Drag or Traction: Understanding How Designers Appropriate Friction in AI Ideation Outputs

Drag or T raction: Understanding How Designers Appropriate Friction in AI Ideation Outputs A. Baki Kocaballi University of T echnology Sydney , Australia Joseph Kizana University of T echnology Sydney , Australia Sharon Stein University of British Columbia, Canada Simon Buckingham Shum University of Technology Sydney, Australia Abstract Seamless AI presents output as a ! nished, polished product that users consume rather than shape. This risks design ! xation: users anchor on AI suggestions rather than generating their own ideas. W e propose Generative Friction —intentional disruptions to AI out- put (fragmentation, delay , ambiguity) designed to transform it from ! nished product into semi- ! nishe d material, inviting human con- tribution rather than passive acceptance. In a qualitative study with six designers, we identi ! ed the di " erent ways in which de- signers appropriated the di " erent types of friction: users mined keywords from broken text, used delays as workspace for indepen- dent 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 ambi- guity and pre-existing work # ow orientation, Friction Disposition emerged as a potential moderator: high-disposition users treated friction as “liberating, ” while low-disposition users experience d drag. W e contribute the concept of Generative Friction as distinct from Protective Friction, with design implications for AI tools that counter ! xation while preser ving agency . CCS Concepts • Human-centered computing → Interactive systems and tools ; Natural language interfaces . Ke ywords generative AI, friction, ideation, human-AI collaboration, design ! xation, appropriation A CM Reference Format: A. Baki Kocaballi, Joseph Kizana, Sharon Stein, and Simon Buckingham Shum. 2026. Drag or T raction: Understanding How Designers Appropriate Friction in AI Ideation Outputs. In Proceedings of CHI Conference on Hu- man Factors in Computing Systems W orkshop on T ools for Thought (CHI’26 W orkshop on T ools for Thought) . ACM, New Y ork, N Y , USA, 6 pages. 1 Introduction Modern generative AI to ols ar e optimized for seamlessness —delivering polished, # uent responses instantly . While e $ cient in some con- texts, this presents AI output as a ! nishe d product to b e consume d rather than material to be shaped, risking design ! xation [ 25 ] and reduced ownership [15]. In design, friction is often treated as drag to be eliminated. But constraints can also be generative: Manning’s concept of enabling CHI’26 W orkshop on T ools for Thought, Barcelona, Spain 2026. constraints describ es limitations that create conditions for novelty to emerge [ 20 ]. Friction in human-AI collaboration can operate similarly—it can be drag (wasted e " ort) or traction (productive resistance that enables agency). W e propose the concept of Generative Friction : intentional dis- ruptions that degrade the seamlessness of AI output to invite human contribution. By fragmenting text (Physical friction), delaying deliv- ery (T emporal friction), or obscuring meaning (Semantic friction), we aim to break the illusion of completeness. While Protective Fric- tion adds barriers to verify accuracy in high-stakes tasks [ 4 ]—asking “Should I accept this?”—Generative Friction disrupts presentation to ask “What can I make from this?” This distinction is critical: creative ideation is low-stakes, where the risk is not accepting a wrong answer but ! xating on an idea prematurely . W e draw on Dourish’s [ 13 ] concept of appropriation, the ac- tive process of adapting technology to one’s own purposes, and Schön’s [ 23 ] notion of design materials that “talk back. ” Where seamless AI o " ers no resistance to prompt re # ection, generative friction transforms AI output into material that invites appropria- tion. W e conducted a qualitative study with six designers using an AI ideation tool under seamless and frictional conditions, asking: How does intentional friction transform the way designers appropriate AI-generated outputs? W e found that friction transforme d the status of AI output from a ! nished product to accept into semi- ! nished material requiring further human engagement. Participants mine d keywords from broken text, front-loaded their own ideas during delays, and translate d metaphors into features. However , this trans- formation was not universal. W e conceptualise Friction Disp osi- tion —a user’s propensity to interpret resistance as invitation rather than obstruction—as a potential moderator . W e contribute: (1) the concept of Generative Friction for ideation; (2) empirical evidence of appropriation strategies; and (3) a preliminar y model of Friction Disposition. 