从用户调查到遥测驱动的AI智能体：探索个性化生产力解决方案的潜力
From User Surveys to Telemetry-Driven AI Agents: Exploring the Potential of Personalized Productivity Solutions
AI总结：
本文通过363人调查和40人对比实验，开发并验证了基于GPT-4和Viva Insights遥测数据的个性化生产力智能体，证明以用户为中心的设计与个性化-隐私平衡能有效提升信息工作者的生产力。
摘要：
信息工作者在现代工作场所中日益面临生产力挑战，在管理时间和有效利用工作场所分析数据以改善行为方面存在困难。尽管通过企业工具可以获得生产力指标，但工作者往往无法将这些数据转化为可操作的见解。我们提出了一种全面的、以用户为中心的方法，通过针对用户需求量身定制的基于AI的生产力智能体来应对这些挑战。采用两阶段方法，我们首先对363名参与者进行了调查，探索了生产力、沟通风格、智能体方法、个性特征、个性化和隐私等多个方面。基于调查见解，我们开发了一个由GPT-4驱动的个性化生产力智能体，该智能体利用通过Viva Insights从信息工作者那里收集的遥测数据来提供量身定制的帮助。我们在一项涉及40名参与者的研究中，将其性能与仪表板和叙述等替代性生产力辅助工具进行了比较。我们的研究结果强调了以用户为中心的设计、适应性以及AI辅助生产力工具中个性化与隐私之间平衡的重要性。基于这些见解，我们的工作为开发更有效的生产力解决方案提供了重要指导，最终为信息工作者带来优化的效率和用户体验。
英文摘要：
Information workers increasingly struggle with productivity challenges in modern workplaces, facing difficulties in managing time and effectively utilizing workplace analytics data for behavioral improvement. Despite the availability of productivity metrics through enterprise tools, workers often fail to translate this data into actionable insights. We present a comprehensive, user-centric approach to address these challenges through AI-based productivity agents tailored to users' needs. Utilizing a two-phase method, we first conducted a survey with 363 participants, exploring various aspects of productivity, communication style, agent approach, personality traits, personalization, and privacy. Drawing on the survey insights, we developed a GPT-4 powered personalized productivity agent that utilizes telemetry data gathered via Viva Insights from information workers to provide tailored assistance. We compared its performance with alternative productivity-assistive tools, such as dashboard and narrative, in a study involving 40 participants. Our findings highlight the importance of user-centric design, adaptability, and the balance between personalization and privacy in AI-assisted productivity tools. By building on these insights, our work provides important guidance for developing more effective productivity solutions, ultimately leading to optimized efficiency and user experiences for information workers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
