This paper explores the paradigm reconstruction of interpreting pedagogy driven by generative AI technology.With the breakthroughs of AI technologies such as ChatGPT in natural language processing,traditional interpre...This paper explores the paradigm reconstruction of interpreting pedagogy driven by generative AI technology.With the breakthroughs of AI technologies such as ChatGPT in natural language processing,traditional interpreting education faces dual challenges of technological substitution and pedagogical transformation.Based on Kuhn’s paradigm theory,the study analyzes the limitations of three traditional interpreting teaching paradigms,language-centric,knowledge-based,and skill-acquisition-oriented,and proposes a novel“teacher-AI-learner”triadic collaborative paradigm.Through reconstructing teaching subjects,environments,and curriculum systems,the integration of real-time translation tools and intelligent terminology databases facilitates the transition from static skill training to dynamic human-machine collaboration.The research simultaneously highlights challenges in technological ethics and curriculum design transformation pressures,emphasizing the necessity to balance technological empowerment with humanistic education.展开更多
Currently,generative AI technologies and services worldwide are experiencing explosive growth.While driving technological innovation and productivity advancement in the social economy,they also precipitate multiple le...Currently,generative AI technologies and services worldwide are experiencing explosive growth.While driving technological innovation and productivity advancement in the social economy,they also precipitate multiple le-gal risks,ethical breaches in technology,and social governance challenges.Distinct regulatory pathways have emerged internationally:the EU promotes a rigid governance system through a unified regulatory framework and centralized oversight mechanisms,though concurrently exhibiting a trend of deferred legal application;the United States adopts an advocacy-based regulatory strategy combining principled guidance with corporate self-compliance;the United King-dom implements a non-mandatory principled framework establishing a compromise-based governance model.Grounded in China's strategic imperative to engage in global AI competition and informed by international experi-ences,the legal governance framework for generative AI must incorporate practical legislative imperatives,anchored in the dynamic adaptation between technological iteration and legal regulation,alongside the recalibration of developmen-tal efficacy against security risks.This necessitates establishing tiered safety thresholds and controllability require-ments within the governance architecture.Accordingly,there is an urgent need to enhance institutional provision and policy coordination,construct a multi-stakeholder long-term mechanism integrating administrative supervision,indus-try self-regulation,and technical governance,and formulate scenario-specific liability rules covering the entire life cycle from R&D to deployment-thereby avoiding arbitrary legislative uniformity.The ultimate objective is to forge a comprehensive governance ecosystem characterized by trustworthiness and security as its foundation,and prudence,in-clusiveness,and dynamic adaptability as its defining features.展开更多
The advancement of generative AI has reshaped EFL education,particularly in EFL writing.This qualitative case study investigates the perceptions of Chinese college students and EFL teachers towards the integration of ...The advancement of generative AI has reshaped EFL education,particularly in EFL writing.This qualitative case study investigates the perceptions of Chinese college students and EFL teachers towards the integration of Gen AI in EFL writing.The research involved semi-structured interviews with 13 students and 10 EFL teachers.Thematic analysis,guided by the Technology Acceptance Model(TAM),was employed to analyze the qualitative data.The findings reveal the perceptions of students and teachers regarding the role of generative AI in EFL writing.Regarding usefulness,students appreciate Gen AI for reducing writing difficulty and enhancing efficiency,though some note that it may produce logical flaws and misinformation.Teachers share similar perceptions,but stress effectiveness depends on students’language level.Some teachers also advocate traditional writing initially to build foundational skills.On the ease of use,most students find it easy interacting with Gen AI but mention dialogical understanding challenges.Both students and teachers stress clear prompts are crucial,indicating“AI interaction literacy”should be part of teaching.Moreover,teachers worry that Gen AI’s ease of use may lead to over-reliance.These results reveal contradicting goals of using Gen AI:students value efficiency,while teachers focus on ability cultivation.These insights guide more effective integration of Gen AI in EFL writing education.展开更多
Recently,generative artificial intelligence(GenAI)has developed into a new form of technology that can create copy,image,audio,and video content and adapt it to individual preferences on every channel and moment autom...Recently,generative artificial intelligence(GenAI)has developed into a new form of technology that can create copy,image,audio,and video content and adapt it to individual preferences on every channel and moment automatically.But most fail at proof-of-concept,as the pipelines needed to govern data,generate it controllably,deliver it,and do causal evaluation are absent or poorly aligned.This paper puts forward a practical end-to-end framework concerning personalized advertising driven by GenAI,which combines representation learning,constrained generation,and experimentation into a single operating cycle.First,we pick a modular architecture:profiles and contexts go into controllable large language and diffusion models that yield brand-safe assets under deterministic conditioning,which are chosen via a contextual bandit and vetted by policy and equality guardrails.Second,we give a measurement stack going from straightforward A/B/n tests to doubly-robust uplift modeling,making it possible to find out diverse treatment effects that are good to use in business metrics(incremental conversions and profit).Third,we operationalize latency budgets,humans in the loop,red teams,safety filters,and post-deployment monitoring with clear escalation paths.We focus throughout the paper on reproducibility,privacy(consent,privacy,differential privacy,on-device inference),and on GDPR/CCPA-like governance specifications.We end on our actionable blueprint,algorithmic choices,sample prompts,KPIs,and step-wise rollout to achieve trustworthy performance upgrades without putting creative quality,fairness,or compliance to the test.展开更多