2 Related W ork 2.1 Friction in HCI and AI HCI has moved from treating friction as drag to be minimised [ 21 ] to recognising its productive potential through Slow T echnology’s reframing of delay as re # ective time [ 17 ], Seamful Design’s argu- ment that exposing system seams emp owers users [ 7 ], and Cox et al. ’s operationalisation of “microboundaries” for mindfulness [ 10 ]. In the AI domain, this lineage continues with Cabitza et al. ’s pro- grammed ine $ ciencies that stimulate cognitive engagement [ 6 ], and Chen & Schmidt’s [ 8 ] b ehavioral model of Positive Friction. W e build on this trajectory , introducing Generative Friction as friction CHI’26 W orkshop on T ools for Thought, April 2026, Barcelona, Spain A. Baki Kocaballi, Joseph Kizana, Sharon Stein, and Simon Buckingham Shum designed to degrade AI output seamlessness and stimulate user contribution. 2.2 Design Fixation and Appropriation Seamless AI risks automation complacency [ 22 ] and design ! xation: users anchor on AI-generated ideas, constraining divergent think- ing. W adinambiarachchi et al. [ 25 ] found that AI during ideation increased ! xation and propose d “partially completed or blurred outputs” as countermeasure. Prior work supp orts this direction: ambiguous, incomplete or misaligned AI outputs can work as gener- ative resources [ 12 ], and partial photographs of products can reduce design ! xation [ 9 ]. Cognitive Forcing Functions [ 4 ] interrupt fast, intuitive acceptance (System 1) and trigger deliberate evaluation (System 2) [ 18 ]. Seamful XAI [ 14 ] extends this to explainability , arguing that revealing AI seams increases agency . However , this work predominantly focuses on high-stakes do- mains such as aviation, medicine, decision-making, where the goal is preventing costly errors. Creative ideation is low-stakes : the risk is not accepting a wrong answer , but ! xating on an idea prema- turely without exploring a larger space of possibilities. Friction in ideation should therefore stimulate elab oration rather than verify accuracy . As introduced earlier , Dourish’s [ 13 ] appropriation framework and Schön’s [ 23 ] concept of re # ective resistance provide our theo- retical lens. Where seamless AI presents output as ! nished—leaving little “back-talk”—our study examines how generative friction in- creases appropriability by inviting users to work with the output rather than accept it. 3 Method W e conducted a qualitative, within-subjects study using think-aloud protocols to compare unrestricted (seamless) and restricted (friction) AI-assisted ideation tasks. Each session began with a short pre-task interview , followed by the ideation tasks. Low risk ethics approval was received, and sessions lasted 45–60 minutes. 3.1 Participants W e recruite d 6 participants (3 female, 3 male; ages 22–30) with design backgrounds (T able 1). Three were students studying Master of Design and three were working professionals (T able 2). T able 1: Participant Demographics ID Age/Gender Role GenAI Use P1 22M Postgrad Student Occasional P2 23M Postgrad Student Occasional P3 23F Postgrad Student Regular P4 24F Product designer (2 yrs) Regular P5 27F Product designer (5 yrs) Regular P6 30M UX/UI designer (7 yrs) Regular Because our friction conditions deliberately introduce ambigu- ity into AI output, we anticipated that participants’ tolerance for ambiguity—their capacity to engage productively with uncertain or incomplete information [ 5 ], a trait positively associated with cre- ative b ehaviour [ 26 ]—could in # uence their responses. W e therefore assessed each participant’s orientation toward AI output and ambi- guity qualitatively , base d on pre-task inter view statements prior to any exposure to friction conditions. Future work will complement this qualitative coding with a standardised ambiguity tolerance scale (Table 2). T able 2: Participant Predispositions to ward AI and Ambiguity ID Orientation Representative Quote P4 Low T olerance “I just don’t se e the point of staring at a blank page anymore. ” P6 Low T olerance “I use it when I just really can’t b e bothered. ” P1 High T olerance “It shouldn’t do everything for you. ” P2 High T olerance “There’s nothing challenging your thinking [with AI]. ” P3 High T olerance “I avoid using it before I’ve sketche d something myself. ” P5 High T olerance “Creativity comes from thinking out- side the box. ” 3.2 System and Conditions W e developed SP ARK v1 , a custom AI ideation tool (GPT -4o). The underlying mo del and system prompt were constant; the presenta- tion of output varie d across four conditions: (1) Seamless (Baseline): Complete AI output displayed nor- mally without any inter vention. (2) Physical Friction (Fragmentation): AI Output visually broken— every se cond word of the full idea text is hidden, causing fragmented text blo cks. Designe d to the dis ! uency e " ect [1]. (3) T emporal Friction (Delay): The words of the full AI output are displayed one by one gradually over time. Designe d to the incubation e " ect [24]. (4) Semantic Friction (Ambiguity): AI Output was cr yptic involving a metaphorical description of the idea. Designed to abstract stimuli [16] and semantic ambiguity [12] 3.3 Procedure and Analysis The study consisted of pre-task interviews (assessing AI experience and attitudes), four 7-minute ideation tasks responding to design briefs, and post-task inter views. The four briefs were selected to minimise variation while maintaining a similar cognitive task pro- ! le: all were student-support product design problems in a shared domain: designing a product, app, or service to help students (1) manage productivity , (2) collaborate as a group, (3) plan for the future, and (4) re # ect on what they had learnt, requiring compara- ble levels of problem framing, constraint satisfaction, and feature ideation. W e set the ideation window to 7 minutes, informed by our pilot study , to provide su $ cient time for concept generation while maintaining task focus. Conditions were presented in ! xed order (Seamless → Physical → T emporal → Semantic). W e acknowledge this confounds friction type with order and fatigue e " ects; we return to this limitation in Section 6. W e used a process-oriented, re # exive thematic analysis on transcribed data, focusing on “breakdowns” Drag or Traction: Understanding How Designers Appropriate Friction in AI Ideation Outputs CHI’26 W orkshop on T ools for Thought, April 2026, Barcelona, Spain (moments where friction halte d progress) and “repair strategies” (how users overcame the halt) to surface spe ci ! c appropriation moves [2]. 4 Findings W e found that intentional friction did not simply “break” the user’s work # ow; participants actively appropriated the disruption, trans- forming obstacles into creative tools. However , this appropriation appeared to have depended on the user’s underlying orientation toward ambiguity and constraint. 4.1 Strategies of Appropriation Participants developed three primar y strategies to appropriate de- graded AI output. Keyword Mining (Physical Friction). When fragmented out- put prevented sentence-level reading, participants shifted to key- word mining —scanning broken text for actionable nouns and verbs. P3 describ ed this as accelerate d reading : “It was pulling out keywords for me. So I didn’t have to go through the text and high- light” (P3). The broken text b ecame a “tag cloud” for immediate concept extraction. P6 similarly noted: “I’m seeing bits and pieces now . It’s just triggering a di " erent thought. ” Notably , P3’s case was distinctive: keyword mining was their pre-existing AI work # ow practice, meaning physical fragmentation was functionally invisible as friction—a point we return to in Section 4.3. Parallel Processing & Front-Loading (T emporal Friction). Under delays, participants ! lled the void with indep endent ideation rather than waiting passively . P5 describ ed front-loading : “I went to ther e [my own mind], came up with a few ideas while I was waiting... and then you can come back and tweak” (P5). This shifte d the usual dynamic: participants reported leading with their own ideas b efore