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Prioritizing human-AI collaboration in healthcare:the TRIAD framework for trustworthy governance,real-world,and integrated adaptive deployment 认领 引用
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作者 Jia Li Zi-Chun Zhou +1 位作者 Zhen-Chang Wang Han Lv 《Military Medical Research》 SCIE CAS CSCD 2026年第6期861-868,共8页
Artificial intelligence(AI)and big data are reshaping the healthcare landscape.However,clinical value depends on how well systems augment clinicians and fit into routine workflows.To this end,we introduce the TRIAD fr... Artificial intelligence(AI)and big data are reshaping the healthcare landscape.However,clinical value depends on how well systems augment clinicians and fit into routine workflows.To this end,we introduce the TRIAD framework:trustworthy governance,real-world clinical value,and integrated adaptive deployment,to guide the development,validation,and deployment of clinical AI.TRIAD requires explicit data provenance and intended use,fairness auditing,and calibrated uncertainty.This framework evaluates the human-AI team in real workflows using team-level metrics,including accuracy,safety,workload,and patterns of acceptance,editing,and overriding.Deployment proceeds via staged rollouts with pre-registered guardrails and continuous monitoring of performance and subgroup impact.TRIAD views intelligence as a property of the human-AI team rather than the AI model alone.Aligning governance,evaluation,and deployment around clinicians and patients enables durable gains in safety,equity,efficiency,and experience,thereby elevating clinical value. 展开更多
关键词 TRIAD Artificial intelligence(AI) Human-AI collaboration Trust mechanisms Clinical decision support
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Implementation of Human-AI Interaction in Reinforcement Learning: Literature Review and Case Studies 认领 引用
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作者 Shaoping Xiao Zhaoan Wang +3 位作者 Junchao Li Caden Noeller Jiefeng Jiang Jun Wang 《Computers, Materials & Continua》 SCIE EI 2026年第2期1-62,共62页
Theintegration of human factors into artificial intelligence(AI)systems has emerged as a critical research frontier,particularly in reinforcement learning(RL),where human-AI interaction(HAII)presents both opportunitie... Theintegration of human factors into artificial intelligence(AI)systems has emerged as a critical research frontier,particularly in reinforcement learning(RL),where human-AI interaction(HAII)presents both opportunities and challenges.As RL continues to demonstrate remarkable success in model-free and partially observable environments,its real-world deployment increasingly requires effective collaboration with human operators and stakeholders.This article systematically examines HAII techniques in RL through both theoretical analysis and practical case studies.We establish a conceptual framework built upon three fundamental pillars of effective human-AI collaboration:computational trust modeling,system usability,and decision understandability.Our comprehensive review organizes HAII methods into five key categories:(1)learning from human feedback,including various shaping approaches;(2)learning from human demonstration through inverse RL and imitation learning;(3)shared autonomy architectures for dynamic control allocation;(4)human-in-the-loop querying strategies for active learning;and(5)explainable RL techniques for interpretable policy generation.Recent state-of-the-art works are critically reviewed,with particular emphasis on advances incorporating large language models in human-AI interaction research.To illustrate some concepts,we present three detailed case studies:an empirical trust model for farmers adopting AI-driven agricultural management systems,the implementation of ethical constraints in roboticmotion planning through human-guided RL,and an experimental investigation of human trust dynamics using a multi-armed bandit paradigm.These applications demonstrate how HAII principles can enhance RL systems’practical utility while bridging the gap between theoretical RL and real-world human-centered applications,ultimately contributing to more deployable and socially beneficial intelligent systems. 展开更多