With the acceleration of global aging,the population aged 60 and above in China has exceeded 280 million,and the contradiction between the digital skills demands of the elderly and the supply of static and universal e...With the acceleration of global aging,the population aged 60 and above in China has exceeded 280 million,and the contradiction between the digital skills demands of the elderly and the supply of static and universal educational resources has become prominent.This article conducts an in-depth study on the“on-demand creation”model of elderly education resource services driven by generative AI.This study proposes an“on-demand creation”service paradigm based on generative AI,providing suitable resources for elderly intelligent life skills training through demand perception,content generation,and dynamic optimization mechanisms.From the perspective of technological philosophy and service science,deconstruct the core element logic of the paradigm to demonstrate its dual value in reconstructing the theoretical framework of elderly education and promoting practical transformation.This research indicates that this paradigm provides systematic theoretical support for the innovation of elderly education services through a balance between technological empowerment and humanistic care,helping the elderly master modern information technology and life skills,enhancing their self-care ability and social participation,and better adapting to life in the digital age.展开更多
With the rapid development of generative artificial intelligence(AI)technology in the field of education,global educational systems are facing unprecedented opportunities and challenges,urgently requiring the establis...With the rapid development of generative artificial intelligence(AI)technology in the field of education,global educational systems are facing unprecedented opportunities and challenges,urgently requiring the establishment of comprehensive,flexible,and forward-looking governance solutions.The“Australian Framework for Generative AI in Schools”builds a multi-dimensional governance system covering aspects such as teaching and humanistic care,fairness and transparency,and accountability and security.Based on 22 specific principles and six core elements,it emphasizes a human-centered design concept,adopts a principle-based flexible structure,focuses on fairness and transparency,and stresses accountability and security.The framework provides valuable references for the use of generative AI in China’s education system and holds significant importance for promoting educational modernization and cultivating innovative talents adapted to the era of artificial intelligence.展开更多
Currently,the transformation and upgrading of digitalization have become a new task that enterprises urgently need to address.To further enhance the leadership of enterprise leaders,relevant enterprise staff should fa...Currently,the transformation and upgrading of digitalization have become a new task that enterprises urgently need to address.To further enhance the leadership of enterprise leaders,relevant enterprise staff should face up to the infinite possibilities that generative AI brings to enterprise management.Based on this,this paper will briefly analyze the value connotation of generative AI empowering the improvement of enterprise leadership and the relevant influencing factors,and discuss the strategies for enhancing enterprise leadership in the generative AI era,in order to promote the smooth progress of enterprises’digital transformation and upgrading.展开更多
This study explores a novel educational model of generative AI-empowered interdisciplinary project-based learning(PBL).By analyzing the current applications of generative AI technology in information technology curric...This study explores a novel educational model of generative AI-empowered interdisciplinary project-based learning(PBL).By analyzing the current applications of generative AI technology in information technology curricula,it elucidates its advantages and operational mechanisms in interdisciplinary PBL.Combining case studies and empirical research,the investigation proposes implementation pathways and strategies for the generative AI-enhanced interdisciplinary PBL model,detailing specific applications across three phases:project preparation,implementation,and evaluation.The research demonstrates that generative AI-enabled interdisciplinary project-based learning can effectively enhance students’learning motivation,interdisciplinary thinking capabilities,and innovative competencies,providing new conceptual frameworks and practical approaches for educational model innovation.展开更多
In the era of generative AI(GAI),translation is undergoing unprecedented transformations.GAI enhances translation quality and efficiency,inaugurating a new chapter in translation.However,due to its limitations,the com...In the era of generative AI(GAI),translation is undergoing unprecedented transformations.GAI enhances translation quality and efficiency,inaugurating a new chapter in translation.However,due to its limitations,the complexity of translation,and the uniqueness of Zhuang medicine,cultivating the critical thinking ability among talents in English translation of Zhuang medicine has become critically important.This study reforms the teaching content,instructional design,and evaluation of the course Computer-Aided Translation Technology,and constructs a teaching model to enhance the critical thinking abilities among talents in English translation of Zhuang medicine.The result shows that this model establishes a critically thinking-oriented teaching system,promotes interaction between humans and machines,teachers and students,and among peers,and improves students’capabilities in translation,critical thinking,and social communication.展开更多
The rapid advancement of 6G communication technologies and generative artificial intelligence(AI)is catalyzing a new wave of innovation at the intersection of networking and intelligent computing.On the one hand,6G en...The rapid advancement of 6G communication technologies and generative artificial intelligence(AI)is catalyzing a new wave of innovation at the intersection of networking and intelligent computing.On the one hand,6G envisions a hyper-connected environment that supports ubiquitous intelligence through ultra-low latency,high throughput,massive device connectivity,and integrated sensing and communication.On the other hand,generative AI,powered by large foundation models,has emerged as a powerful paradigm capable of creating.展开更多