consulting AI output, though we acknowledge the study design cannot fully distinguish delib erate front-loading from idle time- ! lling during the enforced wait. P2 similarly noted: “I like to think I had some good ideas in the time that it took. ” Abstract Interpretation (Semantic Friction). Semantic fric- tion, the cryptic, metaphorical outputs, forced interpretive labor . Unlike physical friction (which obscured form) or temporal friction (which delayed delivery), semantic friction obscured meaning itself . For ambiguity-tolerant participants, this b ecame puzzle-solving: “I feel like you’ve got given a di " erent medium, like jelly . But it allowed you to move more # uid within it” (P5). P1 described the experience as a “mini-game, ” actively se eking connections between abstract metaphors and concrete design problems: “Thinking of a labyrinth makes me think of like a maze... so maybe a game app. ” 4.2 “Friction Disposition” Appropriation and appreciation of friction were not universal; it depended on users’ tolerance for ambiguity—their readiness to engage with uncertain, incomplete, or metaphorical content. High- tolerance users treated friction as invitation; low-tolerance users experienced it as obstruction (T able 3). W e propose that Friction Disp osition relates to at least two mea- surable factors: (a) tolerance for ambiguity —a well-studied p ersonal- ity construct [ 5 ]—and ( b) pre-existing work # ow orientation toward AI output. P3, who already parsed AI text by keywords rather than sentences, experienced physical friction as invisible. P4, who re- lied on copy-paste work # ows optimised for spee d, experience d all friction as obstruction. In our data, these pre-existing orientations appeared to align with friction responses at least as strongly as friction type alone. 4.3 Preliminary Dispositional Pro # les Though preliminary , our data with six designers reveals three dis- tinct archetypes of what we term Friction Disposition. The Reframers (P1, P5) interpreted friction as creative con- straint. P1 exhibited playful engagement—treating friction as a puzzle: “I could genuinely feel my head throbbing to think... usually when I’m using AI I’m not thinking. ” P5 described semantic friction as liberating: “I could go wherever I wanted, like a constellation, a forest. It didn’t give me the answer , so I had to make it” (P5). The F luent Appropriator (P3) represents users for whom friction b ecomes invisible . As noted in Section 4.1, P3’s baseline keyword-processing practice meant physical fragmentation was not experience d as friction at all. Furthermore, P3 treated semantic metaphors not as riddles but as “role reversal”: “It’s forcing me to have to use my own brain... It’s almost like a role reversal” while producing the highest idea count under this condition T able 4. The Resisters (P4, P6) perceived friction as obstruction. P4: “I don’t like it at all. It’s just annoying. I want it to give me the ideas so I can move on” (P4). P6 exhibited friction fatigue: they adapted to physical and temporal friction early but collapsed under semantic friction: “This is where I would tap out... This hurts my brain to try to interpret. ” (W e note that semantic friction was always the ! nal condition, so cumulative fatigue may have contributed to P6’s collapse; see Section 6.) 4.4 A Dispositional Contrast: P4 vs P3 The P3–P4 contrast most clearly illustrates Friction Disposition as a moderator . Both experienced identical conditions yet diverged dramatically . P3 entered the study with a keyword-processing men- tal model ( “I never copy-paste whole ideas” ), and this pre-existing orientation meant friction was absorb ed into work # ow rather than fought against—physical fragmentation was frictionless, temporal delays b ecame background processing time, and semantic ambigu- ity produced the highest engagement. P4, by contrast, prioritise d speed and e $ ciency ( “e $ ciency is a big part of my process” ), experi- encing each friction typ e as obstruction to be endured or rejected. This divergence under identical experimental conditions suggests that friction appropriation is relational , depending not only the fric- tion design but also on the user’s pre-existing readiness to engage with resistance—their Friction Disposition (see also T able 3). 