RESEARCH REPORT · 17 OCTOBER 2026

Visual Thinking in the AI Era

From key concepts and research evidence to AI implementation, instructional design, and professional practice.

Bilingual edition: ~70,700 Word-count units · 128 pages

A cycle of framing, representation selection, AI candidates, evidence checks, human judgment, and feedback
Make visual artifacts part of an inspectable reasoning process
24Chapters
25 + 24Figures + tables
42English research and official sources
107Bilingual concept entities

See the relationships.
Return to the evidence.

Visual thinking uses internal imagery and external representations to select information, organize relationships, explore change, and test explanations. AI can lower production barriers; its value for learning and decisions depends on people being able to interpret, revise, and question the resulting representations.

The report uses critical narrative synthesis and distinguishes experimental results, theoretical explanations, technical feasibility, and recommendations. Evidence for learner-generated drawing cannot directly establish the learning benefits of AI-generated drawing.

CONCEPTS · METHODS · APPLICATIONS · EVIDENCE

Visual Thinking Knowledge Graph

107 concept entities and 95 explicitly named semantic relationships. Search terms, follow connections, and inspect report passages and references.

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Read by chapter

01 — 24
01

Research questions and principal conclusions

4 sections · Illustrated reading ↗
02

Concepts and intellectual traditions of visual thinking

4 sections · Illustrated reading ↗
03

Cognitive mechanisms and individual differences

4 sections · Illustrated reading ↗
04

Evidence and boundary conditions for learning

4 sections · Illustrated reading ↗
05

The grammar and design of visual representations

4 sections · Illustrated reading ↗
06

Value and capability development in the AI era

4 sections · Illustrated reading ↗
07

Technical routes for AI supported visual thinking

4 sections · Illustrated reading ↗
08

An architecture for trustworthy generation and validation

4 sections · Illustrated reading ↗
09

A working method for thinking with AI

4 sections · Illustrated reading ↗
10

Hotel operations and service design cases

4 sections · Illustrated reading ↗
11

Marketing and tourism finance cases

4 sections · Illustrated reading ↗
12

Redesigning an introductory AI course

4 sections · Illustrated reading ↗
13

Classroom implementation and a complete lesson design

4 sections · Illustrated reading ↗
14

Skills and expert role collaboration

4 sections · Illustrated reading ↗
15

Evaluation experiments and research design

4 sections · Illustrated reading ↗
16

Risk management and implementation

4 sections · Illustrated reading ↗
17

Reusable tasks and prompting templates

4 sections · Illustrated reading ↗
18

Terminology and a research agenda

4 sections · Illustrated reading ↗
19

Applications in research reading and knowledge discovery

4 sections · Illustrated reading ↗
20

Design practice and organizational collaboration

4 sections · Illustrated reading ↗
21

Deployment reproducibility and daily use

4 sections · Illustrated reading ↗
22

Integrated case discussions and exercises

4 sections · Illustrated reading ↗
23

Interactive computation and visual simulation

4 sections · Illustrated reading ↗
24

Integrated discussion and priority actions

4 sections · Illustrated reading ↗

Full report and supporting materials

The Word and PDF downloads contain the same complete bilingual edition. Web chapters present each language separately.

SEARCH CUTOFF: 17 OCTOBER 2026

References and official documentation

Original English titles are retained for discovery. Read source types and limitations alongside the chapters; preprints are not equivalent to peer-reviewed findings.

