This empirical study serves as a primer for interested service providers to
determine if and how Large Language Models (LLMs) technology will be integrated
for their practitioners and the broader community. We investigate the mutual
learning journey of non-AI experts and AI through CoAGent, a service
co-creation tool with LLM-based agents. Engaging in a three-stage participatory
design processes, we work with with 23 domain experts from public libraries
across the U.S., uncovering their fundamental challenges of integrating AI into
human workflows. Our findings provide 23 actionable "heuristics for service
co-creation with AI", highlighting the nuanced shared responsibilities between
humans and AI. We further exemplar 9 foundational agency aspects for AI,
emphasizing essentials like ownership, fair treatment, and freedom of
expression. Our innovative approach enriches the participatory design model by
incorporating AI as crucial stakeholders and utilizing AI-AI interaction to
identify blind spots. Collectively, these insights pave the way for synergistic
and ethical human-AI co-creation in service contexts, preparing for workforce
ecosystems where AI coexists.

该研究通过 CoAGent 这款基于 LLM 的工具，与 23 位公共图书馆领域专家合作进行三阶段的参与式设计过程，揭示了整合人工智能到人类工作流程中的基本挑战，并提出了可行的 “与人工智能共同创造服务的启示”，强调人工智能和人类之间共享的责任，为 AI 在参与式设计模型中的应用开辟了新途径，为服务环境中的人工智能与人类相互协作创作的产生奠定了基础。

人工智能与人类协同创造的 23 个启发法指南

Synergizing Human-AI Agency: A Guide of 23 Heuristics for Service  Co-Creation with LLM-Based Agents

Generative models are a powerful tool in AI for material discovery. We are
designing a software framework that supports a human-AI co-creation process to
accelerate finding replacements for the ``forever chemicals''-- chemicals that
enable our modern lives, but are harmful to the environment and the human
health. Our approach combines AI capabilities with the domain-specific tacit
knowledge of subject matter experts to accelerate the material discovery. Our
co-creation process starts with the interaction between the subject matter
experts and a generative model that can generate new molecule designs. In this
position paper, we discuss our hypothesis that these subject matter experts can
benefit from a more iterative interaction with the generative model, asking for
smaller samples and ``guiding'' the exploration of the discovery space with
their knowledge.

设计支持人工智能辅助协作的软件框架，加速发现替代环境和人类健康有害的化学物质 ——“永久化学物质”。通过领域专家的知识与人工智能技术相结合，使用生成模型生成新的分子设计。研究表明，领域专家可以通过更迭代的方式与生成模型互动，指导探索发现空间，并取小样本以获益。

人工智能与人类协作的方式寻找永久性化学物质的替代品

Human-AI Co-Creation Approach to Find Forever Chemicals Replacements

Generative AI technologies are growing in power, utility, and use. As
generative technologies are being incorporated into mainstream applications,
there is a need for guidance on how to design those applications to foster
productive and safe use. Based on recent research on human-AI co-creation
within the HCI and AI communities, we present a set of seven principles for the
design of generative AI applications. These principles are grounded in an
environment of generative variability. Six principles are focused on designing
for characteristics of generative AI: multiple outcomes & imperfection;
exploration & control; and mental models & explanations. In addition, we urge
designers to design against potential harms that may be caused by a generative
model's hazardous output, misuse, or potential for human displacement. We
anticipate these principles to usefully inform design decisions made in the
creation of novel human-AI applications, and we invite the community to apply,
revise, and extend these principles to their own work.

介绍了基于 HCI 和 AI 社区的人工智能协作的最新研究，提出了七个面向多个结果和潜在危害的生成式人工智能设计原则。