5 Discussion Our ! ndings extend the discourse on friction in human- AI collab o- ration for AI-assisted creative ideation, a low-stakes domain where the cost of a “bad” idea is tolerable compared to a high-stakes do- main with strict requirements of safety and reliability . W e se e three paradoxes playing out, which merit further investigation: Friction: Drag or Traction? In physics, friction provides trac- tion —the grip that enables controlled motion. Our ! ndings reveal the same duality . P1’s disp osition converted friction into traction : CHI’26 W orkshop on T ools for Thought, April 2026, Barcelona, Spain A. Baki Kocaballi, Joseph Kizana, Sharon Stein, and Simon Buckingham Shum T able 3: Participant appropriation traje ctories and strategies across friction conditions. ID Archetype Physical Friction T emp oral Friction Semantic Friction P1 Reframer Breakdown → Repair: deco d- ing/puzzle solving. Breakdown → Repair: front- loading (sketching while waiting). Breakdown → Repair: abstract interpretation (“mini-game”). P2 Reframer Breakdown → Repair: keyword mining/extraction. Breakdown → Repair: anticipa- tory ideation during delay. Endured/W orked-around: mixed uptake; limited interpreta- tion. P3 Fluent Appropriator Absorbed: keyword mining as baseline practice. Absorbed: parallel processing; AI queued in background. Breakdown → Repair: abstract interpretation (“role reversal”). P4 Resister Rejected: abandoned interpreta- tion. Rejected: impatient wait- ing/bypassing. Rejected: re-rolled for concrete- ness. P5 Reframer Breakdown → Repair: keyword mining (slower but functional). Breakdown → Repair: front- loading own ideas ! rst. Breakdown → Repair: abstract interpretation (“like jelly”). P6 Resister Rejected: incomplete ideas un- helpful. Rejected: delays as failure. Collapse: tappe d out under inter- pretive load. productive resistance that enabled agency . P4’s disp osition experi- enced only drag : wasted e " ort that blocked work # ow . The same design produced opposite valences depending on disp osition. The Seamlessness Trap . A central tension emerged: users who most need friction (over-trusters like P4) are least likely to accept it. P4 copy-pasted AI output verbatim in the seamless condition— demonstrating precisely the over-reliance that friction targets—yet rejected friction outright. Buçinca et al. [ 4 ] found e " ective inter- ventions received the worst ratings. Unlike their uniform ! nding, we observed variance: P1 called friction “like a game”; P5 found it “lib erating. ” This suggests the e " ectiveness-acceptance trade-o " depends on who the user is. This pattern resonates with psycholog- ical reactance theor y: users who perceive friction as threatening their e $ ciency , a valued behavioral freedom, resist the intervention most strongly , regardless of its potential bene ! t [3]. Friction Fatigue. P6’s collapse in the ! nal condition suggests friction tolerance may be a function of cumulative cognitive load . Sustained interpretive e " ort under friction depletes attentional re- sources, leading to breakdown—a framing grounded in cognitive load theory rather than the conteste d ego depletion mo del. Fu- ture designs should consider fatigue-aware adaptation by reducing friction intensity as sessions progress or cognitive load exceeds a threshold. Prior work on friction has fo cuse d on whether to intro- duce friction, not who bears the cost . However , friction could be shareable: future designs might distribute the burden through AI self-interpretation (o " ering multiple readings of its own metaphor) or progressive disclosure (cryptic output with an “explain” tog- gle). T able 5 in Appendix shows the improved version of Spark implementing tuneable frictions. Design Implications. Based on our ! ndings, we propose four principles for designing Generative Friction: • Mode-selectable: O " er exploration, e $ ciency , and validation modes rather than a single intensity dial. As P3 noted, some users already “parse text by keywords, ” making P hysical friction invisible for some and obstructive for others. • Legible: Explicitly communicate friction’s purpose. Instead of “Y ou may experience incomplete suggestions, ” try “This tool shows fragments to help you build ideas, not copy them”— reframing friction