Prompt engineering, the art of crafting effective prompts for artificial intelligence models, has emerged as a pivotal factor in determining the quality and usefulness of AI (Artificial Intelligence)-generated outputs...Prompt engineering, the art of crafting effective prompts for artificial intelligence models, has emerged as a pivotal factor in determining the quality and usefulness of AI (Artificial Intelligence)-generated outputs. This practice involves strategically designing and structuring prompts to guide AI models toward desired outcomes, ensuring that they generate relevant, informative, and accurate responses. The significance of prompt engineering cannot be overstated. Well-crafted prompts can significantly enhance the capabilities of AI models, enabling them to perform tasks that were once thought to be exclusively human domain. By providing clear and concise instructions, prompts can guide AI models to generate creative text, translate languages, write different kinds of creative content, and answer your questions in an informative way. Moreover, prompt engineering can help mitigate biases and ensure that AI models produce outputs that are fair, equitable, and inclusive. However, prompt engineering is not without its challenges. Crafting effective prompts requires a deep understanding of both the AI model’s capabilities and the specific task at hand. Additionally, the quality of the prompts can be influenced by factors such as the model’s training data [1] and the complexity of the task. As AI models continue to evolve, prompt engineering will likely become even more critical in unlocking their full potential.展开更多
The risk of bias is widely noticed in the entire process of generative artificial intelligence(generative AI)systems.To protect the rights of the public and improve the effectiveness of AI regulations,feasible measure...The risk of bias is widely noticed in the entire process of generative artificial intelligence(generative AI)systems.To protect the rights of the public and improve the effectiveness of AI regulations,feasible measures to address the bias problem in the context of large data should be proposed as soon as possible.Since bias originates in every part and various aspects of AI product lifecycles,laws and technical measures should consider each of these layers and take different causes of bias into account,from data training,modeling,and application design.The Interim Measures for the Administration of Generative AI Service(the Interim Measures),formulated by the Office of the Central Cyberspace Affairs Commission(CAC)and other departments have taken the initiatives to govern AI.However,it lacks specific details on issues such as how to prevent the risk of bias and reduce the effect of bias in decision-making.The Interim Measures also fail to take causes of bias into account,and several principles must be further interpreted.Meanwhile,regulations on generative AI at the global level are still in their early stages.By forming a governance framework,this paper could provide the community with useful experiences and play a leading role.The framework includes at least three parts:first,determining the realm of governance and unifying related concepts;second,developing measures for different layers to identify the causes and specific aspects of bias;third,identifying parties with the skills to take responsibility for detecting bias intrusions and proposing a program for the allocation of liabilities among the large-scale platform developers.展开更多
With the rapid advancement of AI technology,especially the emergence of generative AI such as ChatGPT and ERNIE Bot,the field of education is undergoing profound changes.While they change the way information is obtain...With the rapid advancement of AI technology,especially the emergence of generative AI such as ChatGPT and ERNIE Bot,the field of education is undergoing profound changes.While they change the way information is obtained and processed,these AI technologies challenge traditional teaching models.Based on evaluating the feasibility of various generative AI tools for teaching and comparing their respective advantages and disadvantages,this paper delves into the application scenarios of these generative AI tools in English reading,writing,and translation,and explores their specific applications in the pre-class,in-class,and post-class parts of“College English Reading,Writing,and Translation”.It is hoped that through innovative teaching methods,both students’learning effectiveness and teachers’teaching efficiency can be improved.At the same time,it is crucial to guide students in recognizing the misinformation and biases that exist in generative AI,while emphasizing the significance of originality and intellectual property.Moreover,their critical thinking skills and proper academic concepts could be cultivated and help them prevent academic misconduct.展开更多
Generative AI is rapidly employed by software developers to generate code or other software artifacts.However,the analysis and assessment of generative AI with respect to requirements analysis and modeling tasks,espec...Generative AI is rapidly employed by software developers to generate code or other software artifacts.However,the analysis and assessment of generative AI with respect to requirements analysis and modeling tasks,especially with UML,has received little attention.This paper investigates the capabilities of generative AI to aid in the creation of three types of UML models:UML use case models,class diagrams,and sequence diagrams.For this purpose,we designed an AI-aided UML modeling task in our course on software requirements modeling.50 undergraduates who majored in Software Engineering at Wuhan University completed the modeling task and the corresponding online survey.Our findings show that generative AI can help create these three types of UML models,but its performance is limited to identifying essential modeling elements of these UML models.展开更多
