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Co-Creative Framework for Interaction Design (COFI): On the left (a) Components of Interaction between the collaborators, On the right (b) Components of Interaction with the Shared Product.
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Human-AI co-creativity involves both humans and AI collaborating on a shared creative product as partners. In a creative collaboration, interaction dynamics, such as turn-taking, contribution type, and communication, are the driving forces of the co-creative process. Therefore the interaction model is a critical and essential component for effectiv...
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There is increasing evidence that land use and land cover (LULC) change interacts with climate change to shape biodiversity dynamics. The prevailing hypothesis suggests that generalist species have an advantage in novel climatic and land cover conditions, while specialists are expected to be more sensitive to both stressors (gener...
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... We additionally explored the creativitysupporting potential of digital tools for overarching project and time management (see the additional online materials on the OSF), which appears relevant to all stages. What is more, one could also explore additional features (e.g., mode of interaction with a tool; Rezwana & Maher, 2023) or different levels of user involvement (e.g., active vs. passive use; . The DCS framework offers a meaningful structure with distinguishable stages and features but at the level of specific tools, certain aspects can co-occur (e.g., functions to publish and potential to interact with others) and are thus not fully independent. ...
While digitalization is thought to have a major impact on creativity, little is known about how digital tools support the creative process across specific stages and domains. Building on a new digital creativity support framework that considers the role of digital tool features (i.e., functions, content, and community) across separable stages in the creative process (i.e., ideation, production, presentation), two survey studies (N1 = 497, N2 = 1,200) examined the relevance and type of digital tools used across eight creative domains. We found that while digital tools pervade all stages in the creative process, there are notable differences in the extent and type of feature support across domains (e.g., handicraft vs. science) and skill levels (e.g., Little-c vs. Pro-c). The reported tools revealed the high relevance of social media for creative behaviors, next to domain-specific tools. We discuss how creative behavior gets transformed by digital tools and implications for the research community.
... Taking the AI-TAM as a point of departure, we added the process variables of user control, AI output transparency, perceived partnership and replaced the output variables collaborative intention and behavioral intention with willingness to train, willingness to co-develop and willingness to adopt. These variables were inspired by fundamental principles of HI (mutual learning), HCAI (pursuit of high levels of automation and control simultaneously), and IML (continuous interactivity) as well as the Co-Creative Framework for Interaction Design (COFI) which emphasizes the importance of establishing a partnership between end users and AI support tool (Rezwana and Maher, 2022). ...
... In an effort to combat algorithmic overconfidence (Lacroux and Martin-Lacroux, 2022), future designs could display the GD assistant's level of confidence in the generated design as a means of the algorithm communicating to the end user. Bi-directional communication has also been shown to increase perceived partnership (Rezwana and Maher, 2022). ...
... Perceived partnership is one of the new constructs we added to the HI-TAM given its importance in human-AI co-creative systems (Rezwana and Maher, 2022). Our HI-TAM shows quantitative correlations and qualitative co-occurrences between perceived partnership and perceived usefulness. ...
The Hybrid Intelligence Technology Acceptance Model (HI-TAM) presented in this paper offers a novel framework for training and adopting generative design (GD) assistants, facilitating co-creation between human experts and AI systems. Despite the promising outcomes of GD, such as augmented human cognition and highly creative design products, challenges remain in the perception, adoption, and sustained collaboration with AI, especially in creative design industries where personalized and specialized assistance is crucial for individual style and expression. In this two-study paper, we present a holistic hybrid intelligence (HI) approach for individual experts to train and personalize their GD assistants on-the-fly. Culminating in the HI-TAM, our contribution to human-AI interaction is 4-fold including (i) domain-specific suitability of the HI approach for real-world application design, (ii) a programmable common language that facilitates the clear communication of expert design goals to the generative algorithm, (iii) a human-centered continual training loop that seamlessly integrates AI training into the expert's workflow, (iv) a hybrid intelligence narrative that encourages the psychological willingness to invest time and effort in training a virtual assistant. This approach facilitates individuals' direct communication of design objectives to AI and fosters a psychologically safe environment for adopting, training, and improving AI systems without the fear of job-replacement. To demonstrate the suitability of HI-TAM, in Study 1 we surveyed 41 architectural professionals to identify the most preferred workflow scenario for an HI approach. In Study 2, we used mixed methods to empirically evaluate this approach with 8 architectural professionals, who individually co-created floor plan layouts of office buildings with a GD assistant through the lens of HI-TAM. Our results suggest that the HI-TAM enables professionals, even non-technical ones, to adopt and trust AI-enhanced co-creative tools.