from “broken feature” to “ designed a " or- dance. ” • Burden-shared: Distribute cognitive work between user and AI through mechanisms like progressive disclosure or AI self-interpretation. • Escapable: Preserve agency through opt-out mechanisms, since inescapable friction drives abandonment [ 19 ]. Friction should b e a nudge , not a trap . W e note a productive tension between legibility and escapability: making friction’s purpose visible may reduce the ne e d for escape, while easy escape may undermine the generative intent. Future work should explore how these principles interact in practice. W e note also that Friction Disposition’s focus on readiness to engage with ambiguity and uncertainty resonates directly with pr ofessional and student "Learning Dispositions" [ 11 ] that slows learners down to re # ect on de ep-seate d assumptions. W e see a key opportunity to investigate friction design for deeper learning. Our sample (N=6) allowed rich qualitative insight but limits gen- eralizability . Conditions were presented in ! xed order (Seamless → Physical → T emporal → Semantic), so we cannot fully disentangle friction e " ects from order e " e cts or fatigue. Future work should counterbalance conditions, develop a validate d Friction Disposition scale, and explore adaptive friction systems that respond to user state in real time. 6 Conclusion W e investigated whether intentional friction can transform AI out- put from ! nished product into semi- ! nishe d material requiring further human engagement. W e found that friction enabled di " er- ent appropriation methods including disappr opriation. Our ! ndings suggest user appropriation is mo derate d by Friction Disposition. High-disposition users describe d friction as “liberating” and “like a game”; low-disposition users experienced only drag. W e propose two conceptual contributions: the concept of Generative Friction to stimulate human engagement and Friction Disp osition as a po- tential moderating factor . Drag or Traction: Understanding How Designers Appropriate Friction in AI Ideation Outputs CHI’26 W orkshop on T ools for Thought, April 2026, Barcelona, Spain References [1] Adam L. Alter , Daniel M. Oppenheimer , Nicholas Epley , and Rebecca N. Eyre. 2007. Overcoming Intuition: Metacognitive Di $ culty Activates Analytic Reasoning. Journal of Experimental Psychology: General 136, 4 (2007), 569–576. doi:10.1037/ 0096- 3445.136.4.569 [2] Virginia Braun, Victoria Clarke, and Nicola Rance. 2014. 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SP ARK v2’s design directly implements two of our proposed design principles: Mode-selectable (users choose their friction level) and Escapable (progressive disclosure allows opting out of high friction without abandoning the tool entirely). The tuneable controls also operationalise the Burden-shared principle by distributing inter- pretive e " ort b etween the system’s default friction and the user’s chosen level of engagement. T able 4: Idea counts per participant across seamless and fric- tion conditions. Participant Seamless Physical T emporal Semantic P1 6 5 6 6 P2 5 1 1 1 P3 9 7 11 14 P4 5 3 5 4 P5 4 3 5 5 P6 5 4 5 2 CHI’26 W orkshop on T ools for Thought, April 2026, Barcelona, Spain A. Baki Kocaballi, Joseph Kizana, Sharon Stein, and Simon Buckingham Shum T able 5: Comparison of SP ARK v1 and SP ARK v2 friction conditions. SP ARK v2 introduces user-controllable mechanisms that allow participants to modulate friction intensity , addressing the Friction Disposition di " erences observed in the study . Friction Type SP ARK v1 SP ARK v2 (Tuneable) Physical Every se cond words are hidden, creating a fragmented presentation with ellipses b etween words. Fragmented display is retained, but users can click on the ellipsis to reveal hidden words, progressively reducing friction on demand. T emp oral AI output text is displayed gradually over time. Users can play , pause, and spee d up the pre- sentation of ideas using playback controls, giving them agency over pacing. Semantic Output is presented as cr yptic metaphors and riddle-like text. Users must interpret abstract lan- guage to extract design ideas. Metaphorical output is retained, but users can click on the idea box to reveal a more literal description, providing an “explain” toggle.