Search arXiv⌕ Search

arXiv · 2609.34655

AI Tools Adoption across the Double Diamond Workflow: Phase, Mode, and Barriers in Designer Practice

Abstract

Designers are adopting AI faster than the tools built for them can keep up. This survey of 443 designers across 43 countries, among the first phase-disaggregated accounts of its kind, examined reported AI use across the four phases of the Double Diamond workflow (Discover, Define, Develop, Deliver). 79.7 percent reported confirmed AI use in at least one phase, but engagement was typically partial, spanning a mean of 2.89 of 4 phases, with adopters retaining the earliest phases and dropping the latest. Tool choice tracked each phase's dominant activity: conversational tools drove Discover and Define, AI-native image generation took over Develop, and Deliver showed a hybrid profile. AI-native and embedded AI use peaked in different phases, pointing to two distinct modes of human-AI collaboration: generating from scratch versus refining within existing software. Use intensity declined through fewer designers engaging, not scaled-back use. Adopters and non-adopters differed on one dimension: perceived usefulness.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sepideh Tajarmakan, Khashayar Hojjati Emami. 2026-09-28. AI Tools Adoption across the Double Diamond Workflow: Phase, Mode, and Barriers in Designer Practice. https://arxiv.org/abs/2609.34655

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Beyond Judgment: Exploring Large Language Models as Non-Judgmental Support for Maternal Mental Health

In the age of Large Language Models (LLMs), much work has already been done on how LLMs support medication advice and serve as information providers; however, how mothers use these tools for emotional and informational support to avoid social judgment remains underexplored. This study conducted a 10-day mixed-methods exploratory survey ($N=107$) to investigate how mothers use LLMs as a non-judgmental resource for emotional support and regulation, and for situational reassurance. Our findings show that mothers are asking LLMs various questions about childcare to reassure themselves and avoid judgment, particularly around childcare decisions, maternal guilt, and late-night caregiving. Open-ended responses also show that mothers are comfortable with LLMs because they do not have to think about social consequences or judgment. Although mothers use LLMs for quick information or reassurance to avoid judgment, over half of the participants value human warmth more than LLMs; however, a significant minority, especially those in joint families, consider LLMs to avoid human judgment. These findings help understand how LLMs can be framed as low-risk interaction support rather than a replacement for human support, and highlight the role of social context in shaping emotional technology use.

cs.HC↗

Avoiding Social Judgment, Seeking Privacy: Investigating why Mothers Shift from Facebook Groups to Large Language Models

Social media platforms, especially Facebook parenting groups, have long been used as informal support networks for mothers seeking advice and reassurance. However, growing concerns about social judgment, privacy exposure, and unreliable information are changing how mothers seek help. This exploratory mixed-method study examines why mothers are moving from Facebook parenting groups to large language models such as ChatGPT and Gemini. We conducted a cross-sectional online survey of 109 mothers. Results show that 41.3% of participants avoided Facebook parenting groups because they expected judgment from others. This difference was statistically significant across location and family structure. Mothers living in their home country and those in joint families were more likely to avoid Facebook groups. Qualitative findings revealed three themes: social judgment and exposure, LLMs as safe and private spaces, and quick and structured support. Participants described LLMs as immediate, emotionally safe, and reliable alternatives that reduce social risk when asking for help. Rather than replacing human support, LLMs appear to fill emotional and practical gaps within existing support systems. These findings show a change in maternal digital support and highlight the need to design LLM systems that support both information and emotional safety.

cs.HC↗

Learning to Assign Prediction Tasks to Agents with Capacity Constraints

We address the problem of learning to assign prediction tasks to one agent from a set of available agents, including human decision-makers and AI models. We focus on sequential learning of agent expertise and assignment policies where each agent is constrained to handle a fraction of tasks. We provide a general theoretical characterization of this problem in terms of agent capacities, differences in agent expertise, and task context. We then develop a framework of sequential explore-exploit policy-learning algorithms that seek to maximize overall performance. Experimental results over a variety of tabular, image, and text prediction tasks demonstrate systematic gains from our policy-learning algorithms relative to non-contextual baselines across different types of agents, including LLMs and humans.

cs.HC↗