Generative AI in Design Practice
Location
New York, Parsons SDM
Date
2025.8~
Focus
Research
Collaborators
Jiwon Pyo
Research Adviser
Cindy Hsiao
Background
Generative AI is rapidly entering design workflows, but adoption has largely focused on individual productivity and speed. As AI becomes the first point of interaction in many tasks, early collaboration, shared sensemaking, and exploratory thinking within design teams risk being compressed or displaced.
Research Objective
This study investigates how GenAI reshapes designers’ cognition, collaboration, and autonomy across the Double Diamond process. The goal is to identify socio-technical risks and opportunities in AI-mediated teamwork and to propose design principles and a system concept that preserve collective exploration, interpretive depth, and human ownership in Hybrid Intelligence environments.
Research
Framework
We grounded our research in The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes (Salas et al., 2017), specifically focusing on the 'Teamwork Process and Emergent States.' We merged these three core mechanisms of Behavior, Cognition, and Affect with the Double Diamond Design Process to create a comprehensive analytical lens. This framework allows us to examine not just the output of design, but the underlying psychological and operational shifts occurring within the team.
Research
Methods
Diary Study
Co-creation Workshop
Professional / SME (Subject-Matter Expert) Interviews
Method 1 - Diary Study
Research Goal
This study aims to explore how designers working in small design teams (2–5 members) experience and adapt to the use of AI tools in collaborative design work.
In particular, it seeks to capture how the integration of AI in individual tasks influences affective, behavioral, and cognitive mechanisms within the team.
Participants
5 design graduate students who have
Active engagement with design practice or education.
Demonstrated use (or avoidance) of AI in daily work.
Willingness to reflect critically on AI’s role in collaboration and creativity.
Questions for
Individual level of AI integration and usage patterns
Primary task domains where AI is applied
Openness and attitude toward exploring new AI tools
Baseline disposition and expectations toward teamwork
Core value perceptions regarding AI collaboration in team settings
Insights
AI as a Cognitive Partner
AI was experienced not just as a tool but as a cognitive partner supporting documentation, research, ideation, and storytelling. Final decisions, however, remained human-led.
Shift in Human Role
Human work shifted from proactive exploration to reactive supervision. High-expertise users internally verified AI outputs, while lower-expertise users relied more on external cross-checking. Some teams showed a “reverse evaluation” pattern, asking AI to critique human ideas.
Affective Impact
AI increased self-efficacy but also triggered imposter syndrome and anxiety as human sense-making appeared to shrink.
Collaboration Dynamics
AI often acted as a “third teammate,” improving information flow but sometimes causing context loss and reducing direct human interaction, pushing teams toward verification-focused workflows.
Method 2 - Design Sprint
Research Goal
This sprint aims to examine how the presence or absence of AI tools shapes teamwork dynamics in small design teams under time constraints.
Specifically, it investigates how AI influences affective, behavioral, and cognitive mechanisms during rapid collaborative design processes, with attention to communication patterns, task distribution, and decision-making.
Participants
Two design student teams (4 members per team) who have:
Working knowledge of the Double Diamond design process
Varying levels of familiarity with AI tools
Active engagement in collaborative design work
Condition setup
Team A (AI-assisted): Free use of AI tools (e.g., ChatGPT, Figma AI, Midjourney)
Team B (Non-AI): No AI tools allowed
Observation points
Role distribution
Decision-making processes
Intensity of interactions
As a result, a range of distinct patterns emerged across the four phases of the design process. Notably, during the ideation phase, conversations within the AI-enabled team often lacked sustained continuity, and participants frequently cross-checked AI-generated content using other AI tools. These and other observations revealed intriguing patterns related to how AI use shapes interaction, verification practices, and collaborative behavior within design teams.
Insights
AI Teams Prioritized Speed, Non-AI Teams Prioritized Continuity
AI-enabled teams moved faster in early stages but showed more fragmented discussion flows, whereas non-AI teams progressed more slowly with stronger conversational continuity.
Verification Load Emerged Only in AI Conditions
Only AI-enabled teams exhibited repeated cross-checking behaviors, introducing an additional verification layer that did not appear in non-AI teams.
Role Distribution Diverged
AI teams developed informal roles around prompting and output checking, while non-AI teams showed more evenly distributed participation during ideation.
Decision Framing Shifted with AI Presence
AI-enabled teams framed decisions around selecting and refining generated options, whereas non-AI teams spent more time jointly constructing ideas from scratch.
Human-to-Human Interaction Was Denser Without AI
Non-AI teams demonstrated more sustained peer-to-peer dialogue, while AI-enabled teams showed intermittent attention shifts between teammates and AI outputs.