... The presence of interactive music ecosystems has recently sparked interest towards the interplay between human performers and artifacts, fostering reflections on how musicians and algorithms co-create in a realtime scenario [Waters, 2007]. As such, the research scrutiny has shifted from individual focus to the interactions between musicians and autonomous agents [Rezwana & Maher, 2023]. Within interactive ecologies, performers can explore new forms of creative expression by engaging with machines in a collaborative or co-mediated fashion. ...
... In the last two decades, research on performance ecologies [Gurevich & Treviño, 2007] highlight the intertwined relationships between actors and artifacts. In this context, several frameworks for designing interactions [Rezwana & Maher, 2023] or analysing ecologies [Masu et al., 2019] have been recently proposed. ...
In this paper, we present an interactive performative ecosystem for two musicians, based on neural networks which estimate parameters for a virtual synthe-siser mimicking the performer's actions. In line with the conference's keyword, we designed the interaction allowing mutual sonic contamination between the musicians. We evaluated the proposed system with four different duets, asking the participants to provide insights in relation to each other and to the system itself .
... Some, such as Murray (2024) think that AI is no more than a tool, something like a paintbrush or musical instrument, through which human artists extend their creative capability. On the other hand, many scholars, such as Rezwana and Maher (2023), highlight the complexity of the matter, where AI is well on its way to co-creator status, as creative decisions eventually come to be made independently by the AI itself. This is further complicated by the question of originality. ...
This paper considers the role of artificial intelligence as an emerging creative collaborator within artistic practices through the frame of speculative design. As technology continues to reshape the creative landscape, AI is increasingly moving from a tool to a co-creative partner in the artistic process. Speculative design is used in this paper to imagine and contrast possible future scenarios in which AI will play a significant role in the creation of art, to explore emerging opportunities and challenges from this integration. The article, guided by three key research questions, investigates how human artists perceive the role of AI in art, the key contributions of AI in collaborative settings, and how speculative design might help clarify the outcomes of AI-assisted versus traditional artistic creation. A comprehensive review of the literature underlines the current state of AI, concerning art forms in visual arts, music, and literature. This, however, must be availed with the gap about research at large in terms of authorship, originality, and ethical implications. The project shows, through speculative scenarios and interviews with practising artists, that while AI holds the potential to extend human creativity through new tools and techniques, several ethical issues regarding authorship and ownership and the possible suppression of human creativity arise. The discussion emphasizes the importance of preserving human agency and the need to develop ethics concerning the use of AI in the arts. In all, while AI affords exciting new opportunities in the realm of collaborative art, great care must be exercised in its integration.
... He notes that traditional educational methods and cognitive theories are increasingly inadequate in meeting modern needs. Several studies (Fragiadakis et al. 2024;Rezwana and Maher 2023) systematically review and design various forms of human-AI collaboration, proposing new evaluation standards. This work further demonstrates the inadequacy of traditional research frameworks in addressing the multidimensional complexities of human-AI collaboration. ...
This paper explores the transformative role of artificial intelligence (AI) in enhancing scientific research, particularly in the fields of brain science and social sciences. We analyze the fundamental aspects of human research and argue that it is high time for researchers to transition to human-AI joint research. Building upon this foundation, we propose two innovative research paradigms of human-AI joint research: "AI-Brain Science Research Paradigm" and "AI-Social Sciences Research Paradigm". In these paradigms, we introduce three human-AI collaboration models: AI as a research tool (ART), AI as a research assistant (ARA), and AI as a research participant (ARP). Furthermore, we outline the methods for conducting human-AI joint research. This paper seeks to redefine the collaborative interactions between human researchers and AI system, setting the stage for future research directions and sparking innovation in this interdisciplinary field.
... Recent research and industry applications have leveraged the conversational and reasoning capabilities of LLMs to create problem-solving tools [33,55,67,87,92], educational assistants [48], and novel search interfaces [50]. In the industry, many companies deploy AI agents on social media to assist with branding and customer service [39,43,90], while others embed agents in their applications for assisting writing [26], brainstorming [65], or gaming [73]. ...
Multi-agent systems - systems with multiple independent AI agents working together to achieve a common goal - are becoming increasingly prevalent in daily life. Drawing inspiration from the phenomenon of human group social influence, we investigate whether a group of AI agents can create social pressure on users to agree with them, potentially changing their stance on a topic. We conducted a study in which participants discussed social issues with either a single or multiple AI agents, and where the agents either agreed or disagreed with the user's stance on the topic. We found that conversing with multiple agents (holding conversation content constant) increased the social pressure felt by participants, and caused a greater shift in opinion towards the agents' stances on each topic. Our study shows the potential advantages of multi-agent systems over single-agent platforms in causing opinion change. We discuss design implications for possible multi-agent systems that promote social good, as well as the potential for malicious actors to use these systems to manipulate public opinion.