关键词 Human-AI interaction reinforcement learning partially observable environments trust model ethical constraints
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从交互到协作,数智时代的人智关系何去何从?——评《Human-AI Interaction and Collaboration》 认领 引用
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作者 夏立新 《信息资源管理学报》 CSSCI 2026年第2期166-168,共3页
数智时代,用户与人工智能系统的交互模式正在发生怎样的转变?《Human-AI Interaction and Collaboration》一书通过理论框架构建、应用场景描述、潜在风险辨识,为读者全方位地解析了人智交互与协作的内在逻辑,提出“以人为本”这一人工... 数智时代,用户与人工智能系统的交互模式正在发生怎样的转变?《Human-AI Interaction and Collaboration》一书通过理论框架构建、应用场景描述、潜在风险辨识,为读者全方位地解析了人智交互与协作的内在逻辑,提出“以人为本”这一人工智能系统设计的首要原则,详细分析了用户感知、系统设计、人智关系等一系列因素如何影响人智协作的效率与成果,在针对潜在风险制定应对方案的同时,为读者展示了人智协作在医疗、科研、金融等多学科场景下的广阔前景。该书为未来人智交互与协作相关研究提供了坚实的理论框架,也为人工智能系统设计者提供了伦理指南。 展开更多
关键词 人智交互 人智协作 人工智能系统 多学科应用场景 书评
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AI Translation Agent Empowers Domestic Game Localization From the Perspective of“Human-AI Collaboration”——A Case Study of Zhipu Qingyan Platform 认领 引用
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作者 GAO Yue CHE Huan-huan 《US-China Foreign Language》 2026年第1期34-38,共5页
Against the backdrop of domestic games“going global”becoming a core path for the international communication of Chinese culture,the quality and efficiency of game localization directly affect the effectiveness of ov... Against the backdrop of domestic games“going global”becoming a core path for the international communication of Chinese culture,the quality and efficiency of game localization directly affect the effectiveness of overseas market expansion.Traditional translation models suffer from high costs,long cycles,and unstable quality,while general artificial intelligence(AI)translation faces shortcomings,such as inconsistent terminology and poor cultural adaptation.Based on the concept of“human-AI collaboration”,this paper constructs an AI translation agent adapted to game localization scenarios using the Zhipu Qingyan platform.Through the construction of an exclusive knowledge base,customized workflow arrangement,and feedback optimization mechanism,it achieves dual improvements in translation efficiency and quality.Tests show that the agent increases translation efficiency by over 65%,the manual evaluation accuracy of cultural imagery transmission reaches 82%,the terminology consistency rate exceeds 92%,and the translation accuracy rate is 89%.It can shorten the translation cycle by 70%and reduce costs by more than 80%,providing an efficient and feasible technical solution for domestic game localization with significant practical value. 展开更多
关键词 human-AI collaboration AI translation agent domestic game localization Zhipu Qingyan
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From Automated Feedback to Human-AI Collaboration: Mechanisms and Pathways of AI Integration in Foreign Language Education 认领 引用
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作者 Miaomiao Cai Jing Ge 《Journal of Contemporary Educational Research》 2026年第6期70-75,共6页
Artificial intelligence(AI)is continuously transforming knowledge organization,teaching activity structure,and learning support mechanisms in foreign language education.Empirical studies in the past five years have sh... Artificial intelligence(AI)is continuously transforming knowledge organization,teaching activity structure,and learning support mechanisms in foreign language education.Empirical studies in the past five years have shown that AI integration in foreign language education is not a linear process of single-technology application in classrooms,but an evolving trajectory deepening from automated feedback to intelligent interaction and further to human-AI collaboration.Early research mainly focused on automated writing evaluation,automatic speech recognition,machine translation,and basic chatbots,verifying their roles in reducing feedback delay,expanding practice opportunities,and alleviating teachers’repetitive workload.With the integration of large language models into teaching contexts,research has gradually shifted to higher-level issues such as writing ideation,oral interaction,learning engagement,selfregulation,and human-AI collaboration.Based on a review of representative empirical studies in the past five years,this paper analyzes the evolution,core application fields,main mechanisms,practical limitations,and future directions of AI integration in foreign language education.The study argues that the educational effectiveness of AI is not directly determined by technological advancement,but by the combined effects of task design,feedback arrangement,teacher intervention,learner literacy,and contextual adaptation.Future research should shift from verifying whether tools are effective to explaining how human-AI collaboration works effectively,and construct an interpretable and generalizable research framework in more authentic classroom ecologies,longer time spans,and a wider range of languages. 展开更多
关键词 Artificial intelligence Foreign language education Automated feedback Human-AI collaboration
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Redefining the Programmer:Human-AI Collaboration,LLMs,and Security in Modern Software Engineering 认领 引用
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作者 Elyson De La Cruz Hanh Le +2 位作者 Karthik Meduri Geeta Sandeep Nadella Hari Gonaygunta 《Computers, Materials & Continua》 SCIE EI 2025年第11期3569-3582,共14页
The rapid integration of artificial intelligence(AI)into software development,driven by large language models(LLMs),is reshaping the role of programmers from traditional coders into strategic collaborators within Indu... The rapid integration of artificial intelligence(AI)into software development,driven by large language models(LLMs),is reshaping the role of programmers from traditional coders into strategic collaborators within Industry 4.0 ecosystems.This qualitative study employs a hermeneutic phenomenological approach to explore the lived experiences of Information Technology(IT)professionals as they navigate a dynamic technological landscape marked by intelligent automation,shifting professional identities,and emerging ethical concerns.Findings indicate that developers are actively adapting to AI-augmented environments by engaging in continuous upskilling,prompt engineering,interdisciplinary collaboration,and heightened ethical awareness.However,participants also voiced growing concerns about the reliability and security of AI-generated code,noting that these tools can introduce hidden vulnerabilities and reduce critical engagement due to automation bias.Many described instances of flawed logic,insecure patterns,or syntactically correct but contextually inappropriate suggestions,underscoring the need for rigorous human oversight.Additionally,the study reveals anxieties around job displacement and the gradual erosion of fundamental coding skills,particularly in environments where AI tools dominate routine development tasks.These findings highlight an urgent need for educational reforms,industry standards,and organizational policies that prioritize both technical robustness and the preservation of human expertise.As AI becomes increasingly embedded in software engineering workflows,this research offers timely insights into how developers and organizations can responsibly integrate intelligent systems to promote accountability,resilience,and innovation across the software development lifecycle. 展开更多