Method 3 - Professional / SME Interview
Research Goal
This study aims to understand how AI integration is reshaping collaboration, cognition, and emotional dynamics in real-world design practice.
It focuses on how professional designers conceptualize AI’s role within hybrid human–AI teamwork and how this affects decision-making, trust, and creative ownership.
Participants
12 subject-matter experts who have:
Professional experience in design, UX research, strategy, or innovation roles
Active exposure to AI tools or AI-augmented workflows
At least 2 years of experience in multidisciplinary or client-facing projects
Ongoing involvement in collaborative creative work
Questions For
How designers conceptualize the role of AI in creative and collaborative workflows
How AI influences decision-making, authorship, and ownership of outputs
Shifts in trust, communication, and coordination in hybrid (human + AI) teams
Designers’ evolving mental models of AI as a creative or cognitive partner
Perceived impact of AI on team learning, expertise distribution, and collaboration quality
“ we're running through like 20 interviews in a week like, and it's just like me and one other designer we have to use AI to synthesize what are the common points that's coming out so a lot of the synthesis work afterwards requires a lot of AI ”
Professional Interviewee
Product Designer | BCGX
“If decisions are made based on wrong insights, the accountability falls on me or my team. For research work, accuracy is extremely important. It’s high-stake work.”
Professional Interviewee
UX Researcher | Financial Indistry
Insights
In this study, we analyzed the shift from human-first, shared exploration–based teamwork toward an AI-first, human-react mode of collaboration using three mechanisms from our theoretical framework. As a result, three major shifts were identified.
Shift 1
Trap of Individual Augmentation
Organizations often assume that if each designer becomes more powerful with AI, team performance will naturally improve. However, our findings reveal a blind spot: while individual productivity is optimized, coordination norms are not. Because teams lack shared definitions of “high-stakes” AI use, boundaries remain subjective, leading to inconsistent collaboration and uneven output quality within the same team.
Shift 2
From Explorers to Supervisors
AI is removing not only repetitive work but also much of the cognitive heavy lifting. As a result, designers are spending less time in active exploration and more time reviewing AI outputs. While this accelerates research workflows, it risks weakening the team’s capacity for deep sense-making. Over time, teams may shift from wrestling with complexity to merely validating answers, becoming increasingly shallow in their collective thinking.
Shift 3
The Rise of Solo Loops
Designers are increasingly turning to AI instead of colleagues. While this reduces coordination overhead, it fragments shared reasoning within teams. Collaboration is gradually shifting toward siloed individual workflows supported by personal AI agents. Over time, these “solo loops” risk eroding team trust, alignment, and collective confidence.
Synthesis - Design process map
Purpose
Design Process Map aims to capture surface-level changes in the design process (Discover – Define – Develop – Deliver) after the introduction of GenAI, and to identify the design values that are most at risk.
Structure
The map is structured across multiple analytical axes, including:
Human Roles
AI Roles
Key Changes
Collaboration Patterns
Benefits
Problems
Values at Risk
Human Involvement Level
Together, these dimensions surface not only where GenAI accelerates the design workflow, but also how responsibility, cognition, and collaboration are being redistributed between humans and AI.
Human involvement
level
Human Involvement Level categorizes each stage of the Double Diamond design process into four dimensions—Intention, Interpretation, Craftsmanship, and Knowledge Competence—to indicate the degree and depth of human cognitive engagement.
This axis is intended to visualize where human agency is being preserved, compressed, or displaced within AI-supported workflows.
Opportunity Framing
Co-creation
refers to a mode of collaboration where teams collectively frame problems, generate ideas, and build meaning together through continuous interaction. In this model, design work emerges from shared exploration—ideas are not pre-generated but developed through dialogue, negotiation, and iterative sense-making among team members.
To define where to intervene, we mapped 20 specific design actions against two critical team values : team brain, team hearts.
Quadrant 1 was identified as the critical intervention zone, covering high-value human activities such as insight synthesis, problem definition, opportunity framing, critique, and ideation. Rather than optimizing individual productivity, AI systems must be redesigned to preserve and strengthen collective creativity.
To guide this shift, we propose four core values:
Explore Together: Enable shared discovery, not isolated AI use.
Imagine “What If”: Encourage multiple interpretations and reframing.
Build on Each Other: Amplify collaborative emergence, not solo scaling.
Own Our Story: Support human meaning-making, not auto-generated narratives.
Together, these values call for a fundamental reorientation of AI design toward sustaining human collaboration, interpretation, and exploratory thinking.
Co-evaluation
describes a collaboration pattern where AI generates, structures, and proposes content first, and humans primarily respond by reviewing, selecting, and refining these outputs together. Rather than building ideas collaboratively, teams increasingly gather to judge, align, and validate AI-generated material.