The realization of an interoperable and scalable virtual platform, currently known as the “metaverse,” is inevitable, but many technological challenges need to be overcome first. With the metaverse still in a nascen...The realization of an interoperable and scalable virtual platform, currently known as the “metaverse,” is inevitable, but many technological challenges need to be overcome first. With the metaverse still in a nascent phase, research currently indicates that building a new 3D social environment capable of interoperable avatars and digital transactions will represent most of the initial investment in time and capital. The return on investment, however, is worth the financial risk for firms like Meta, Google, and Apple. While the current virtual space of the metaverse is worth $6.30 billion, that is expected to grow to $84.09 billion by the end of 2028. But the creation of an entire alternate virtual universe of 3D avatars, objects, and otherworldly cityscapes calls for a new development pipeline and workflow. Existing 3D modeling and digital twin processes, already well-established in industry and gaming, will be ported to support the need to architect and furnish this new digital world. The current development pipeline, however, is cumbersome, expensive and limited in output capacity. This paper proposes a new and innovative immersive development pipeline leveraging the recent advances in artificial intelligence (AI) for 3D model creation and optimization. The previous reliance on 3D modeling software to create assets and then import into a game engine can be replaced with nearly instantaneous content creation with AI. While AI art generators like DALL-E 2 and DeepAI have been used for 2D asset creation, when combined with game engine technology, such as Unreal Engine 5 and virtualized geometry systems like Nanite, a new process for creating nearly unlimited content for immersive reality is possible. New processes and workflows, such as those proposed here, will revolutionize content creation and pave the way for Web 3.0, the metaverse and a truly 3D social environment.展开更多
Recent advances in generative artiflcial intelligence(AI)technologies have been signiflcantly driven by models such as generative adversarial networks(GANs),variational autoencoders(VAEs),and denoising diffusion proba...Recent advances in generative artiflcial intelligence(AI)technologies have been signiflcantly driven by models such as generative adversarial networks(GANs),variational autoencoders(VAEs),and denoising diffusion probabilistic models(DDPMs).Although architects recognize the potential of generative AI in design,personal barriers often restrict their access to the latest technological developments,thereby causing the application of generative AI in architectural design to lag behind.Therefore,it is essential to comprehend the principles and advancements of generative AI models and analyze their relevance in architecture applications.This paper flrst provides an overview of generative AI technologies,with a focus on probabilistic diffusion models(DDPMs),3D generative models,and foundation models,highlighting their recent developments and main application scenarios.Then,the paper explains how the abovementioned models could be utilized in architecture.We subdivide the architectural design process into six steps and review related research projects in each step from 2020 to the present.Lastly,this paper discusses potential future directions for applying generative AI in the architectural design steps.This research can help architects quickly understand the development and latest progress of generative AI and contribute to the further development of intelligent architecture.展开更多
Generative artificial intelligence(GenAI)models,such as_ChatGPT,have rapidly gained popularity.Despite this widespread usage,there is still a imited understanding of how this emerging technology impacts different stak...Generative artificial intelligence(GenAI)models,such as_ChatGPT,have rapidly gained popularity.Despite this widespread usage,there is still a imited understanding of how this emerging technology impacts different stakeholders in higher education.While extensive research exists on the general opportunities and risks in education,there is often a lack of specificity regarding the target audiencenamely,students,educators,and institutionsand concrete solution strategies and recommendations are typically absent.Our goal is to address the perspectives of students and educators separately and offer tailored solutions for each of these two stakeholder groups.This study employs a mixed-method approach that integrates a detailed online questionnaire of 188 students with a scenario_analysis to examine potential benefits and drawbacks introduced by GenAI.The findings indicate that students utilize the technology for tasks such as assignment writing and exam preparation,seeing it as an effective tool for achieving academic goals.Subsequent the scenario analysis provided insights into possible future scenarios,highlighting both opportunities and challenges of integrating GenAI within higher education for students as well as educators.The primary aim is to offer a clear and precise understanding of the potential implications for students and educators separately while providing recommendations and solution strategies.The results suggest that irresponsible and excessive use of the technology could pose significant challenges.Therefore,educators need to establish clear policies,reevaluate learning objectives,enhance AI skills,update curricula,and reconsider examination methods.展开更多
This study systematically reviews the applications of generative artificial intelligence(GAI)in breast cancer research,focusing on its role in diagnosis and therapeutic development.While GAI has gained significant att...This study systematically reviews the applications of generative artificial intelligence(GAI)in breast cancer research,focusing on its role in diagnosis and therapeutic development.While GAI has gained significant attention across various domains,its utility in breast cancer research has yet to be comprehensively reviewed.This study aims to fill that gap by synthesizing existing research into a unified document.A comprehensive search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines,resulting in the retrieval of 3827 articles,of which 31 were deemed eligible for analysis.The included studies were categorized based on key criteria,such as application types,geographical distribution,contributing organizations,leading journals,publishers,and temporal trends.Keyword co-occurrence mapping and subject profiling further highlighted the major research themes in this field.The findings reveal that GAI models have been applied to improve breast cancer diagnosis,treatment planning,and outcome predictions.Geographical and network analyses showed that most contributions come from a few leading institutions,with limited global collaboration.The review also identifies key challenges in implementing GAI in clinical practice,such as data availability,ethical concerns,and model validation.Despite these challenges,the study highlights GAI’s potential to enhance breast cancer research,particularly in generating synthetic data,improving diagnostic accuracy,and personalizing treatment approaches.This review serves as a valuable resource for researchers and stakeholders,providing insights into current research trends,major contributors,and collaborative networks in GAI-based breast cancer studies.By offering a holistic overview,it aims to support future research directions and encourage broader adoption of GAI technologies in healthcare.Additionally,the study emphasizes the importance of overcoming implementation barriers to fully realizeGAI’s potential in transforming breast cancer management.展开更多