关键词 Human-AI collaboration large language models AI security developer identity ethical AI in software development AI-assisted programming
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Toward an Integrated Framework for Understanding and Guiding Human-AI Collaboration in Secondary School EFL Teaching 认领 引用 被引量：1
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作者 Siyuan Yang Baohua Su Xixi Yang 《教育技术与创新》 2025年第4期36-44,共9页
This study explores the impact of human-AI collaborative teaching strategies on English teachers in secondary schools.Based on semi-structured interviews with five English teachers in Jiangxi Province,thematic analysi... This study explores the impact of human-AI collaborative teaching strategies on English teachers in secondary schools.Based on semi-structured interviews with five English teachers in Jiangxi Province,thematic analysis was conducted using the SAMR,UTAUT,and GHEX-IPACK theoretical frameworks.The findings indicate that AI technology is primarily applied in scenarios such as resource generation,assignment distribution,and learning analytics.By substituting traditional tools,enhancing teaching interactions,and reconstructing instructional processes,AI facilitates a shift in teaching strategies from“teacher-led”to“human-AI collaboration”.Teachers generally recognized the potential of this model for improving efficiency and supporting personalized learning,but also pointed out challenges,including data bias,hardware limitations,and a lack of emotional interaction.The study suggests that achieving deep human-AI collaboration requires balancing technological efficacy with humanistic care relying on blended instructional design and teacher training to optimize teachers’knowledge structures.This research preliminary constructs a practical model of human-AI collaboration in secondary school English education,providing insights for teacher professional development. 展开更多
关键词 human-AI collaboration artificial intelligence in education teaching strategies SAMR UTAUT GHEX-IPACK
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Human-AI Cooperation in Education: Human in Loop and Teaching as leadership 认领 引用 被引量：5
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作者 Feng Chen 《教育技术与创新》 2022年第1期14-25,共12页
Using the differences and complementarities between human intelligence and artificial intelligence(AI),a hybrid-augmented intelligence,that is both stronger than human intelligence and AI,is created through Human-AI C... Using the differences and complementarities between human intelligence and artificial intelligence(AI),a hybrid-augmented intelligence,that is both stronger than human intelligence and AI,is created through Human-AI Cooperation(HAC)for teaching and learning.Human-AI Cooperation is infiltrating into all links of education,and recent research has focused a lot on the impact of teaching,learning,management,and evaluation with Human-AI Cooperation.However,AI still has its limits of intelligence,and cannot cooperate as humans.Thus,it is critical to study the obstacles of Human-AI Cooperation in education,as AI plays a role as a partner,not a tool.This study discussed for the first time how teachers and AI cooperate based on Multiple Intelligences of AI proposed by Andrzej Cichocki and puts forward a new Human-AI Cooperation teaching mode:human in the loop and teaching as leadership.It is proposed that humans in the loop and teaching as leadership can solve the problem that AI cannot cope with complex and dynamic teaching tasks in open situations,as well as the limits of intelligence for AI. 展开更多