  1. [1]
    Arnheim, R. (1969). Visual Thinking. University of California Press.Theoretical monograph
  2. [2]
    Grandin, T. (2022). Visual Thinking The Hidden Gifts of People Who Think in Pictures Patterns and Abstractions. Riverhead Books.Research-informed trade book and personal perspective
  3. [3]
    Larkin, J. H., & Simon, H. A. (1987). Why a Diagram is Sometimes Worth Ten Thousand Words. Cognitive Science, 11, 65–100.Computational and theoretical analysis
  4. [4]
    Blazhenkova, O., & Kozhevnikov, M. (2009). The new object spatial verbal cognitive style model Theory and measurement. Applied Cognitive Psychology, 23, 638–663.Psychometric research
  5. [5]
    Blazhenkova, O., Becker, M., & Kozhevnikov, M. (2011). Object spatial imagery and verbal cognitive styles in children and adolescents Developmental trajectories in relation to ability. Learning and Individual Differences, 21, 281–287.Developmental correlational research
  6. [6]
    Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning Styles Concepts and Evidence. Psychological Science in the Public Interest, 9, 105–119.Critical evidence review
  7. [7]
    Uttal, D. H., et al. (2013). The malleability of spatial skills A meta analysis of training studies. Psychological Bulletin, 139, 352–402.Meta-analysis
  8. [8]
    Mayer, R. E. (2021). Cognitive Theory of Multimedia Learning. The Cambridge Handbook of Multimedia Learning, third edition, chapter 5.Theory and research synthesis
  9. [9]
    Ainsworth, S. (2006). DeFT A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16, 183–198.Conceptual framework
  10. [10]
  11. [11]
    Fiorella, L., & Zhang, Q. (2018). Drawing Boundary Conditions for Learning by Drawing. Educational Psychology Review.Review
  12. [12]
    Cromley, J. G., Du, Y., & Dane, A. P. (2020). Drawing to Learn Does Meta Analysis Show Differences Between Technology Based Drawing and Paper and Pencil Drawing. Journal of Science Education and Technology, 29, 216–229; first online 2019.Meta-analysis
  13. [13]
    Leutner, D., & Biele, J. (2025). Without Integration Everything Is Nothing A Meta Analysis of the Effectiveness of Instructional Support for Drawing to Learn. Educational Psychology Review, 37.Meta-analysis; fetched full paper record
  14. [14]
    Rexigel, E., Kühn, J., & Becker-Genschow, S. (2024). The More the Better A Systematic Review and Meta Analysis of the Benefits of More than Two External Representations in STEM Education. Educational Psychology Review.Systematic review and meta-analysis
  15. [15]
    Zhang, Q., & Fiorella, L. (2021). Learning by drawing When is it worth the time and effort. Contemporary Educational Psychology.Two experiments; source record
  16. [16]
    Tversky, B., Morrison, J. B., & Betrancourt, M. (2002). Animation can it facilitate. International Journal of Human Computer Studies.Review and design analysis
  17. [17]
    Kirsh, D., & Maglio, P. (1994). On Distinguishing Epistemic from Pragmatic Action. Cognitive Science, 18, 513–549.Situated cognition analysis
  18. [18]
    Cleveland, W. S., & McGill, R. (1984). Graphical Perception Theory Experimentation and Application to the Development of Graphical Methods. Journal of the American Statistical Association, 79, 531–554.Graphical perception experiments
  19. [19]
    Sutherland, I. E. (1963). Sketchpad A Man Machine Graphical Communication System. MIT doctoral dissertation.Technical dissertation
  20. [20]
    Hu, Y., et al. (2024). Visual Sketchpad Sketching as a Visual Chain of Thought for Multimodal Language Models. NeurIPS 37.Peer-reviewed technical benchmark study
  21. [21]
  22. [22]
    Wang, C., Thompson, J., & Lee, B. (2023). Data Formulator AI powered Concept driven Visualization Authoring. IEEE VIS; Microsoft Research publication record.Research prototype and small user study
  23. [23]
    Microsoft Research (2024). Data Formulator Exploring how AI can help analysts create rich data visualizations. Research project account.Primary project description
  24. [24]
    Masry, A., et al. (2022). ChartQA A Benchmark for Question Answering about Charts with Visual and Logical Reasoning. Findings of ACL, 2263–2279.Benchmark
  25. [25]
    Rahmanzadehgervi, P., et al. (2024; revised 2025). Vision language models are blind Failing to translate detailed visual features into words. arXiv 2407.06581, version 6.Preprint benchmark; version-specific findings
  26. [26]
    Lu, P., et al. (2024). MathVista Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts. ICLR; arXiv 2310.02255.Benchmark
  27. [27]
    Vaccaro, M., Almaatouq, A., & Malone, T. (2024). When combinations of humans and AI are useful A systematic review and meta analysis. Nature Human Behaviour, 8, 2293–2303.Preregistered systematic review and meta-analysis
  28. [28]
  29. [29]
    Bastani, H., et al. (2025). Generative AI without guardrails can harm learning Evidence from high school mathematics. PNAS, 122, e2422633122.Randomized controlled trial
  30. [30]
  31. [31]
    UNESCO (2023). Guidance for generative AI in education and research. UNESCO.Policy guidance
  32. [32]
    W3C Web Accessibility Initiative (accessed 2026). Complex Images. Images Tutorial.Official accessibility guidance
  33. [33]
    W3C Web Accessibility Initiative (accessed 2026). Understanding Success Criterion 1 4 11 Non text Contrast. WCAG 2.2 guidance.Official accessibility guidance
  34. [34]
    Mermaid (accessed 2026). Flowcharts Syntax and Mindmap. Official documentation.Technical documentation
  35. [35]
    Vega Lite (accessed 2026). Visualization specification and transformations. Official documentation.Technical documentation
  36. [36]
    Wolfram Research (accessed 2026). Manipulate. Wolfram Language Documentation.Technical documentation
  37. [37]
    Wolfram Research (accessed 2026). Graph. Wolfram Language Documentation.Technical documentation
  38. [38]
    Coalition for Content Provenance and Authenticity (version 2.2). Content Credentials Technical Specification. C2PA.Technical specification
  39. [39]
    Dechamps, T., & Skulmowski, A. (2025). The Effective Design of Tasks Involving Learning by Drawing Current Trends and Methodological Progress in Research on Drawing to Learn. Educational Psychology Review, 37.Review; paper record fetched
  40. [40]
    Kim, S., et al. (2025). Can the effects of generative drawing on lasting learning be optimized through integration of retrieval practice. Learning and Instruction.Experimental study; search abstract and fetched metadata
  41. [41]
    Wu, J., et al. (2026). Visual Generation Unlocks Human Like Reasoning through Multimodal World Models. arXiv 2601.19834; preprint.Preprint technical study
  42. [42]
    Zhang, H., et al. (2025). Latent Sketchpad Sketching Visual Thoughts to Elicit Multimodal Reasoning in MLLMs. arXiv 2510.24514; preprint.Preprint technical study