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arxiv: 2607.02361 · v1 · pith:WZGDKY25new · submitted 2026-07-02 · 💻 cs.HC · cs.ET

Data Comics for Education: Evaluating Effectiveness, Benefits, and the Ethics of AI-Assisted Creation

Zirui Shan , Vanessa Echeverria , Yuheng Li , Yi-Shan Tsai , Roberto Martinez-Maldonado This is my paper

Pith reviewed 2026-07-03 05:53 UTC · model grok-4.3

classification 💻 cs.HC cs.ET
keywords data comicsvisualization literacygenerative AIdata visualizationeducationAI ethicscomprehension tasks
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The pith

University students performed better on insight comprehension tasks with data comics than with conventional visualizations, regardless of their visualization literacy.

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

The paper examines whether data comics can help students interpret data more effectively than standard charts in classroom settings. It describes a study with 60 university students who completed information retrieval and comprehension tasks using both formats, where the comics were produced with generative AI support. Performance improved notably with the comics, especially on tasks that required extracting insights, and this held across different levels of prior visualization skills. Participants described the comics as more engaging and easier to follow but voiced worries about factual accuracy and ownership when AI tools are involved in creation.

Core claim

In a within-subjects study with 60 university students, data comics created with generative AI assistance led to higher performance on insight comprehension tasks compared to conventional visualizations. The advantage appeared independent of participants' prior visualization literacy. Students rated the comics as more engaging and easier to understand, while also raising concerns about potential misinformation and ownership issues arising from the AI-assisted production process.

What carries the argument

Within-subjects comparison of conventional visualizations versus GenAI-assisted data comics on information retrieval and insight comprehension tasks.

If this is right

  • Data comics may function as an effective medium for improving data understanding in education.
  • Generative AI can lower the creation effort needed to produce data comics for classroom use.
  • Ethical questions around misinformation and ownership must be resolved before broad adoption of AI-assisted data comics.
  • The performance benefit occurs across varying levels of visualization literacy.

Where Pith is reading between the lines

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

  • The approach could be tested with younger students or in non-university settings to check if the gains generalize.
  • If accuracy concerns are addressed, educators might more readily adopt narrative formats for data lessons.
  • Studies could examine how much human editing is required to keep AI-generated comics reliable for teaching.

Load-bearing premise

The data comics made with generative AI help kept the same factual accuracy and teaching value as human-created ones and did not introduce errors or biases that would change the measured performance differences.

What would settle it

A follow-up experiment that uses only human-created data comics and still finds the same performance advantage on insight tasks would support the claim; finding no advantage would indicate the benefit may stem from the specific comic quality or AI process rather than the comic format.

Figures

Figures reproduced from arXiv: 2607.02361 by Roberto Martinez-Maldonado, Vanessa Echeverria, Yi-Shan Tsai, Yuheng Li, Zirui Shan.

Figure 1
Figure 1. Figure 1: Conventional visualisation - CV2 in comparative pair 2 [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Data comic - DC2 in comparative pair 2 10 [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Correct rate of data comics (DC) versus conventional visualisations (CV). The [PITH_FULL_IMAGE:figures/full_fig_p015_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Correct rate in Information Retrieval and Comprehension tasks between conven [PITH_FULL_IMAGE:figures/full_fig_p016_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Correct rate in Comprehension tasks with single and multiple insights between [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Comparative Visualisations 1: Conventional Visualisation [PITH_FULL_IMAGE:figures/full_fig_p040_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Comparative Visualisations 1: Data Comics [PITH_FULL_IMAGE:figures/full_fig_p041_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Comparative Visualisations 2: Conventional Visualisation [PITH_FULL_IMAGE:figures/full_fig_p042_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Comparative Visualisations 2: Data Comics [PITH_FULL_IMAGE:figures/full_fig_p043_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Comparative Visualisations 3: Conventional Visualisation [PITH_FULL_IMAGE:figures/full_fig_p045_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Comparative Visualisations 3: Data Comics [PITH_FULL_IMAGE:figures/full_fig_p046_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Comparative Visualisations 4: Conventional Visualisation [PITH_FULL_IMAGE:figures/full_fig_p047_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Comparative Visualisations 4: Data Comics [PITH_FULL_IMAGE:figures/full_fig_p048_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: • Style-alternation iterative prompting. 3The car image was used as the example. 51 [PITH_FULL_IMAGE:figures/full_fig_p051_14.png] view at source ↗
Figure 14
Figure 14. Figure 14: Example snapshot of data comics illustrating the [PITH_FULL_IMAGE:figures/full_fig_p053_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Example snapshot of data comics illustrating the [PITH_FULL_IMAGE:figures/full_fig_p054_15.png] view at source ↗
read the original abstract

In today's data-driven world, students often struggle with interpreting visualisations due to limited visualisation literacy. Data comics have emerged as a promising medium to enhance engagement and understanding, but their educational value has seen little empirical examination, partly due to the effort required to create them. Recent advances in Generative AI (GenAI) offer a scalable solution to this challenge. We conducted a within-subjects study with 60 university students, comparing conventional visualisations with data comics, created with assistance from GenAI tools, across information retrieval and comprehension tasks. Students consistently performed better with data comics, particularly in insight comprehension tasks, independent of prior visualisation literacy. Students also commented data comics as more engaging and easier to understand, though concerns were raised about GenAI-driven misinformation and ownership. Our findings highlight the potential of data comics as a potentially effective tool for data communication in education, while underscoring the need to address ethical concerns related to AI-assisted creation.

