Synergizing Human-AI Agency: A Guide of 23 Heuristics for Service Co-Creation with LLM-Based Agents
Project Overview
The document explores the integration of generative AI, particularly Large Language Models (LLMs), in education and public services, with a focus on libraries. It highlights the collaborative nature of human-AI interactions and the challenges faced by non-experts in effectively utilizing AI technologies. A key component is the introduction of CoAGent, a tool aimed at enhancing service co-creation with LLMs, which underscores the importance of clearly defining roles and responsibilities between humans and AI. The findings reveal 23 heuristics that facilitate effective collaboration and emphasize the recognition of AI as an active participant in service delivery. Overall, the document underscores the transformative potential of generative AI in educational contexts, advocating for ethical guidelines and best practices to maximize its benefits while addressing the challenges associated with its implementation. Through these insights, it points to the necessity of fostering a cooperative environment where both educators and AI can work together to enhance learning experiences, ultimately leading to improved educational outcomes.
Key Applications
CoAGent - a three-module design tool for service co-creation with LLM-based agents.
Context: Public libraries, serving as community hubs for education and digital literacy.
Implementation: A participatory design process involving domain experts from public libraries to co-create and refine the tool.
Outcomes: Enhanced understanding of AI's capabilities, improved service delivery, and the establishment of 23 heuristics for collaboration with AI.
Challenges: Knowledge disparities between AI and non-AI experts, issues of control over AI outputs, and the need for continuous updates to AI systems.
Implementation Barriers
Knowledge Gap
Non-AI experts face challenges in understanding and utilizing AI tools due to limited technical expertise.
Proposed Solutions: Incorporate training programs and resources to enhance understanding, and use participatory design processes to involve users in AI development.
Ethical Concerns
Lack of ethical guidelines for AI deployment in public services may lead to mistrust and quality issues.
Proposed Solutions: Develop clear ethical design principles and guidelines to ensure responsible AI integration in public services.
Project Team
Qingxiao Zheng
Researcher
Zhongwei Xu
Researcher
Abhinav Choudhry
Researcher
Yuting Chen
Researcher
Yongming Li
Researcher
Yun Huang
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Qingxiao Zheng, Zhongwei Xu, Abhinav Choudhry, Yuting Chen, Yongming Li, Yun Huang
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai