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arxiv: 2606.26626 · v1 · pith:SWF4SFTQnew · submitted 2026-06-25 · 💻 cs.HC

Reviving Reflection-in-Action: Instilling Designerly Thinking in AI-Supported Ideation through Multimodal Prompting

Samangi Wadinambiarachchi , Jenny Waycott , Greg Wadley This is my paper

Pith reviewed 2026-06-26 03:43 UTC · model grok-4.3

classification 💻 cs.HC
keywords AI creativity support toolsmultimodal promptingreflection-in-actiondesign ideationsketch inputdivergent thinkinghuman-AI interactiondesignerly thinking
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The pith

Sketch input to AI design tools tends to increase the number of ideas generated compared with text input alone.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper built SketchifAI, a prototype that lets design students prompt an AI generator with text, freehand sketches, or sketches plus tags. A within-participants study measured how each modality affected students' sense that they could express their intent, their view of the tool's creativity support, and standard divergent-thinking scores such as fluency, variety, originality, and quality. Sketch input produced higher fluency scores than text, while differences on the other measures remained unclear; participants nevertheless expressed a clear preference for text prompting. The authors conclude that deliberate use of sketching can reintroduce productive friction and reflection during AI-supported ideation. They argue that future AI creativity tools should therefore be built to encourage sketching so that core design practices are not lost.

Core claim

By comparing text, sketch, and sketch-plus-tags input in the SketchifAI prototype, the study shows that the sketch modality tended to enhance fluency in divergent thinking tasks while evidence for effects on variety, originality, or quality remained inconclusive; at the same time participants strongly preferred text prompting, leading the authors to propose that AI tools can be deliberately designed to revive reflection-through-sketching and thereby sustain essential designerly thinking skills.

What carries the argument

The SketchifAI prototype that accepts text, sketch, or sketch-plus-tags as prompts to an AI image generator, evaluated through a mixed-methods within-participants comparison of perceived intent expression, creativity-support ratings, and divergent-thinking performance.

If this is right

  • AI creativity support tools can be engineered to favor sketch input in order to increase the sheer number of ideas produced during ideation.
  • Design education using AI should retain sketching interfaces to keep reflection-in-action active rather than letting text-only prompting dominate.
  • Multimodal prompting offers a practical route to maintain divergent thinking skills when students work with generative AI.
  • Tool designers can add lightweight tagging to sketches without losing the fluency benefit observed in the sketch-only condition.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If sketching forces slower, more deliberate prompting, the same friction could be introduced in other creative domains such as writing or music composition.
  • A longitudinal study tracking whether repeated sketch prompting improves students' unaided sketching skill would test whether the benefit transfers beyond the AI session.
  • Sequencing modalities, for example starting with text then switching to sketch for refinement, might capture both the preference for text and the fluency gain from sketching.

Load-bearing premise

The specific study setup with SketchifAI and its participant pool sufficiently isolates the effect of input modality from order, familiarity, or prototype limitations.

What would settle it

A larger or better-controlled replication that finds no fluency advantage for sketch input over text input would undermine the central claim.

Figures

Figures reproduced from arXiv: 2606.26626 by Greg Wadley, Jenny Waycott, Samangi Wadinambiarachchi.