基金2025 General Project of Humanities and Social Sciences Research in Henan Higher Education Institutions,“Research on the Dynamic Mechanisms and Paths of Innovative Development of Undergraduate Translation Programs Empowered by New Productive Forces”(Project No.:2025-ZDJH-885)2024 College-Level Undergraduate Teaching Reform Project of the School of Foreign Languages,Henan University of Technology,“Research on Implementation Paths of New Models for Interpreter Training Based on AI Large Models”(Project No.:2024YJWYJG06)+1 种基金2025 First-Class Undergraduate Program Construction Special Project of the School of Foreign Languages,Henan University of Technology,titled“Research on Development Paths for Innovative Development of Undergraduate Translation Programs Empowered by New Productive Forces”(Project No.:2025WYZYJS30)2025 Educational Reform Project of the School of International Education,Henan University of Technology,“A Study on the Language Competence Development Model for International Talents Based on the Al Large Model-Taking IELTS Reading and Writing Teaching Practice as an Example”(Project No.:GJXY202533)。
文摘This paper explores the paradigm reconstruction of interpreting pedagogy driven by generative AI technology.With the breakthroughs of AI technologies such as ChatGPT in natural language processing,traditional interpreting education faces dual challenges of technological substitution and pedagogical transformation.Based on Kuhn’s paradigm theory,the study analyzes the limitations of three traditional interpreting teaching paradigms,language-centric,knowledge-based,and skill-acquisition-oriented,and proposes a novel“teacher-AI-learner”triadic collaborative paradigm.Through reconstructing teaching subjects,environments,and curriculum systems,the integration of real-time translation tools and intelligent terminology databases facilitates the transition from static skill training to dynamic human-machine collaboration.The research simultaneously highlights challenges in technological ethics and curriculum design transformation pressures,emphasizing the necessity to balance technological empowerment with humanistic education.
基金This Research Was Funded by the Key Project of Humanities and Social Science Study from the Ministry of Education"Research on the Consideration and Promotion of Human Rights Benchmarks in Global Data Competition"(19JJD820009)。
文摘Currently,generative AI technologies and services worldwide are experiencing explosive growth.While driving technological innovation and productivity advancement in the social economy,they also precipitate multiple le-gal risks,ethical breaches in technology,and social governance challenges.Distinct regulatory pathways have emerged internationally:the EU promotes a rigid governance system through a unified regulatory framework and centralized oversight mechanisms,though concurrently exhibiting a trend of deferred legal application;the United States adopts an advocacy-based regulatory strategy combining principled guidance with corporate self-compliance;the United King-dom implements a non-mandatory principled framework establishing a compromise-based governance model.Grounded in China's strategic imperative to engage in global AI competition and informed by international experi-ences,the legal governance framework for generative AI must incorporate practical legislative imperatives,anchored in the dynamic adaptation between technological iteration and legal regulation,alongside the recalibration of developmen-tal efficacy against security risks.This necessitates establishing tiered safety thresholds and controllability require-ments within the governance architecture.Accordingly,there is an urgent need to enhance institutional provision and policy coordination,construct a multi-stakeholder long-term mechanism integrating administrative supervision,indus-try self-regulation,and technical governance,and formulate scenario-specific liability rules covering the entire life cycle from R&D to deployment-thereby avoiding arbitrary legislative uniformity.The ultimate objective is to forge a comprehensive governance ecosystem characterized by trustworthiness and security as its foundation,and prudence,in-clusiveness,and dynamic adaptability as its defining features.
文摘The advancement of generative AI has reshaped EFL education,particularly in EFL writing.This qualitative case study investigates the perceptions of Chinese college students and EFL teachers towards the integration of Gen AI in EFL writing.The research involved semi-structured interviews with 13 students and 10 EFL teachers.Thematic analysis,guided by the Technology Acceptance Model(TAM),was employed to analyze the qualitative data.The findings reveal the perceptions of students and teachers regarding the role of generative AI in EFL writing.Regarding usefulness,students appreciate Gen AI for reducing writing difficulty and enhancing efficiency,though some note that it may produce logical flaws and misinformation.Teachers share similar perceptions,but stress effectiveness depends on students’language level.Some teachers also advocate traditional writing initially to build foundational skills.On the ease of use,most students find it easy interacting with Gen AI but mention dialogical understanding challenges.Both students and teachers stress clear prompts are crucial,indicating“AI interaction literacy”should be part of teaching.Moreover,teachers worry that Gen AI’s ease of use may lead to over-reliance.These results reveal contradicting goals of using Gen AI:students value efficiency,while teachers focus on ability cultivation.These insights guide more effective integration of Gen AI in EFL writing education.
