Drag or Traction: Understanding How Designers Appropriate Friction in AI Ideation Outputs
- URL: http://arxiv.org/abs/2603.27550v1
- Date: Sun, 29 Mar 2026 07:06:22 GMT
- Title: Drag or Traction: Understanding How Designers Appropriate Friction in AI Ideation Outputs
- Abstract summary: Seamless AI presents output as a finished, polished product that users consume rather than shape.<n>This risks design fixation: users anchor on AI suggestions rather than generating their own ideas.<n>We propose Generative Friction, which introduces intentional disruptions to AI output.
- Score: 2.3508851646751663
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Seamless AI presents output as a finished, polished product that users consume rather than shape. This risks design fixation: users anchor on AI suggestions rather than generating their own ideas. We propose Generative Friction, which introduces intentional disruptions to AI output (fragmentation, delay, ambiguity) designed to transform it from finished product into semi-finished material, inviting human contribution rather than passive acceptance. In a qualitative study with six designers, we identified the different ways in which designers appropriated the different types of friction: users mined keywords from broken text, used delays as workspace for independent thought, and solved metaphors as creative puzzles. However, this transformation was not universal, motivating the concept of Friction Disposition, a user's propensity to interpret resistance as invitation rather than obstruction. Grounded in tolerance for ambiguity and pre-existing workflow orientation, Friction Disposition emerged as a potential moderator: high-disposition users treated friction as "liberating," while low-disposition users experienced drag. We contribute the concept of Generative Friction as distinct from Protective Friction, with design implications for AI tools that counter fixation while preserving agency.
Related papers
- AI as Friction for Reflection Support in Ideation [12.382214663255953]
We argue that AI in design ideation might be more usefully thought of as a friction agent for reflection rather than as a smoothing agent for output.<n>This reframing opens up a different role for AI in design ideation, one that supports designers in building and carrying rationale rather than substituting for it.
arXiv Detail & Related papers (2026-07-29T12:20:12Z) - From Vibe to Code -- and Back: Lexical Oscillation in the Formation of Design Intent with Generative AI [0.0]
We ask how design intent is formed through situated interaction with AI.<n>Five expert UI/UX designers designed landing-page hero sections with a generative AI tool.<n>We position AI as a non-neutral generative interlocutor and ambiguity as a resource for design judgment.
arXiv Detail & Related papers (2026-07-25T10:00:55Z) - Creativity from Friction: Human-AI Interaction for Exploratory Structural Design [15.328146080270853]
Fields such as structural design and architecture need interactive systems that help users externalise and develop ideas, explore alternatives, and refine partial solutions.<n>This paper shows how structural designers perceive interactive AI systems and how such systems can support design space exploration by reducing repetitive modelling friction while preserving reflective design friction.
arXiv Detail & Related papers (2026-07-08T15:17:23Z) - Exploring Semantic-constrained Adversarial Example with Instruction Uncertainty Reduction [51.50282796099369]
This paper develops a multi-dimensional instruction uncertainty reduction framework to generate semantically constrained adversarial examples.<n>By predicting the language-guided sampling process, the optimization process will be stabilized by the designed ResAdv-DDIM sampler.<n>We realize the reference-free generation of semantically constrained 3D adversarial examples for the first time.
arXiv Detail & Related papers (2025-10-27T04:02:52Z) - ThematicPlane: Bridging Tacit User Intent and Latent Spaces for Image Generation [49.805992099208595]
We introduce ThematicPlane, a system that enables users to navigate and manipulate high-level semantic concepts.<n>This interface bridges the gap between tacit creative intent and system control.
arXiv Detail & Related papers (2025-08-08T06:57:14Z) - Interactive Reasoning: Visualizing and Controlling Chain-of-Thought Reasoning in Large Language Models [54.85405423240165]
We introduce Interactive Reasoning, an interaction design that visualizes chain-of-thought outputs as a hierarchy of topics.<n>We implement interactive reasoning in Hippo, a prototype for AI-assisted decision making in the face of uncertain trade-offs.