Figure 1
Figure 1. Figure 1: Interfaces of SketchifAI. The left panel shows three input modalities: (1) Text, (2) Sketch, and (3) SketchPlusTags, where (A) shows the user’s input and (B) shows the corresponding AI-generated sketch output. The right panel shows the application UI for each modality, stacked with Text (back), SketchPlusTags (middle), and Sketch (front), each comprising (4) a Toolbox for drawing controls and (5) Canvas Fr… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of briefs provided to participants. Brief A (left), Brief B (middle), and Brief C (right). [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Participant activity workflow. During the study par [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The overall experiment flow (1–2): Initial briefing, consent, questionnaire, (3–17): Main experimental sessions, (18–19) Semi-structured interview and debriefing. in product and service design. Their ratings exhibited almost per￾fect agreement (𝜅 = 0.87). Clustering, ratings and all questionnaire responses were imported to R 9 for statistical analysis. 4.6 Data Analysis 4.6.1 Quantitative Analysis. Post-ta… view at source ↗
Figure 5
Figure 5. Figure 5: Example products created by participants in [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Model posterior predictions for perceived support [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Model posterior predictions for Creativ￾ity Support. (1) Collaboration, (2) Enjoyment, (3) Exploration, (4) Expressiveness, (5) Immersion, (6) Results-worth-the-Effort. Error bars represent the standard error of the estimates. 5.2 Creativity Support To address RQ2, we utilised the Creativity Support Index (CSI) [13]. This instrument evaluates how effectively a tool supports the cre￾ative process across six… view at source ↗
Figure 8
Figure 8. Figure 8: Model posterior predictions for Fluency (Number of products generated). Error bars represent the standard error of the estimates. 5.3 Divergent Thinking Performance To answer RQ3, we calculated the fluency, Variety, Originality, and Quality of the products based on expert ratings to examine how inspiration stimuli generated through different modalities affect participants’ divergent thinking performance (s… view at source ↗
Figure 9
Figure 9. Figure 9: Model posterior predictions for Variety (Number of clustered covered). Error bars represent the standard error of the estimates [PITH_FULL_IMAGE:figures/full_fig_p009_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Model posterior predictions for Originality (how unique products are). Error bars represent the standard error of the estimates. 5.3.4 Quality of the Products. To modelQuality, experts evaluated whether the participants’ products met the design brief: Yes =1, Maybe =0.5, and No =0. Again, our explorations were limited to simple models because of our sample size restrictions and we opted to explore only th… view at source ↗
Figure 11
Figure 11. Figure 11: Model posterior predictions for Quality (Expert evaluations). Error bars represent the standard error of the estimates. 5.4 UMUX-Lite We modelled UMUX-Lite ∼ Condition (1 + Condition |id), in a Bayesian linear regression model. The Text condition was (𝑀 = 4.15 89%CI [ 3.56, 4.72]). Sketch produced lower scores than Text (𝑀 = −0.77, (89%CI [-1.38, -0.17]), with 97% posterior probability that Sketch reduces… view at source ↗
Figure 13
Figure 13. Figure 13: Example sketches inputs, tags used by participants [PITH_FULL_IMAGE:figures/full_fig_p012_13.png] view at source ↗
read the original abstract

Current AI-powered creativity support tools (AI-CSTs) primarily use text prompting to generate solution-oriented outputs. However, the potential value of multimodal prompting in designer-AI interaction, specifically the introduction of productive friction to encourage iteration and reflection, has not been fully explored. To address this, we developed SketchifAI, a prototype AI-CST, and evaluated it with design students. In a mixed-methods, within-participants study, we examined how different input modalities (text, sketch, and sketch-plus-tags) affected design students' perceived ability to express their intent, their perception of creativity support, and their divergent thinking performance. Our preliminary findings suggest that the sketch modality tended to enhance fluency, with inconclusive evidence for differences in variety, originality, or quality compared to text modality. Yet, paradoxically, participants showed a strong preference for text prompting. We discuss how AI tools might be designed to reintroduce reflection-through-sketching, ensuring that designer-AI interaction supports, rather than erodes, essential design skills in students.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces SketchifAI, a prototype AI creativity support tool enabling multimodal input (text, sketch, sketch-plus-tags), and reports results from a mixed-methods within-participants study with design students. It claims that the sketch modality tended to enhance fluency in divergent thinking tasks relative to text, with inconclusive differences on variety, originality, and quality, yet participants paradoxically preferred text prompting; the authors conclude that multimodal designs can reintroduce productive friction to support reflection-in-action and preserve designerly skills.

