, substitution à l’analyse critique, saisie de données sensibles dans des outils non souverains) ; responsabilités des étudiants, encadrants et institutions ; contrôle et sanctions ; perspectives et accompagnement. Le texte final a été adopté par la CDD en séance plénière le 26 novembre 2025 et sa diffusion nationale est en cours, via son adoption par chaque conseil de faculté. Conclusion : L’élaboration de cette charte illustre la volonté et la capacité des facultés de médecine à se doter d’un cadre commun face à une innovation technologique majeure. Ce processus collégial a permis de concilier innovation, pertinence pédagogique, intégrité scientifique et sécurité des données. La charte constitue désormais un référentiel national évolutif.