Human-Centered AI Communication in Co-Creativity: An Initial Framework and Insights

• URL: http://arxiv.org/abs/2505.18385v1
• Date: Fri, 23 May 2025 21:19:37 GMT
• Title: Human-Centered AI Communication in Co-Creativity: An Initial Framework and Insights
• Authors: Jeba Rezwana, Corey Ford,
• Abstract summary: This paper presents the initial design of the Framework for AI Communication (FAICO) for co-creative AI.<n>FAICO presents key aspects of AI communication and their impact on user experience, offering preliminary guidelines for designing human-centered AI communication.<n>Our findings reveal a preference for a human-AI feedback loop over linear communication.
• Score: 2.3020018305241337
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effective communication between AI and humans is essential for successful human-AI co-creation. However, many current co-creative AI systems lack effective communication, which limits their potential for collaboration. This paper presents the initial design of the Framework for AI Communication (FAICO) for co-creative AI, developed through a systematic review of 107 full-length papers. FAICO presents key aspects of AI communication and their impact on user experience, offering preliminary guidelines for designing human-centered AI communication. To improve the framework, we conducted a preliminary study with two focus groups involving skilled individuals in AI, HCI, and design. These sessions sought to understand participants' preferences for AI communication, gather their perceptions of the framework, collect feedback for refinement, and explore its use in co-creative domains like collaborative writing and design. Our findings reveal a preference for a human-AI feedback loop over linear communication and emphasize the importance of context in fostering mutual understanding. Based on these insights, we propose actionable strategies for applying FAICO in practice and future directions, marking the first step toward developing comprehensive guidelines for designing effective human-centered AI communication in co-creation.

Related papers

• When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration [79.69935257008467]
  We introduce Knowledge Integration and Transfer Evaluation (KITE), a conceptual and experimental framework for Human-AI knowledge transfer capabilities.<n>We conduct the first large-scale human study (N=118) explicitly designed to measure it.<n>In our two-phase setup, humans first ideate with an AI on problem-solving strategies, then independently implement solutions, isolating model explanations' influence on human understanding.
  arXiv  Detail & Related papers  (2025-06-05T20:48:16Z)
• Improving User Experience with FAICO: Towards a Framework for AI Communication in Human-AI Co-Creativity [2.3020018305241337]
  This paper introduces our initial design of a Framework for designing AI Communication (FAICO) for co-creative AI.<n>FAICO presents key aspects of AI communication and their impacts on user experience to guide the design of effective AI communication.<n>We show actionable ways to translate our framework into two practical tools: design cards for designers and a configuration tool for users.
  arXiv  Detail & Related papers  (2025-04-03T12:29:53Z)
• Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportunities [148.601430677814]
  This paper presents a comprehensive overview of AI and communication for 6G networks.<n>We first review the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G.<n>The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks.
  arXiv  Detail & Related papers  (2024-12-19T05:36:34Z)
• Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions [101.67121669727354]
  Recent advancements in AI have highlighted the importance of guiding AI systems towards the intended goals, ethical principles, and values of individuals and groups, a concept broadly recognized as alignment. The lack of clarified definitions and scopes of human-AI alignment poses a significant obstacle, hampering collaborative efforts across research domains to achieve this alignment. We introduce a systematic review of over 400 papers published between 2019 and January 2024, spanning multiple domains such as Human-Computer Interaction (HCI), Natural Language Processing (NLP), Machine Learning (ML)
  arXiv  Detail & Related papers  (2024-06-13T16:03:25Z)
• Beyond Prompts: Learning from Human Communication for Enhanced AI Intent Alignment [30.93897332124916]
  We study human strategies for intent specification in human-human communication. This study aims to advance toward a human-centered AI system by bringing together human communication strategies for the design of AI systems.
  arXiv  Detail & Related papers  (2024-05-09T11:10:29Z)
• Untangling Critical Interaction with AI in Students Written Assessment [2.8078480738404]
  Key challenge exists in ensuring that humans are equipped with the required critical thinking and AI literacy skills. This paper provides a first step toward conceptualizing the notion of critical learner interaction with AI. Using both theoretical models and empirical data, our preliminary findings suggest a general lack of Deep interaction with AI during the writing process.
  arXiv  Detail & Related papers  (2024-04-10T12:12:50Z)
• GOMA: Proactive Embodied Cooperative Communication via Goal-Oriented Mental Alignment [72.96949760114575]
  We propose a novel cooperative communication framework, Goal-Oriented Mental Alignment (GOMA)<n>GOMA formulates verbal communication as a planning problem that minimizes the misalignment between parts of agents' mental states that are relevant to the goals.<n>We evaluate our approach against strong baselines in two challenging environments, Overcooked (a multiplayer game) and VirtualHome (a household simulator)
  arXiv  Detail & Related papers  (2024-03-17T03:52:52Z)
• Human-AI collaboration is not very collaborative yet: A taxonomy of interaction patterns in AI-assisted decision making from a systematic review [6.013543974938446]
  Leveraging Artificial Intelligence in decision support systems has disproportionately focused on technological advancements. A human-centered perspective attempts to alleviate this concern by designing AI solutions for seamless integration with existing processes.
  arXiv  Detail & Related papers  (2023-10-30T17:46:38Z)
• Identifying Ethical Issues in AI Partners in Human-AI Co-Creation [0.7614628596146599]
  Human-AI co-creativity involves humans and AI collaborating on a shared creative product as partners. In many existing co-creative systems, users communicate with the AI using buttons or sliders. This paper explores the impact of AI-to-human communication on user perception and engagement in co-creative systems.
  arXiv  Detail & Related papers  (2022-04-15T20:41:54Z)
• On some Foundational Aspects of Human-Centered Artificial Intelligence [52.03866242565846]
  There is no clear definition of what is meant by Human Centered Artificial Intelligence. This paper introduces the term HCAI agent to refer to any physical or software computational agent equipped with AI components. We see the notion of HCAI agent, together with its components and functions, as a way to bridge the technical and non-technical discussions on human-centered AI.
  arXiv  Detail & Related papers  (2021-12-29T09:58:59Z)
• Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir" [76.44130385507894]
  This paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices. Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design.
  arXiv  Detail & Related papers  (2021-11-01T17:57:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.