文摘Recently,generative artificial intelligence(GenAI)has developed into a new form of technology that can create copy,image,audio,and video content and adapt it to individual preferences on every channel and moment automatically.But most fail at proof-of-concept,as the pipelines needed to govern data,generate it controllably,deliver it,and do causal evaluation are absent or poorly aligned.This paper puts forward a practical end-to-end framework concerning personalized advertising driven by GenAI,which combines representation learning,constrained generation,and experimentation into a single operating cycle.First,we pick a modular architecture:profiles and contexts go into controllable large language and diffusion models that yield brand-safe assets under deterministic conditioning,which are chosen via a contextual bandit and vetted by policy and equality guardrails.Second,we give a measurement stack going from straightforward A/B/n tests to doubly-robust uplift modeling,making it possible to find out diverse treatment effects that are good to use in business metrics(incremental conversions and profit).Third,we operationalize latency budgets,humans in the loop,red teams,safety filters,and post-deployment monitoring with clear escalation paths.We focus throughout the paper on reproducibility,privacy(consent,privacy,differential privacy,on-device inference),and on GDPR/CCPA-like governance specifications.We end on our actionable blueprint,algorithmic choices,sample prompts,KPIs,and step-wise rollout to achieve trustworthy performance upgrades without putting creative quality,fairness,or compliance to the test.
文摘With the acceleration of global aging,the population aged 60 and above in China has exceeded 280 million,and the contradiction between the digital skills demands of the elderly and the supply of static and universal educational resources has become prominent.This article conducts an in-depth study on the“on-demand creation”model of elderly education resource services driven by generative AI.This study proposes an“on-demand creation”service paradigm based on generative AI,providing suitable resources for elderly intelligent life skills training through demand perception,content generation,and dynamic optimization mechanisms.From the perspective of technological philosophy and service science,deconstruct the core element logic of the paradigm to demonstrate its dual value in reconstructing the theoretical framework of elderly education and promoting practical transformation.This research indicates that this paradigm provides systematic theoretical support for the innovation of elderly education services through a balance between technological empowerment and humanistic care,helping the elderly master modern information technology and life skills,enhancing their self-care ability and social participation,and better adapting to life in the digital age.
基金2024 Undergraduate Innovation Training Program Project“Research on the Current Situation,Impact and Management Countermeasures of Generative AI in College Students’Learning”(202410065153)。
文摘With the rapid development of generative artificial intelligence(AI)technology in the field of education,global educational systems are facing unprecedented opportunities and challenges,urgently requiring the establishment of comprehensive,flexible,and forward-looking governance solutions.The“Australian Framework for Generative AI in Schools”builds a multi-dimensional governance system covering aspects such as teaching and humanistic care,fairness and transparency,and accountability and security.Based on 22 specific principles and six core elements,it emphasizes a human-centered design concept,adopts a principle-based flexible structure,focuses on fairness and transparency,and stresses accountability and security.The framework provides valuable references for the use of generative AI in China’s education system and holds significant importance for promoting educational modernization and cultivating innovative talents adapted to the era of artificial intelligence.
文摘Currently,the transformation and upgrading of digitalization have become a new task that enterprises urgently need to address.To further enhance the leadership of enterprise leaders,relevant enterprise staff should face up to the infinite possibilities that generative AI brings to enterprise management.Based on this,this paper will briefly analyze the value connotation of generative AI empowering the improvement of enterprise leadership and the relevant influencing factors,and discuss the strategies for enhancing enterprise leadership in the generative AI era,in order to promote the smooth progress of enterprises’digital transformation and upgrading.
文摘This study explores a novel educational model of generative AI-empowered interdisciplinary project-based learning(PBL).By analyzing the current applications of generative AI technology in information technology curricula,it elucidates its advantages and operational mechanisms in interdisciplinary PBL.Combining case studies and empirical research,the investigation proposes implementation pathways and strategies for the generative AI-enhanced interdisciplinary PBL model,detailing specific applications across three phases:project preparation,implementation,and evaluation.The research demonstrates that generative AI-enabled interdisciplinary project-based learning can effectively enhance students’learning motivation,interdisciplinary thinking capabilities,and innovative competencies,providing new conceptual frameworks and practical approaches for educational model innovation.
基金Key Project of Guangxi Higher Education Teaching Reform and Research(2024B028)Innovation Project of Guangxi Graduate Education(JGY202513)。
文摘In the era of generative AI(GAI),translation is undergoing unprecedented transformations.GAI enhances translation quality and efficiency,inaugurating a new chapter in translation.However,due to its limitations,the complexity of translation,and the uniqueness of Zhuang medicine,cultivating the critical thinking ability among talents in English translation of Zhuang medicine has become critically important.This study reforms the teaching content,instructional design,and evaluation of the course Computer-Aided Translation Technology,and constructs a teaching model to enhance the critical thinking abilities among talents in English translation of Zhuang medicine.The result shows that this model establishes a critically thinking-oriented teaching system,promotes interaction between humans and machines,teachers and students,and among peers,and improves students’capabilities in translation,critical thinking,and social communication.
