研究资讯 | Call for papers：Information Systerm Research- 大数跨境

首页

研究资讯 | Call for papers：Information Systerm Research

营在社媒

2025-06-20

导读：当前人工智能在处理数据和实现任务自动化方面很有效，但它往往缺乏人情味，不利于人工智能的应用。为解决这些不足，必须重新思考人工智能的发展方向，采用一种强调共情设计的方法，即将伦理考量、文化敏感性和情商融

点击上方蓝字关注，了解更多前沿研究

征文期刊

Information Systerm Research

征文主题

ISR Special Issue on Compassionate AI

关于“共情人工智能”的特刊

截止日期

January 25, 2026

（提交将于January 10, 2026 开放）

客座编辑

Dr.Rajiv Kohli

(Raymond A. Mason School of Business at William & Mary）

rajiv.kohli@mason.wm.edu

Prof. Meng Li

（ University of Houston）

mli@bauer.uh.edu

Prof. Ting Li

（ Rotterdam School）

tli@rsm.nl

Prof. Paul A. Pavlou

(University of Miami )

pavlou@miami.edu

特刊动机

尽管人工智能（包括生成式和智能体形式）持续重塑着各个行业和社会，但它在很大程度上仍然缺乏人类特质，如今的人工智能常常被描述为机械的，甚至是 “枯燥乏味的”。事实上，当前的人工智能设计范式侧重于将效率、准确性和计算复杂性最大化，往往以人类互动中固有的情感、社会和道德层面为代价。虽然人工智能在处理数据和实现任务自动化方面很有效，但它往往缺乏人情味，从而脱离人类体验，不利于人工智能的应用。为解决这些不足，必须重新思考人工智能的发展方向，采用一种强调共情设计的方法，即将伦理考量、文化敏感性和情商融入人工智能系统的核心设计中。通过植入这些原则，人工智能不仅应具备专业的技术能力，还应能够理解并回应人类用户的多样化需求，以确保人工智能的进步能切实造福人类。以共情为核心的人工智能设计的需求从未像现在这样迫切。

征文话题

This special issue invites submissions of rigorous and creative scholarly work related to compassionate AI. Relevant areas for this SI include, but are not limited to:

Healthcare: Compassionate AI can enhance patient care by predicting adverse events, assisting with end-of-life decision-making, and providing emotional support to patients and families. For instance, AI-powered chatbots and virtual assistants can offer non-judgmental, empathetic support to individuals facing mental health challenges, while monitoring systems can aid older adults by alleviating social isolation through companionship and daily activity tracking.
Crisis Management: During natural disasters or emergencies, compassionate AI systems can analyze real-time data, such as social media posts, to identify distressed individuals and provide timely assistance. These systems can also coordinate resource allocation and deliver emotionally sensitive communications to those affected.
Education: AI systems can transform education by personalizing learning experiences, identifying individual strengths and weaknesses, and adapting instruction to meet diverse student needs. Additionally, empathetic AI-driven feedback mechanisms can support students' emotional well-being, fostering engagement and boosting motivation in learning environments. These AI technologies can also play a pivotal role in improving access to education for underserved populations.
Social Services: Compassionate AI can support victims of trauma or abuse by offering non-judgmental, understanding virtual assistance tailored to their needs.

These AI systems can also connect individuals with critical resources, providing a safe and accessible channel for seeking help.

Customer Service: AI-powered chatbots can enhance customer experiences by providing compassionate and empathetic responses to inquiries, reducing frustration, and increasing satisfaction. By recognizing and adapting to customer emotions, these systems can de-escalate tense situations and build stronger customer relationships.
Human Resources: AI can monitor employee well-being by detecting signals of stress or burnout and proactively offering support resources. Furthermore,emotionally aware AI systems can improve workplace dynamics by facilitating more compassionate and effective communication between managers and team members.

Examples of topics/themes that fit this special issue include, but are not limited to:

Redefining Compassion in the Age of AI: How should compassion be defined in the context of AI? How does it differ from other concepts, such as empathy, ethics, and responsibility, and how can these be integrated into AI systems? What can historical and philosophical frameworks teach us about embedding compassion in AI?
Embedding Compassion in AI Design: How can compassion be operationalized, embedded, and measured in AI designs? What role do algorithms, data, and societal and cultural considerations play in designing compassionate AI systems?
Measuring Compassion in AI Systems: What metrics can be developed to evaluate the level of compassion in AI interactions? How can these metrics be standardized across diverse AI applications and settings?
Governance of Compassionate AI: What governance models are needed to ensure AI systems incorporate compassion while balancing efficiency and technical sophistication?
Accountability in Compassionate AI: When AI systems fail to exhibit compassion or cause harm, who should be held accountable—developers, users, or regulatory and governing bodies? How can accountability be embedded in the design of AI systems?
Compassion in Practical Settings: How can compassionate AI improve patient outcomes in mental health, chronic disease management, and end-of-life care? How can AI systems identify and respond to distressed individuals during emergencies, ensuring that compassionate communication is maintained? How can compassionate AI systems address educational inequities by supporting vulnerable students?
Human-AI Collaboration: Under what conditions can AI systems enhance human compassion during collaboration, such as in healthcare, education, or social services? How can agentic AI systems that exhibit autonomy and technical sophistication balance compassionate decision-making with achieving task-specific objectives?
Downsides of Compassionate AI: What are the potential risks of overemphasizing compassion in AI systems, such as reduced technical sophistication, reduced efficiency, human manipulation, and even cultural insensitivity?
Cultural Contexts of Compassion: How do cultural variations in the understanding and expression of compassion affect the design and use of AI systems?
Behavioral Responses to Compassionate AI: How do users perceive and respond to compassionate AI, and how does it affect user trust, satisfaction, and adoption?
Societal Impact: How can compassionate AI contribute to global challenges, such as poverty, inequality, and climate change? How can AI systems improve healthcare? How can AI systems transform customer service interactions by adapting to user emotions and needs? How can AI systems support employee well-being and foster a more inclusive and empathetic workplace?
Scalability of Compassionate AI: What strategies can ensure that compassionate AI systems scale effectively while maintaining empathy and care? How can autonomy and efficiency in AI systems be aligned with the principles of compassion to create more responsible and effective AI technologies?