Exploring the Potential of Metacognitive Support Agents for Human-AI Co-Creation
- URL: http://arxiv.org/abs/2506.12879v1
- Date: Sun, 15 Jun 2025 15:09:37 GMT
- Title: Exploring the Potential of Metacognitive Support Agents for Human-AI Co-Creation
- Authors: Frederic Gmeiner, Kaitao Luo, Ye Wang, Kenneth Holstein, Nikolas Martelaro,
- Abstract summary: We envision novel metacognitive support agents that assist designers in working more reflectively with GenAI.<n>We conducted exploratory prototyping through a Wizard of Oz elicitation study with 20 mechanical designers probing multiple metacognitive support strategies.<n>We found that agent-supported users created more feasible designs than non-supported users, with differing impacts between support strategies.
- Score: 15.100530378569866
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Despite the potential of generative AI (GenAI) design tools to enhance design processes, professionals often struggle to integrate AI into their workflows. Fundamental cognitive challenges include the need to specify all design criteria as distinct parameters upfront (intent formulation) and designers' reduced cognitive involvement in the design process due to cognitive offloading, which can lead to insufficient problem exploration, underspecification, and limited ability to evaluate outcomes. Motivated by these challenges, we envision novel metacognitive support agents that assist designers in working more reflectively with GenAI. To explore this vision, we conducted exploratory prototyping through a Wizard of Oz elicitation study with 20 mechanical designers probing multiple metacognitive support strategies. We found that agent-supported users created more feasible designs than non-supported users, with differing impacts between support strategies. Based on these findings, we discuss opportunities and tradeoffs of metacognitive support agents and considerations for future AI-based design tools.
Related papers
- Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training [86.70255651945602]
We introduce a novel inference-time steering methodology called Reinforcing Cognitive Experts (RICE)<n>RICE aims to improve reasoning performance without additional training or complexs.<n> Empirical evaluations with leading MoE-based LRMs demonstrate noticeable and consistent improvements in reasoning accuracy, cognitive efficiency, and cross-domain generalization.
arXiv Detail & Related papers (2025-05-20T17:59:16Z) - AI Automatons: AI Systems Intended to Imitate Humans [54.19152688545896]
There is a growing proliferation of AI systems designed to mimic people's behavior, work, abilities, likenesses, or humanness.<n>The research, design, deployment, and availability of such AI systems have prompted growing concerns about a wide range of possible legal, ethical, and other social impacts.
arXiv Detail & Related papers (2025-03-04T03:55:38Z) - The Design Space of Recent AI-assisted Research Tools for Ideation, Sensemaking, and Scientific Creativity [2.0558118968162673]
Generative AI (GenAI) tools are expanding the scope and capability of automation in knowledge work such as academic research.<n>While promising for augmenting cognition and streamlining processes, AI-assisted research tools may also increase automation bias and hinder critical thinking.
arXiv Detail & Related papers (2025-02-22T16:42:11Z) - Empowering Clients: Transformation of Design Processes Due to Generative AI [1.4003044924094596]
The study reveals that AI can disrupt the ideation phase by enabling clients to engage in the design process through rapid visualization of their own ideas.
Our study shows that while AI can provide valuable feedback on designs, it might fail to generate such designs, allowing for interesting connections to foundations in computer science.
Our study also reveals that there is uncertainty among architects about the interpretative sovereignty of architecture and loss of meaning and identity when AI increasingly takes over authorship in the design process.
arXiv Detail & Related papers (2024-11-22T16:48:15Z) - Data Analysis in the Era of Generative AI [56.44807642944589]
This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges.
We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow.
We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
arXiv Detail & Related papers (2024-09-27T06:31:03Z) - Responding to Generative AI Technologies with Research-through-Design: The Ryelands AI Lab as an Exploratory Study [6.028558240668647]
We partner with a primary school to develop a constructionist curriculum centered on students interacting with a generative AI technology.
We provide a detailed account of the design of and outputs from the curriculum and learning materials, finding centrally that the reflexive and prolonged hands-on' approach led to a co-development of students' practical and critical competencies.
arXiv Detail & Related papers (2024-05-07T21:34:10Z) - Prototyping with Prompts: Emerging Approaches and Challenges in Generative AI Design for Collaborative Software Teams [2.237039275844699]
Generative AI models are increasingly being integrated into human task, enabling the production of expressive content.<n>Unlike traditional human-AI design methods, the new approach to designing generative capabilities focuses heavily on prompt engineering strategies.<n>Our findings highlight emerging practices and role shifts in AI system prototyping among multistakeholder teams.
arXiv Detail & Related papers (2024-02-27T17:56:10Z) - Exploring Challenges and Opportunities to Support Designers in Learning
to Co-create with AI-based Manufacturing Design Tools [31.685493295306387]
AI-based design tools are proliferating in professional software to assist engineering and industrial designers in complex manufacturing and design tasks.
These tools take on more agentic roles than traditional computer-aided design tools and are often portrayed as "co-creators"
To date, we know little about how engineering designers learn to work with AI-based design tools.
arXiv Detail & Related papers (2023-03-01T02:57:05Z) - Design Space Exploration and Explanation via Conditional Variational
Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges [52.77024349608834]
This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE)
The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland.
arXiv Detail & Related papers (2022-11-29T17:28:31Z) - Seamful XAI: Operationalizing Seamful Design in Explainable AI [59.89011292395202]
Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps.
We propose that seamful design can foster AI explainability by revealing sociotechnical and infrastructural mismatches.
We explore this process with 43 AI practitioners and real end-users.
arXiv Detail & Related papers (2022-11-12T21:54:05Z) - Investigating Positive and Negative Qualities of Human-in-the-Loop
Optimization for Designing Interaction Techniques [55.492211642128446]
Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives.
Model-based computational design algorithms assist designers by generating design examples during design.
Black box methods for assistance, on the other hand, can work with any design problem.
arXiv Detail & Related papers (2022-04-15T20:40:43Z)
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.