文摘The rapid advancement of 6G communication technologies and generative artificial intelligence(AI)is catalyzing a new wave of innovation at the intersection of networking and intelligent computing.On the one hand,6G envisions a hyper-connected environment that supports ubiquitous intelligence through ultra-low latency,high throughput,massive device connectivity,and integrated sensing and communication.On the other hand,generative AI,powered by large foundation models,has emerged as a powerful paradigm capable of creating.
文摘Prompt engineering, the art of crafting effective prompts for artificial intelligence models, has emerged as a pivotal factor in determining the quality and usefulness of AI (Artificial Intelligence)-generated outputs. This practice involves strategically designing and structuring prompts to guide AI models toward desired outcomes, ensuring that they generate relevant, informative, and accurate responses. The significance of prompt engineering cannot be overstated. Well-crafted prompts can significantly enhance the capabilities of AI models, enabling them to perform tasks that were once thought to be exclusively human domain. By providing clear and concise instructions, prompts can guide AI models to generate creative text, translate languages, write different kinds of creative content, and answer your questions in an informative way. Moreover, prompt engineering can help mitigate biases and ensure that AI models produce outputs that are fair, equitable, and inclusive. However, prompt engineering is not without its challenges. Crafting effective prompts requires a deep understanding of both the AI model’s capabilities and the specific task at hand. Additionally, the quality of the prompts can be influenced by factors such as the model’s training data [1] and the complexity of the task. As AI models continue to evolve, prompt engineering will likely become even more critical in unlocking their full potential.
文摘The risk of bias is widely noticed in the entire process of generative artificial intelligence(generative AI)systems.To protect the rights of the public and improve the effectiveness of AI regulations,feasible measures to address the bias problem in the context of large data should be proposed as soon as possible.Since bias originates in every part and various aspects of AI product lifecycles,laws and technical measures should consider each of these layers and take different causes of bias into account,from data training,modeling,and application design.The Interim Measures for the Administration of Generative AI Service(the Interim Measures),formulated by the Office of the Central Cyberspace Affairs Commission(CAC)and other departments have taken the initiatives to govern AI.However,it lacks specific details on issues such as how to prevent the risk of bias and reduce the effect of bias in decision-making.The Interim Measures also fail to take causes of bias into account,and several principles must be further interpreted.Meanwhile,regulations on generative AI at the global level are still in their early stages.By forming a governance framework,this paper could provide the community with useful experiences and play a leading role.The framework includes at least three parts:first,determining the realm of governance and unifying related concepts;second,developing measures for different layers to identify the causes and specific aspects of bias;third,identifying parties with the skills to take responsibility for detecting bias intrusions and proposing a program for the allocation of liabilities among the large-scale platform developers.
基金Teaching Reform Program of Guangxi University of Chinese Medicine(XGJ23097,2024B028),Teaching Reform Program of Guangxi Higher Education(2024JGB229).
文摘With the rapid advancement of AI technology,especially the emergence of generative AI such as ChatGPT and ERNIE Bot,the field of education is undergoing profound changes.While they change the way information is obtained and processed,these AI technologies challenge traditional teaching models.Based on evaluating the feasibility of various generative AI tools for teaching and comparing their respective advantages and disadvantages,this paper delves into the application scenarios of these generative AI tools in English reading,writing,and translation,and explores their specific applications in the pre-class,in-class,and post-class parts of“College English Reading,Writing,and Translation”.It is hoped that through innovative teaching methods,both students’learning effectiveness and teachers’teaching efficiency can be improved.At the same time,it is crucial to guide students in recognizing the misinformation and biases that exist in generative AI,while emphasizing the significance of originality and intellectual property.Moreover,their critical thinking skills and proper academic concepts could be cultivated and help them prevent academic misconduct.
文摘Generative AI is rapidly employed by software developers to generate code or other software artifacts.However,the analysis and assessment of generative AI with respect to requirements analysis and modeling tasks,especially with UML,has received little attention.This paper investigates the capabilities of generative AI to aid in the creation of three types of UML models:UML use case models,class diagrams,and sequence diagrams.For this purpose,we designed an AI-aided UML modeling task in our course on software requirements modeling.50 undergraduates who majored in Software Engineering at Wuhan University completed the modeling task and the corresponding online survey.Our findings show that generative AI can help create these three types of UML models,but its performance is limited to identifying essential modeling elements of these UML models.
文摘The realization of an interoperable and scalable virtual platform, currently known as the “metaverse,” is inevitable, but many technological challenges need to be overcome first. With the metaverse still in a nascent phase, research currently indicates that building a new 3D social environment capable of interoperable avatars and digital transactions will represent most of the initial investment in time and capital. The return on investment, however, is worth the financial risk for firms like Meta, Google, and Apple. While the current virtual space of the metaverse is worth $6.30 billion, that is expected to grow to $84.09 billion by the end of 2028. But the creation of an entire alternate virtual universe of 3D avatars, objects, and otherworldly cityscapes calls for a new development pipeline and workflow. Existing 3D modeling and digital twin processes, already well-established in industry and gaming, will be ported to support the need to architect and furnish this new digital world. The current development pipeline, however, is cumbersome, expensive and limited in output capacity. This paper proposes a new and innovative immersive development pipeline leveraging the recent advances in artificial intelligence (AI) for 3D model creation and optimization. The previous reliance on 3D modeling software to create assets and then import into a game engine can be replaced with nearly instantaneous content creation with AI. While AI art generators like DALL-E 2 and DeepAI have been used for 2D asset creation, when combined with game engine technology, such as Unreal Engine 5 and virtualized geometry systems like Nanite, a new process for creating nearly unlimited content for immersive reality is possible. New processes and workflows, such as those proposed here, will revolutionize content creation and pave the way for Web 3.0, the metaverse and a truly 3D social environment.
