Visual Thinking in the AI Era

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]
    Rau, M. A. (2017). Conditions for the Effectiveness of Multiple Visual Representations in Enhancing STEM Learning. Educational Psychology Review.Review
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]
    Dibia, V. (2023). LIDA A Tool for Automatic Generation of Grammar Agnostic Visualizations and Infographics using Large Language Models. ACL System Demonstrations.Research system
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]
    Lee, H. P., et al. (2025). The Impact of Generative AI on Critical Thinking Self Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers. CHI; DOI 10.1145/3706598.3713778.Self-report survey
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]
    NIST (2024). Artificial Intelligence Risk Management Framework Generative Artificial Intelligence Profile. NIST AI 600-1.Official risk framework
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