关键词 Human-AI Cooperation Education Human in Loop Teaching as leadership Multiagents Multiple intelligences Emotional intelligence Social intelligence Creative Intelligence Innovative intelligence Ethical and moral intelligence Hybrid-augmented intelligence
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Human-AI coordination via policy generation from language-guided diffusion 认领 引用
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作者 Kunmin LIN Lei YUAN +3 位作者 Ziqian ZHANG Lihe LI Feng CHEN Yang YU 《Science China(Technological Sciences)》 SCIE EI CAS CSCD 2026年第1期149-161,共13页
Developing intelligent agents that can effectively coordinate with diverse human partners is a fundamental goal of artificial general intelligence.Previous approaches typically generate a variety of partners to cover ... Developing intelligent agents that can effectively coordinate with diverse human partners is a fundamental goal of artificial general intelligence.Previous approaches typically generate a variety of partners to cover human policies,and then either train a single universal agent or maintain multiple best-response(BR)policies for different partners.However,the first direction struggles with the stochastic and multimodal nature of human behaviors,and the second relies on costly few-shot adaptations during policy deployment,which is unbearable in real-world applications such as healthcare and autonomous driving.Recognizing that human partners can easily articulate their preferences or behavioral styles through natural languages(NLs)and make conventions beforehand,we propose a framework for Human-AI Coordination via Policy Generation from Language-guided Diffusion(Haland).Haland first trains BR policies for various partners using reinforcement learning,and then compresses policy parameters into a single latent diffusion model,conditioned on task-relevant language derived from their behaviors.Finally,the alignment between task-relevant and NLs is achieved to facilitate efficient human-AI coordination.Empirical evaluations across diverse cooperative environments demonstrate that Haland generates agents with significantly enhanced zero-shot coordination performance,utilizing only NL instructions from various partners,and outperforms existing methods by approximately 89.64%. 展开更多
关键词 reinforcement learning human-AI coordination diffusion language-guided reinforcement learning
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A Review of Human-AI Synergy in Smart Energy Management Concepts,Functions,Applications,and Future Frontiers 认领 引用
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作者 Sihai An Jing Qiu +3 位作者 Jiafeng Lin Zhe Yuan Weiyi Tian Zongyu Yao 《Energy Internet》 2026年第1期5-22,共18页
Smart energy management systems(EMS)are entering a phase of rapid transformation.Artificial intelligence(AI)-including machine learning(ML),deep learning(DL),and reinforcement learning(RL)-has become the computational... Smart energy management systems(EMS)are entering a phase of rapid transformation.Artificial intelligence(AI)-including machine learning(ML),deep learning(DL),and reinforcement learning(RL)-has become the computational backbone for real-time forecasting,scheduling,and control of renewable-rich power systems.Yet the long-term efficiency,resilience,and social acceptance of these systems depend critically on human-AI synergy:well-structured roles for people to supervise,override,and enrich AI decisions.This review integrates recent literature on AI-powered EMS for smart grids,buildings,microgrids,and isolated hybrid systems with emerging human-in-the-loop(HiTL)paradigms that explicitly incorporate occupants,operators,and policy makers.It offers an evaluation of data-driven forecasting,adaptive optimisation,and edge intelligence,and highlights research gaps in transparency,interoperability,and co-optimisation of technical and human objectives.We show that well-designed human-AI collaboration improves not only energy efficiency and renewable integration but also the robustness and trustworthiness of future energy systems. 展开更多
