The results show the significant potential of AI in personalizing learning, automating routine tasks, and providing access to knowledge, but also reveal serious risks of exacerbating social inequality and ethical dilemmas.
GPT-4 significantly outperforms both human test-takers and prior models, demonstrating a 26% increase over ChatGPT and beating humans in five of seven subject areas, document not just the rapid and remarkable advance of large language model performance generally, but also the potential for such models to support the delivery of legal services in society.
This work presents SuperGPQA, a comprehensive benchmark that evaluates graduate-level knowledge and reasoning capabilities across 285 disciplines and management of a large-scale annotation process, involving over 80 expert annotators and an interactive Human-LLM collaborative system, offering valuable methodological guidance for future research initiatives of comparable scope.
While ChatGPT was perceived as effective in potentially improving AI literacy, digital communication, and content creation skills, it was less useful for interpersonal communication, decision-making, numeracy, native language proficiency, and the development of critical thinking skills.
This study investigates the impact of ChatGPT, an AI-driven language that offers instant feedback, personalized learning experiences, and support for various academic tasks.
A framework to evaluate sycophantic behavior in ChatGPT-4o, Claude-Sonnet, and Gemini-1.5-Pro across AMPS (mathematics) and MedQuad (medical advice) datasets is introduced.
The findings indicate that educational chatbots are suitable for integration into courses to improve personalized learning and reduce teacher administrative burden, although improvements in automated fact-checking are needed.
This evidence-informed and consensus-guided framework will play an important role in enabling medical institutions to better prepare future physicians for the ongoing digital transformation in health care.
The ISAR model is introduced, which differentiates four types of AI effects on learning compared to learning conditions without AI, namely inversion, substitution, augmentation, and redefinition.
The study aims to explore the benefits and challenges of AI chatbots in educational settings, with the goal of identifying how they can address existing barriers to learning and understand the risks, benefits, and ethical use of AI chatbots in education.
The need for a standardized, adaptable AI curriculum in UME that prioritizes transversal skills, including digital competence and ethical awareness, to support AI's gradual integration is underscored.
Detailed guidance on formulating questions using the PICO (population, intervention, comparison, outcome) structure, and refining the question considering possible differences in relative and absolute effects across patient groups is presented.
A theoretical investigation of mechanisms by which ultraviolet circular polarisation may be produced in star formation regions, and how dichroic extinction may play a key role in producing an enantiomeric excess.
It is demonstrated that AI is an effective and credible tool in medical education, offering personalized learning experiences and improved educational outcomes.
Technologies such as learning management systems (LMS), massive open online courses (MOOCs), artificial intelligence (AI), collaborative platforms, and learning analytics support SRL by providing personalized feedback and facilitating autonomous learning.
It is demonstrated how a single localised initiative can evolve into a multi-layered strategy, balancing day-to-day enhancement with capacity for systemic innovation.
The study investigates students’ awareness, adoption patterns, and perceptions of generative AI’s role in academic tasks, alongside the benefits they identify and the challenges they face, including ethical concerns, reliability, and accessibility.
Combining AI efficiency with expert oversight could optimise question creation for high-stakes exams, offering a scalable model for medical education that balances time efficiency and content quality.
Female students and students from the humanities and medicine consistently expressed more negative attitudes and concerns about AI ’ s role in learning and assessment, while males and technology and engineering students showed higher usage and optimism.
A review of the literature regarding AI applications in medical education highlighted AI's varied roles, from augmenting traditional educational methods to introducing innovative practices, and underscores the urgent need for ethical guidelines in AI's application in medical education.
A refined convolutional neural network model designed to detect students’ emotions with high accuracy, using the FER2013 facial expression recognition dataset, is developed and demonstrated that the model is effective at recognizing subtle differences in facial expressions, making it suitable for real-time application in educational settings.
The study shows that the efficient application of AI-enabled PL requires a comprehensive strategy addressing technological, pedagogical, and ethical issues all at once, and underline the need for thorough professional development activities for teachers and AI tools for especially targeted instruction.
The necessity for longitudinal studies to explore the long-term effects of AI on educational outcomes and mental health is suggested and the importance of incorporating student perspectives for a thorough understanding of AI’s role in education is underscored.
The research findings indicate that students worried more about hindering the development of their own writing skills than the risk of being caught and facing academic penalties, and students believed that ChatGPT-written works are easily detectable, and institutions should incorporate plagiarism detectors.
It is essential for universities to implement concrete measures, including ethical oversight mechanisms, audit protocols, and digital literacy training programs, to maximize the benefits and mitigate the risks of AI in education.
Overall, gamification is most likely to be effective when instructional design principles are used to ensure training content meets learners’ needs and expectations.
This study addresses challenges in performance analysis, quality education delivery, and student evaluation through machine learning (ML) models, and offers actionable insights for educators, administrators, and policymakers to better understand student performance drivers and support data-informed educational strategies.
This study conducts a systematic literature review (SLR) to investigate the applications and trends of AI in mathematics education by examining articles published in reputable journals indexed in Web of Science and Scopus.
AI offers promising advancements in nursing informatics, leading to more efficient patient care and improved decision‐making, Nonetheless, overcoming ethical challenges and ensuring AI literacy among nurses are critical steps for successful implementation.
This AI literacy guide is designed to empower nurses to navigate and help build the future of health care and AI with confidence and competence.
The findings reveal significant positive relationships between gamification and student achievement, gamification and student engagement, gamification and artificial intelligence, and AI with both student engagement and achievement.
ChatGPT's potential to enhance MSRL is highlighted and holds implications for teacher education and AI integration in educational settings and ChatGPT's acceptance among academics is explored.
This study validated “TAME-ChatGPT” as a useful tool for assessing ChatGPT adoption among university students and indicated the adequacy of the “TAME-ChatGPT” constructs.
A systematic literature review unveiled a diverse array of methods and technologies employed in learning communities to facilitate effective knowledge sharing, underscoring the significance of methods and technologies in supporting knowledge sharing within learning communities.
The quasi-experimental approach emerged as the most commonly used design technique for implementing GenAI ideas in the classroom, and psychological variables were the primary measures used to gauge learning outcomes.
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