Significance. If the modality effects prove robust after addressing design confounds, the work would usefully extend HCI research on AI-CSTs by demonstrating how input modality can influence reflection and divergent thinking, offering concrete guidance for tools that avoid eroding core design practices. The mixed-methods framing and focus on preliminary user data are appropriate strengths for an early-stage exploration.

major comments (2)
  1. [Methods] Methods section: The within-participants comparison of text vs. sketch vs. sketch-plus-tags modalities does not report counterbalancing of presentation order, washout procedures, or any statistical tests for sequence or carryover effects. This directly threatens the causal interpretation of the reported fluency advantage and the text-preference paradox, as these could arise from order, differential sketching familiarity, or prototype rendering differences rather than modality per se.
  2. [Results] Results and Abstract: The central empirical claims rest on 'preliminary findings' with several inconclusive measures and a paradoxical preference result, yet the manuscript provides no full statistical details, raw data, error bars, or participant-level breakdowns. Without these, the fluency enhancement claim and the overall pattern cannot be verified or assessed for practical significance.
minor comments (2)
  1. [Discussion] The connection between the observed preference paradox and Schön's reflection-in-action framework is asserted in the discussion but would benefit from a more explicit mapping to specific study measures or participant quotes.
  2. [Figures] Figure captions and axis labels for any divergent-thinking metrics (fluency, variety, etc.) should explicitly state the scoring rubric and inter-rater reliability to allow readers to interpret the inconclusive results.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful and constructive comments on our manuscript. We address each major point below and will revise the paper to improve clarity and transparency.

read point-by-point responses

  1. Referee: [Methods] Methods section: The within-participants comparison of text vs. sketch vs. sketch-plus-tags modalities does not report counterbalancing of presentation order, washout procedures, or any statistical tests for sequence or carryover effects. This directly threatens the causal interpretation of the reported fluency advantage and the text-preference paradox, as these could arise from order, differential sketching familiarity, or prototype rendering differences rather than modality per se.

    Authors: We agree that these procedural details are necessary to support causal claims in a within-participants design. The study used a balanced Latin square to counterbalance modality order across participants. No dedicated washout period was included because tasks were short and independent, but short breaks were provided between conditions. We will add a complete description of the counterbalancing procedure to the Methods section and report the results of statistical checks for order and carryover effects (which showed no significant impact on fluency or preference outcomes). revision: yes

  2. Referee: [Results] Results and Abstract: The central empirical claims rest on 'preliminary findings' with several inconclusive measures and a paradoxical preference result, yet the manuscript provides no full statistical details, raw data, error bars, or participant-level breakdowns. Without these, the fluency enhancement claim and the overall pattern cannot be verified or assessed for practical significance.

    Authors: We accept that the current Results section is too abbreviated for full verification. In revision we will add complete statistical reporting (including exact test statistics, p-values, effect sizes, and confidence intervals), include error bars on all relevant figures, and provide anonymized participant-level summaries or breakdowns for the key measures. We will also expand discussion of the inconclusive results and the preference paradox. Raw participant data will be made available upon reasonable request subject to consent constraints, but the analysis pipeline will be fully documented. revision: yes

Circularity Check

0 steps flagged

Empirical user study with no derivation chain or self-referential predictions

full rationale

The paper reports a mixed-methods within-participants study of the SketchifAI prototype, comparing input modalities on measures of fluency, variety, originality, quality, and preference. No equations, fitted parameters, uniqueness theorems, or predictions appear in the text. All claims rest on collected participant data and thematic analysis rather than any reduction of outputs to inputs by construction. Self-citations, if present, are not load-bearing for any central result. This matches the default expectation for non-circular empirical work.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an empirical HCI user study; no mathematical free parameters, domain axioms, or invented entities are introduced or required.

pith-pipeline@v0.9.1-grok · 5723 in / 1041 out tokens · 47911 ms · 2026-06-26T03:43:55.088323+00:00 · methodology

discussion (0)

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Reference graph

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