关键词 AI human-AI synergy smart energy management
The naturalness of AI tutoring in English speaking practice:a conversation analysis of turn-taking in human-AI interaction 认领 引用
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作者 Wenting Li 《Advances in Humanities Research》 2026年第7期156-171,共16页
Interactional naturalness of human-AI dialogue is a key factor in fostering meaningful communicative engagement.This study addresses this gap by adopting Conversation Analysis(CA)as a methodological framework to exami... Interactional naturalness of human-AI dialogue is a key factor in fostering meaningful communicative engagement.This study addresses this gap by adopting Conversation Analysis(CA)as a methodological framework to examine recorded speaking practice sessions between English as a Second or Foreign Language(ESL/EFL)learners and an AI tutoring system.Drawing on the principles of emergence,indexicality,and recipient design,the study investigates why and how interactional naturalness is violated in AI-mediated conversation.These findings reveal that while the AI tutor produces grammatically well-formed and topically relevant utterances,its conduct frequently diverges from the moment-by-moment,context-sensitive organization that characterizes natural human interaction.Particularly,the AI system fails to consistently align with emergent turn sequences,indexical references,and recipient-designed responses.The results indicate that current AI tutoring systems,despite advanced language models,have not yet achieved interactional naturalness as defined from a conversation-analytic perspective.Therefore,a shift in evaluation criteria from technical accuracy to interactional quality in the design and assessment of AI-based language tutors is called-for. 展开更多
关键词 Artificial Intelligence(AI)tutoring EFL/ESL speaking practice interactional naturalness conversation analysis human-AI interaction
Open and real-world human-AI coordination by heterogeneous training with communication 认领 引用
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作者 Cong GUAN Ke XUE +5 位作者 Chunpeng FAN Feng CHEN Lichao ZHANG Lei YUAN Chao QIAN Yang YU 《Frontiers of Computer Science》 SCIE EI CSCD 2025年第4期59-76,共18页
Human-AI coordination aims to develop AI agents capable of effectively coordinating with human partners,making it a crucial aspect of cooperative multi-agent reinforcement learning(MARL).Achieving satisfying performan... Human-AI coordination aims to develop AI agents capable of effectively coordinating with human partners,making it a crucial aspect of cooperative multi-agent reinforcement learning(MARL).Achieving satisfying performance of AI agents poses a long-standing challenge.Recently,ah-hoc teamwork and zero-shot coordination have shown promising advancements in open-world settings,requiring agents to coordinate efficiently with a range of unseen human partners.However,these methods usually assume an overly idealistic scenario by assuming homogeneity between the agent and the partner,which deviates from real-world conditions.To facilitate the practical deployment and application of human-AI coordination in open and real-world environments,we propose the first benchmark for open and real-world human-AI coordination(ORC)called ORCBench.ORCBench includes widely used human-AI coordination environments.Notably,within the context of real-world scenarios,ORCBench considers heterogeneity between AI