... Additionally, Vision-Language Models (VLMs) possess the ability to interpret videos with high detail, reducing the need for extensive human effort [7]. These advancements have the potential to assist designers in overcoming challenges associated with generating efficient and effective ideas, particularly when faced with prolonged video viewing and limited design experience [34,50]. As such, this paper explores an approach that combines a customized VLM and LLM (DesignMinds) to enhance the "watch-summarize-ideate" process in VBD tasks through designer-AI co-ideation. ...
Ideation is a critical component of video-based design (VBD), where videos serve as the primary medium for design exploration and inspiration. The emergence of generative AI offers considerable potential to enhance this process by streamlining video analysis and facilitating idea generation. In this paper, we present DesignMinds, a prototype that integrates a state-of-the-art Vision-Language Model (VLM) with a context-enhanced Large Language Model (LLM) to support ideation in VBD. To evaluate DesignMinds, we conducted a between-subject study with 35 design practitioners, comparing its performance to a baseline condition. Our results demonstrate that DesignMinds significantly enhances the flexibility and originality of ideation, while also increasing task engagement. Importantly, the introduction of this technology did not negatively impact user experience, technology acceptance, or usability.
... The Symbiotic mode promotes a balanced partnership, with human and AI systems sharing decision-making and complementing each other's strengths. This setup enables mutual feedback and close collaboration, ideal for tasks requiring both AI's computational power and human intuition, such as creative co-production (Rezwana and Maher, 2023). ...
As Artificial Intelligence (AI) systems increasingly influence decision-making across various fields, the need to attribute responsibility for undesirable outcomes has become essential, though complicated by the complex interplay between humans and AI. Existing attribution methods based on actual causality and Shapley values tend to disproportionately blame agents who contribute more to an outcome and rely on real-world measures of blameworthiness that may misalign with responsible AI standards. This paper presents a causal framework using Structural Causal Models (SCMs) to systematically attribute responsibility in human-AI systems, measuring overall blameworthiness while employing counterfactual reasoning to account for agents' expected epistemic levels. Two case studies illustrate the framework's adaptability in diverse human-AI collaboration scenarios.
... Ning et al. [78] as a collaborative music editing tool investigated the human dominated enhancement and creation process, improving the collaborative creation efficiency. Rezwana et al. [88] introduced a co-creative interaction design framework, which identified pleasureoriented, improvisational, advisory AI agent. They found most systems lacked effective communication channels. ...
... Notably, visual artists exhibited different collaboration patterns from designers and human-centric interaction researchers. The past literature illustrated the system facilitating co-creation in interaction design [88]. However, due to the aesthetic value of art works, these systems were not directly applicable to artists. ...
The rise of Generative Artificial Intelligence (G-AI) has transformed the creative arts landscape by producing novel artwork, whereas in the same time raising ethical concerns. While previous studies have addressed these concerns from technical and societal viewpoints, there is a lack of discussion from an HCI perspective, especially considering the community's perception and the visual artists as human factors. Our study investigates G-AI's impact on visual artists and their relationship with GAI to inform HCI research. We conducted semi-structured interviews with 20 novice visual artists from an art college in the university with G-AI courses and practices. Our findings reveal (1) the mis-conception and the evolving adoption of visual artists, (2) the miscellaneous opinions of the society on visual artists' creative work, and (3) the co-existence of confrontation and collaboration between visual artists and G-AI. We explore future HCI research opportunities to address these issues.
... Recent work has focused on developing interfaces and frameworks that enable co-creation between users and AI [25] [12] [20]. However, at the end of 2022 and the beginning of 2023, OpenAI's ChatGPT and similar services made GenAI accessible to everyone through user-friendly interfaces. ...
The creative industries are at a significant turning point, blurring the lines between human innovation and machine efficiency due to the advent of AI tools for design. This research addresses the design community’s reaction to generative AI’s rapid ascent. Involving both professionals and students, this paper uses a survey (n=49), focus groups (n=6), and interviews (n=6), to explore the perspectives on AI’s role in creative workflows, revealing a mix of enthusiasm and dread for its potential to enhance innovation and concerns about its fit in traditionally human realms. The prevailing view frames AI as a partner or co-worker in design, yet calls for ethical considerations and transparent practices in its applications. This exploration aims to understand current designer attitudes and presents an initial framework for the adoption of AI tools in the designer’s workflow.
