教育智能体作为人工智能技术与教育深度融合的创新产物,正在推动传统教育结构由“师—生”二元模式向“师—生—机”三元协同模式演进。在“师—生”二元交互模式中,教师的反馈对促进学生知识建构与动机调节至关重要。然而,在教育智能...教育智能体作为人工智能技术与教育深度融合的创新产物,正在推动传统教育结构由“师—生”二元模式向“师—生—机”三元协同模式演进。在“师—生”二元交互模式中,教师的反馈对促进学生知识建构与动机调节至关重要。然而,在教育智能体嵌入的“师—生—机”三元交互模式中,教育智能体的反馈机制及其教学效能尚不清楚。本研究利用“教育智能体—使用条件模型”(Pedagogical Agents—Conditions of Use Model,PACU)系统梳理2015—2025年国内外35篇教育智能体反馈文献,分析何种设计因素与执行功能(反馈形式及反馈内容)能促进学习。研究发现,教育智能体的形象、承担角色、性别等因素可能显著影响其反馈效果。教育智能体的面部表情反馈可显著提升社会临场感与学习表现,手势反馈需明确语义以避免认知干扰;类人语音可显著提升情感表现力并激发学生学习动机,“生成性文本”反馈采用较少但值得探索;认知反馈需依任务复杂度分层设计,在必要时可提供学习者认知支架;情感反馈应以积极情绪为主导,但需避免虚假情绪表达。上述发现能为相关人员优化教育智能体设计、完善其执行功能提供依据,进而提升教育智能体的反馈效果,助力智能时代个性化学习普及。展开更多
Artificial Intelligence (AI) is transforming organizational dynamics, and revolutionizing corporate leadership practices. This research paper delves into the question of how AI influences corporate leadership, examini...Artificial Intelligence (AI) is transforming organizational dynamics, and revolutionizing corporate leadership practices. This research paper delves into the question of how AI influences corporate leadership, examining both its advantages and disadvantages. Positive impacts of AI are evident in communication, feedback systems, tracking mechanisms, and decision-making processes within organizations. AI-powered communication tools, as exemplified by Slack, facilitate seamless collaboration, transcending geographical barriers. Feedback systems, like Adobe’s Performance Management System, employ AI algorithms to provide personalized development opportunities, enhancing employee growth. AI-based tracking systems optimize resource allocation, as exemplified by studies like “AI-Based Tracking Systems: Enhancing Efficiency and Accountability.” Additionally, AI-powered decision support, demonstrated during the COVID-19 pandemic, showcases the capability to navigate complex challenges and maintain resilience. However, AI adoption poses challenges in human resources, potentially leading to job displacement and necessitating upskilling efforts. Managing AI errors becomes crucial, as illustrated by instances like Amazon’s biased recruiting tool. Data privacy concerns also arise, emphasizing the need for robust security measures. The proposed solution suggests leveraging Local Machine Learning Models (LLMs) to address data privacy issues. Approaches such as federated learning, on-device learning, differential privacy, and homomorphic encryption offer promising strategies. By exploring the evolving dynamics of AI and leadership, this research advocates for responsible AI adoption and proposes LLMs as a potential solution, fostering a balanced integration of AI benefits while mitigating associated risks in corporate settings.展开更多
文摘教育智能体作为人工智能技术与教育深度融合的创新产物,正在推动传统教育结构由“师—生”二元模式向“师—生—机”三元协同模式演进。在“师—生”二元交互模式中,教师的反馈对促进学生知识建构与动机调节至关重要。然而,在教育智能体嵌入的“师—生—机”三元交互模式中,教育智能体的反馈机制及其教学效能尚不清楚。本研究利用“教育智能体—使用条件模型”(Pedagogical Agents—Conditions of Use Model,PACU)系统梳理2015—2025年国内外35篇教育智能体反馈文献,分析何种设计因素与执行功能(反馈形式及反馈内容)能促进学习。研究发现,教育智能体的形象、承担角色、性别等因素可能显著影响其反馈效果。教育智能体的面部表情反馈可显著提升社会临场感与学习表现,手势反馈需明确语义以避免认知干扰;类人语音可显著提升情感表现力并激发学生学习动机,“生成性文本”反馈采用较少但值得探索;认知反馈需依任务复杂度分层设计,在必要时可提供学习者认知支架;情感反馈应以积极情绪为主导,但需避免虚假情绪表达。上述发现能为相关人员优化教育智能体设计、完善其执行功能提供依据,进而提升教育智能体的反馈效果,助力智能时代个性化学习普及。
文摘Artificial Intelligence (AI) is transforming organizational dynamics, and revolutionizing corporate leadership practices. This research paper delves into the question of how AI influences corporate leadership, examining both its advantages and disadvantages. Positive impacts of AI are evident in communication, feedback systems, tracking mechanisms, and decision-making processes within organizations. AI-powered communication tools, as exemplified by Slack, facilitate seamless collaboration, transcending geographical barriers. Feedback systems, like Adobe’s Performance Management System, employ AI algorithms to provide personalized development opportunities, enhancing employee growth. AI-based tracking systems optimize resource allocation, as exemplified by studies like “AI-Based Tracking Systems: Enhancing Efficiency and Accountability.” Additionally, AI-powered decision support, demonstrated during the COVID-19 pandemic, showcases the capability to navigate complex challenges and maintain resilience. However, AI adoption poses challenges in human resources, potentially leading to job displacement and necessitating upskilling efforts. Managing AI errors becomes crucial, as illustrated by instances like Amazon’s biased recruiting tool. Data privacy concerns also arise, emphasizing the need for robust security measures. The proposed solution suggests leveraging Local Machine Learning Models (LLMs) to address data privacy issues. Approaches such as federated learning, on-device learning, differential privacy, and homomorphic encryption offer promising strategies. By exploring the evolving dynamics of AI and leadership, this research advocates for responsible AI adoption and proposes LLMs as a potential solution, fostering a balanced integration of AI benefits while mitigating associated risks in corporate settings.