基金supported by the Innovative Research Group Project of the National Natural Science Foundation of China(Grant No.202401-202712)。
文摘Recent advances in generative artiflcial intelligence(AI)technologies have been signiflcantly driven by models such as generative adversarial networks(GANs),variational autoencoders(VAEs),and denoising diffusion probabilistic models(DDPMs).Although architects recognize the potential of generative AI in design,personal barriers often restrict their access to the latest technological developments,thereby causing the application of generative AI in architectural design to lag behind.Therefore,it is essential to comprehend the principles and advancements of generative AI models and analyze their relevance in architecture applications.This paper flrst provides an overview of generative AI technologies,with a focus on probabilistic diffusion models(DDPMs),3D generative models,and foundation models,highlighting their recent developments and main application scenarios.Then,the paper explains how the abovementioned models could be utilized in architecture.We subdivide the architectural design process into six steps and review related research projects in each step from 2020 to the present.Lastly,this paper discusses potential future directions for applying generative AI in the architectural design steps.This research can help architects quickly understand the development and latest progress of generative AI and contribute to the further development of intelligent architecture.
基金supported by the German Federal Ministry of Education and Research(BMBF)in the Programme Kunstliche Intelligenz in der Hochschulbildung(Grant No.16DHBKI010)as well as the Programme Sachsen-Anhalt WISSENSCHAFT Gleichstellung,Qualifikation,NNachwuchs aus dem Europaischen Sozialfonds Plus(Grant No.ZS/2023/11/18180)。
文摘Generative artificial intelligence(GenAI)models,such as_ChatGPT,have rapidly gained popularity.Despite this widespread usage,there is still a imited understanding of how this emerging technology impacts different stakeholders in higher education.While extensive research exists on the general opportunities and risks in education,there is often a lack of specificity regarding the target audiencenamely,students,educators,and institutionsand concrete solution strategies and recommendations are typically absent.Our goal is to address the perspectives of students and educators separately and offer tailored solutions for each of these two stakeholder groups.This study employs a mixed-method approach that integrates a detailed online questionnaire of 188 students with a scenario_analysis to examine potential benefits and drawbacks introduced by GenAI.The findings indicate that students utilize the technology for tasks such as assignment writing and exam preparation,seeing it as an effective tool for achieving academic goals.Subsequent the scenario analysis provided insights into possible future scenarios,highlighting both opportunities and challenges of integrating GenAI within higher education for students as well as educators.The primary aim is to offer a clear and precise understanding of the potential implications for students and educators separately while providing recommendations and solution strategies.The results suggest that irresponsible and excessive use of the technology could pose significant challenges.Therefore,educators need to establish clear policies,reevaluate learning objectives,enhance AI skills,update curricula,and reconsider examination methods.
基金financial support from the Fundamental Research Grant Scheme(FRGS)under grant number:FRGS/1/2024/ICT02/TARUMT/02/1from the Ministry of Higher Education Malaysiafunded in part by the internal grant from the Tunku Abdul Rahman University of Management and Technology(TAR UMT)with grant number:UC/I/G2024-00129.
文摘This study systematically reviews the applications of generative artificial intelligence(GAI)in breast cancer research,focusing on its role in diagnosis and therapeutic development.While GAI has gained significant attention across various domains,its utility in breast cancer research has yet to be comprehensively reviewed.This study aims to fill that gap by synthesizing existing research into a unified document.A comprehensive search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines,resulting in the retrieval of 3827 articles,of which 31 were deemed eligible for analysis.The included studies were categorized based on key criteria,such as application types,geographical distribution,contributing organizations,leading journals,publishers,and temporal trends.Keyword co-occurrence mapping and subject profiling further highlighted the major research themes in this field.The findings reveal that GAI models have been applied to improve breast cancer diagnosis,treatment planning,and outcome predictions.Geographical and network analyses showed that most contributions come from a few leading institutions,with limited global collaboration.The review also identifies key challenges in implementing GAI in clinical practice,such as data availability,ethical concerns,and model validation.Despite these challenges,the study highlights GAI’s potential to enhance breast cancer research,particularly in generating synthetic data,improving diagnostic accuracy,and personalizing treatment approaches.This review serves as a valuable resource for researchers and stakeholders,providing insights into current research trends,major contributors,and collaborative networks in GAI-based breast cancer studies.By offering a holistic overview,it aims to support future research directions and encourage broader adoption of GAI technologies in healthcare.Additionally,the study emphasizes the importance of overcoming implementation barriers to fully realizeGAI’s potential in transforming breast cancer management.