agents and partners,encompassing variations in capabilities and observations,which aligns more closely with real-world applications.Furthermore,we introduce a framework known as Heterogeneous training with Communication(HeteC)for ORC.HeteC builds upon a heterogeneous training framework and enhances partner population diversity by using mixed partner training and frozen historical partners.Additionally,HeteC incorporates a communication module that enables human partners to communicate with AI agents,mitigating the adverse effects of partially observable environments.Through a series of experiments,we demonstrate the effectiveness of HeteC in improving coordination performance.Our contribution serves as an initial but important step towards addressing the challenges of ORC. 展开更多
关键词 human-AI coordination multi-agent reinforcement learning communication open-environment coordination real-world coordination
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Human-AI Collaborative Writing:Pedagogies for Using LLMs to Improve the Ideation and Revision Process in Academic Writing 认领 引用
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作者 Sophia LI 《Artificial Intelligence Education Studies》 2025年第2期19-31,共13页
This paper explores effective human-AI collaboration in academic writing using Large Language Models(LLMs).Focusing on the two critical stages of ideation and revision,the article argues that higher education institut... This paper explores effective human-AI collaboration in academic writing using Large Language Models(LLMs).Focusing on the two critical stages of ideation and revision,the article argues that higher education institutions must develop specific pedagogical strategies to guide students in leveraging the benefits of LLMs while mitigat-ing risks such as academic integrity issues,over-reliance,and bias.The core of these strategies is to emphasize the primacy of human agency,critical thinking,and ethical responsibility.The ultimate goal is to transform AI from a potential pitfall into a powerful tool that enhances scholarly skills and depth of thought,rather than being used as a simple text generator. 展开更多
关键词 Human-AI Collaboration Academic Writing Large Language Models(LLMs) Pedagogy Critical Thinking
人机信任对协同决策质量的影响:人机共享心智模式的中介作用 认领 引用
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作者 牛莉霞 林彦宏 《中国安全科学学报》 EI CAS CSCD 北大核心 2026年第2期244-252,共9页
为提升复杂工业场景下人机协同决策质量,缓解人类对人工智能(AI)的信任不足并弥合人机协作中的认知差异,基于心智理论,构建人机信任、人机共享心智模式(Human-AI SMM)与人机协同决策质量之间的关系模型,并引入任务复杂性作为调节变量。... 为提升复杂工业场景下人机协同决策质量,缓解人类对人工智能(AI)的信任不足并弥合人机协作中的认知差异,基于心智理论,构建人机信任、人机共享心智模式(Human-AI SMM)与人机协同决策质量之间的关系模型,并引入任务复杂性作为调节变量。首先,根据各变量间的理论关系提出假设,并结合人机信任量表、Human-AI SMM量表、人机协同决策质量量表以及任务复杂性量表设计问卷;然后,向全国范围内AI使用企业的一线员工发放问卷,并收集有效样本493份;最后,采用SPSS 26.0、AMOS 24.0及Process 4.0对收集的有效样本进行数据分析与假设检验。结果表明:人机信任显著正向影响人机协同决策质量;Human-AI SMM在人机信任与人机协同决策质量之间起中介作用;任务复杂性正向调节人机信任与Human-AI SMM之间的关系。 展开更多
关键词 人机信任 人机协同决策质量 人机共享心智模式(Human-AI SMM) 心智理论 中介作用 任务复杂性
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生成式人工智能赋能职业教育:人智协同的范式重构、机理阐释与进路设计 认领 引用 被引量：11
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作者 兰国帅 蒋顷烁 +3 位作者 郑明扬 肖琪 宋帆 王茜 《职教论坛》 北大核心 2026年第1期30-39,共10页
生成式人工智能(GenAI)的迅猛发展,推动职业教育从“技术赋能”走向“系统重构”。然而,当前“AI+职业教育”实践仍多停留在“技术工具论”阶段,人工智能常被机械嵌入既有教学流程,尚未引发教育理念、模式与制度的深层变革。教师面临“... 生成式人工智能(GenAI)的迅猛发展,推动职业教育从“技术赋能”走向“系统重构”。然而,当前“AI+职业教育”实践仍多停留在“技术工具论”阶段,人工智能常被机械嵌入既有教学流程,尚未引发教育理念、模式与制度的深层变革。教师面临“技能替代焦虑”,学生易陷入“AI依赖”与认知浅表化;算法偏见、生成“幻觉”、数据安全等伦理风险日益凸显;适配人智协同的课程标准、评价体系及教师发展机制仍显缺失。立足国家“人工智能+”行动与职业教育数字化转型战略,以人智协同理论为核心,构建“技术—教学—治理”三维分析框架,系统阐释GenAI赋能职业教育的内在机理与实现路径。研究发现,当前GenAI在课程开发、虚拟实训、个性化学习与智能评价等环节的应用不断深化,但仍面临技术伦理风险、教师角色转型滞后、产教协同机制不畅等系统性挑战。为此,文章提出以“教师—AI—学生”三元协同为核心的人智协同职业教育新范式,并从目标、角色、流程、资源、评价五个维度构建可操作的实践框架。在此基础上,从治理体系、师资发展、校企协同、质量监测四个层面提出推动职业教育人智协同高质量发展的系统路径,为建构适应新质生产力要求的现代职业教育体系提供理论参照与实践指引。 展开更多