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 reports a within-subjects user study (N=60 university students) comparing conventional visualizations to data comics created with GenAI assistance on information-retrieval and insight-comprehension tasks. It claims that participants performed better with data comics (especially on comprehension), that gains were independent of prior visualization literacy, and that comics were rated more engaging, while also documenting participant concerns about GenAI misinformation and ownership.

Significance. If the performance differences are robust and not confounded by content quality, the work would supply needed empirical evidence that data comics can improve educational outcomes in data interpretation and that GenAI can lower creation barriers. The within-subjects design and literacy-independence claim are potentially valuable for the HCI/education community, though the absence of reported accuracy-validation procedures for the GenAI materials limits the strength of the causal attribution to format.

major comments (2)
  1. [Methods] Methods section: the description of stimulus creation states that comics were 'created with assistance from GenAI tools' but supplies no post-generation factual-accuracy check, expert review protocol, or comparison of data fidelity between comic and conventional conditions. Because the central claim attributes performance gains to the comic format rather than to differences in content accuracy or pedagogical framing, this omission is load-bearing.
  2. [Results] Results section: the abstract and summary claim 'consistently performed better' and 'independent of prior visualisation literacy' yet the provided text contains no statistical tests, effect sizes, confidence intervals, or exclusion criteria. Without these details the independence claim cannot be evaluated and post-hoc selection cannot be ruled out.
minor comments (2)
  1. [Abstract] Abstract: reports performance differences without any statistical detail or task materials; this should be expanded or moved to a results summary.
  2. [Discussion] Ethics discussion: participant comments on misinformation and ownership are noted but not linked back to any concrete mitigation steps or study limitations; a short paragraph connecting these concerns to the GenAI creation process would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback, which identifies key areas where additional detail will strengthen the manuscript's transparency and interpretability. We address each major comment below and will revise accordingly.

read point-by-point responses

  1. Referee: [Methods] Methods section: the description of stimulus creation states that comics were 'created with assistance from GenAI tools' but supplies no post-generation factual-accuracy check, expert review protocol, or comparison of data fidelity between comic and conventional conditions. Because the central claim attributes performance gains to the comic format rather than to differences in content accuracy or pedagogical framing, this omission is load-bearing.

    Authors: We agree this detail is necessary for causal attribution. The revised manuscript will add a dedicated subsection on stimulus creation that specifies: (1) the original data sources and conventional visualizations used in both conditions, (2) the exact GenAI tools and prompts, (3) the post-generation verification steps performed by the authors (including manual cross-checks against source data and any expert review), and (4) confirmation that the same underlying data values and pedagogical framing were preserved across formats. This will allow readers to evaluate content equivalence. revision: yes

  2. Referee: [Results] Results section: the abstract and summary claim 'consistently performed better' and 'independent of prior visualisation literacy' yet the provided text contains no statistical tests, effect sizes, confidence intervals, or exclusion criteria. Without these details the independence claim cannot be evaluated and post-hoc selection cannot be ruled out.

    Authors: We acknowledge that the current text does not report the statistical details. The revised results section will explicitly include: the full statistical models (e.g., mixed-effects ANOVA or regression), all test statistics, p-values, effect sizes, confidence intervals, power considerations, and participant exclusion criteria. We will also detail how the literacy-independence claim was tested (via interaction effects between condition and visualization literacy score) to allow direct evaluation of the claim. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical user study with no derivations or self-referential modeling

full rationale

The paper reports a within-subjects user study (N=60) measuring task performance on information retrieval and insight comprehension, with no equations, fitted parameters, predictive models, or derivation chains. Central claims rest directly on observed scores and qualitative comments rather than any self-definition, fitted-input renaming, or self-citation load-bearing step. The GenAI creation detail is a methodological note, not a modeled input that is later re-predicted. No load-bearing uniqueness theorems or ansatzes appear. This is the expected 0 outcome for a purely empirical report whose results are externally falsifiable via replication.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on the assumption that the within-subjects comparison isolates the effect of comic format from other variables and that GenAI assistance did not systematically alter content quality; no free parameters, invented entities, or non-standard axioms are introduced in the abstract.

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discussion (0)

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