arXiv Detail & Related papers (2025-06-30T10:00:43Z) - Intent Tagging: Exploring Micro-Prompting Interactions for Supporting Granular Human-GenAI Co-Creation Workflows [28.404085240725717]
Core challenges include misalignment of AI-generated content with user intentions (intent elicitation and alignment), user uncertainty around how to best communicate their intents to an AI system, and insufficient flexibility of AI systems to support diverse creative (workflow flexibility)<n>Motivated by these challenges, we created IntentTagger: a system for slide creation based on the notion of Intent Tags.<n>Our user study with 12 participants provides insights into the value of flexibly expressing intent across varying levels of ambiguity, meta-intent elicitation, and the benefits and challenges of intent tag-driven intent.
arXiv Detail & Related papers (2025-02-26T01:13:47Z) - Concept Steerers: Leveraging K-Sparse Autoencoders for Test-Time Controllable Generations [5.2956273221301835]
Text-to-image generative models are prone to adversarial attacks and inadvertently generate unsafe, unethical content.<n>We propose a novel framework leveraging k-sparse autoencoders (k-SAEs) to enable efficient and interpretable concept manipulation.<n>Our method yields an improvement of $mathbf20.01%$ in unsafe concept removal, is effective in style manipulation, and is $mathbfsim5$x faster than the current state-of-the-art.
arXiv Detail & Related papers (2025-01-31T11:52:47Z) - AdvAnchor: Enhancing Diffusion Model Unlearning with Adversarial Anchors [61.007590285263376]
Security concerns have driven researchers to unlearn inappropriate concepts through fine-tuning.<n>Recent fine-tuning methods exhibit a considerable performance trade-off between eliminating undesirable concepts and preserving other concepts.<n>We propose AdvAnchor, a novel approach that generates adversarial anchors to alleviate the trade-off issue.
arXiv Detail & Related papers (2024-12-28T04:44:07Z) - RealEra: Semantic-level Concept Erasure via Neighbor-Concept Mining [25.769144703607214]
Concept erasure has been proposed to remove the model's knowledge about protected and inappropriate concepts.
We propose RealEra to address this "concept residue" issue.
We show that RealEra outperforms previous concept erasing methods in terms of superior erasing efficacy, specificity, and generality.
arXiv Detail & Related papers (2024-10-11T17:55:30Z) - PaCE: Parsimonious Concept Engineering for Large Language Models [57.740055563035256]
Parsimonious Concept Engineering (PaCE) is a novel activation engineering framework for alignment.
We construct a large-scale concept dictionary in the activation space, in which each atom corresponds to a semantic concept.
We show that PaCE achieves state-of-the-art alignment performance while maintaining linguistic capabilities.
arXiv Detail & Related papers (2024-06-06T17:59:10Z) - Exploring a Behavioral Model of "Positive Friction" in Human-AI
Interaction [1.8673970128645236]
This paper first proposes a "positive friction" model that can help characterize how friction is currently beneficial in user and developer experiences with AI.
It then explores this model in the context of AI users and developers by proposing the value of taking a hybrid "AI+human" lens.
arXiv Detail & Related papers (2024-02-15T03:39:55Z) - Explainability Pitfalls: Beyond Dark Patterns in Explainable AI [9.020313208419378]
We introduce explainability pitfalls (EPs), unanticipated negative downstream effects from AI explanations manifesting even when there is no intention to manipulate users.
EPs are different from, yet related to, dark patterns, which are intentionally deceptive practices.
We propose proactive and preventative strategies to address EPs at three interconnected levels.
arXiv Detail & Related papers (2021-09-26T03:05:50Z) - Congestion-aware Multi-agent Trajectory Prediction for Collision
Avoidance [110.63037190641414]
We propose to learn congestion patterns explicitly and devise a novel "Sense--Learn--Reason--Predict" framework.
By decomposing the learning phases into two stages, a "student" can learn contextual cues from a "teacher" while generating collision-free trajectories.
In experiments, we demonstrate that the proposed model is able to generate collision-free trajectory predictions in a synthetic dataset.
arXiv Detail & Related papers (2021-03-26T02:42:33Z)
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.