关键词 生成式人工智能 职业教育 人智协同 范式重构 教学创新 教育治理 新质生产力
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人智协同模式的合规与共治:教育可解释人工智能治理框架构建 认领 引用 被引量：5
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作者 兰国帅 郑明扬 +2 位作者 蒋顷烁 肖琪 宋帆 《远程教育杂志》 CSSCI 北大核心 2026年第1期51-60,82,共10页
生成式人工智能深度融入教育核心环节,在赋能教学革新的同时,其固有的“黑箱”特性也引发了透明度缺失、算法偏见与问责困难等严峻治理挑战,对人智协同教育的实现构成根本障碍。为应对上述挑战,研究旨在构建一个面向人智协同、融贯技术... 生成式人工智能深度融入教育核心环节,在赋能教学革新的同时,其固有的“黑箱”特性也引发了透明度缺失、算法偏见与问责困难等严峻治理挑战,对人智协同教育的实现构成根本障碍。为应对上述挑战,研究旨在构建一个面向人智协同、融贯技术可行性与教育可接受性的教育可解释人工智能综合治理框架。首先,通过批判性整合国际政策与学术理论,廓清教育可解释性的核心概念体系,为治理实践奠定理论基础。其次,从“政策合规—协同治理—能力建设”三个维度构建治理框架:系统解析了以《人工智能法案》为核心的欧盟数字法律生态,并将其转化为适用于教育高风险场景的合规操作清单与治理工具;通过对智能阅卷、课堂行为分析等本土典型案例的深度剖析,揭示了多元利益相关者的差异化解释需求、治理干预与责任共担机制;借鉴国际能力框架,设计了分阶段、分角色的教育者可解释人工智能能力矩阵。最终,提出了从宏观制度到微观实践、从主体赋能到生态培育的系统性路径,不仅为破解教育人工智能的“黑箱”困境提供了系统的理论分析框架,也为在中国教育语境下构建“以人为本、技术向善”的治理新生态提供了可操作性的实践路线。 展开更多
关键词 教育可解释人工智能 治理框架 人智协同 算法治理 教育人工智能 教师数字素养
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AI for Science范式变革下科研人员胜任力提升及对策 认领 引用 被引量：5
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作者 王硕 阎妍 李正风 《中国科学院院刊》 CAS CSSCI CSCD 北大核心 2026年第4期774-783,共10页
在AI for Science引发科研范式变革、全球科技竞争加剧的背景下,系统性提升科研人员的AI4S胜任力,是加快形成新质生产力、实现高水平科技自立自强的重要基础。AI4S在实践路径上呈现出专用型与通用型2种形态,对科研人员提出了超越单一技... 在AI for Science引发科研范式变革、全球科技竞争加剧的背景下,系统性提升科研人员的AI4S胜任力,是加快形成新质生产力、实现高水平科技自立自强的重要基础。AI4S在实践路径上呈现出专用型与通用型2种形态,对科研人员提出了超越单一技术维度的通用能力要求。AI4S胜任力包含4个核心维度:科学问题的计算思维、人机交互与验证能力、跨学科协作与沟通能力,以及科技伦理意识与责任。未来的科技人才建设应将零散的AI技能自学转变为有组织的AI4S胜任力培养,实现从技术工具教学向计算思维培养的理念转变,构建面向通用型与专用型需求的递进式支持体系,激励可复用的专家知识产出,强化科技伦理教育与治理。 展开更多
关键词 AI for Science 第五范式 知识生产 人机协作 人工智能素养
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探寻可解释人工智能(XAI)的“解释奇点”:基于AIGC信息采纳视角 认领 引用 被引量：5
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作者 卢新元 徐安琪 张进澳 《数据分析与知识发现》 EI CSSCI CSCD 北大核心 2026年第1期76-87,共12页
【目的】探寻可解释性和解释精度与信息采纳行为的作用关系,优化多场景适用的可解释设计。【方法】通过两个不同情境的平行实验,分析人工智能可解释性、解释内容关联度对用户信息采纳的影响,探讨不同任务信息需求情境下人工智能“解释... 【目的】探寻可解释性和解释精度与信息采纳行为的作用关系,优化多场景适用的可解释设计。【方法】通过两个不同情境的平行实验,分析人工智能可解释性、解释内容关联度对用户信息采纳的影响,探讨不同任务信息需求情境下人工智能“解释奇点”的变化规律。【结果】人工智能的可解释性对用户信息采纳具有显著影响,其具体的影响关系受制于内容关联度的制约;“解释奇点”是可解释设计作用于信息采纳行为的转折点,不同任务情境下“解释奇点”存在一定变化。【局限】未考虑虚假信息的存在,对可解释人工智能与用户信息采纳“效果”的揭示较为有限。【结论】本研究在多情境视角下揭示人工智能生成内容(AIGC)解释信息采纳机制差异,挖掘了“解释奇点”的变化规律。该发现不仅揭示了解释关系与内容关联度在驱动用户信息采纳中的关键作用,更强调其对用户信息采纳行为的边际影响。 展开更多
关键词 可解释人工智能 人智交互 人工智能生成内容 信息采纳行为 生成式人工智能
从盲信到反思:元认知引导智能体对人智交互信任的校准作用 认领 引用 被引量：3
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作者 齐云飞 耿秀娟 +1 位作者 宋雪含 罗青娜 《图书馆论坛》 CSSCI 北大核心 2026年第1期104-115,共12页
文章基于元认知理论开发覆盖个体认知全过程的元认知引导智能体,探索其对人智交互信任校准和元认知水平提升的影响;通过文献梳理,明确元认知引导智能体的功能特征和影响机理,采用多智能技术完成原型系统开发;采用准实验方法将参与者随... 文章基于元认知理论开发覆盖个体认知全过程的元认知引导智能体,探索其对人智交互信任校准和元认知水平提升的影响;通过文献梳理,明确元认知引导智能体的功能特征和影响机理,采用多智能技术完成原型系统开发;采用准实验方法将参与者随机分配到元认知智能体引导组与非引导组,完成信息搜索和方案生成任务;通过问卷和操作录像编码采集数据,对比分析两组在自我报告信任、行为指标信任、过度信任和元认知水平上的差异。研究发现,元认知引导智能体通过生成提示信息,能够引导个体进行元认知活动,激励他们进行更多追问和更长时间思考,实现信任态度和系统质量的同步提升,在提高信任水平的同时避免盲信行为的发生;为了提高智能体元认知引导效果,需要探索合适的教育方法,对使用者元认知能力进行长期系统化的教育。 展开更多
关键词 元认知 人智交互 信任校准 智能体
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人文社科研究者的提示词行为探究及提示素养培育启示——元认知活动视角 认领 引用 被引量：3
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作者 景雨田 赵宇翔 +2 位作者 王杜荣 刘思琦 朱庆华 《图书情报知识》 CSSCI 北大核心 2026年第1期51-63,共13页
[目的/意义]旨在丰富生成式人工智能(Generative Artificial Intelligence,GenAI)情境下对用户人智交互行为的探索,为人文社科研究者的提示素养培育工作提供对策建议。[研究设计/方法]基于元认知理论,采用日记研究法展开纵向追踪,借鉴Gi... [目的/意义]旨在丰富生成式人工智能(Generative Artificial Intelligence,GenAI)情境下对用户人智交互行为的探索,为人文社科研究者的提示素养培育工作提供对策建议。[研究设计/方法]基于元认知理论,采用日记研究法展开纵向追踪,借鉴Gioia方法论对25位人文社科研究者的访谈数据进行编码分析并构建模型。[结论/发现]人文社科研究者的提示词行为演化呈现出显著的阶段性特征,包括探索适应、策略建构与调控、迁移和创新。同时,识别出了试探型、结构化和高阶提示设计三类提示词行为,以及与之对应的适应性信息交互、策略性信息交互、协同式共创交互三类阶段。此外,元认知知识、体验和调控对不同阶段的提示素养培育产生影响,在此基础上构建了“元认知活动-提示词行为-提示素养”整合模型。[创新/价值]拓展了元认知理论在人智交互中的应用边界,为人文社科研究者提示素养培育提供理论参考与实践启示。 展开更多
关键词 提示素养 提示词行为 元认知理论 人文社科研究者 人智交互
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