Inclusive Learning Analytics with Embedded Data Comics: A Conceptual Framework for Public Understanding of AI Ethics
Pith reviewed 2026-05-08 09:45 UTC · model grok-4.3
The pith
A conceptual framework combines inclusive learning analytics with data comics to make AI ethics accessible across diverse public mindsets.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
The paper claims that embedding data comics within inclusive learning analytics creates equal and accessible channels for AI ethics issues, enabling the public to reflect on incidents from multiple perspectives, develop empathy and introspection, and form habits of continuous learning to adapt to evolving AI technologies and ethical risks.
What carries the argument
The conceptual framework of inclusive learning analytics with embedded data comics, which transforms abstract AI ethics cases into visual stories to bridge cognitive limitations and biases.
If this is right
- Public reflection on AI ethics incidents becomes more inclusive by accounting for cognitive biases.
- Data comics reduce the complexity barrier so more people can engage with ethical risks.
- Continuous learning habits emerge as people adapt to new AI developments through ongoing access.
- Responsible AI development gains support from a broader, better-informed public base.
Where Pith is reading between the lines
- The framework could be piloted in community workshops to test whether story-based analytics actually shift attitudes toward specific AI cases like bias in hiring tools.
- It might link to existing public science communication efforts, such as museum exhibits on technology, by adding structured analytics tracking of learning progress.
- Policy makers could use similar comic formats to explain AI regulations, though the paper does not test that application.
Load-bearing premise
The assumption that data comics will successfully foster empathy, multi-perspective reflection, and continuous learning habits across people with different mindsets and cognitive biases.
What would settle it
An experiment in which exposure to the proposed data comics and analytics shows no measurable increase in empathy, perspective-taking, or self-reported continuous learning about AI ethics among groups with varied cognitive biases.
Figures
read the original abstract
Public awareness of AI ethics plays a crucial role in fostering the responsible and sustainable development of AI technology. However, finding effective ways to promote public understanding of the ethical risks of AI remains a challenge. Given the complexity of AI ethical issues and the cognitive limitations of the public, this review paper proposes a conceptual framework for inclusive learning analytics with embedded data comics. Data comics help transform complex and abstract AI ethics cases into compelling and relatable stories, fostering public empathy and introspection. More importantly, inclusive learning analytics targets not only people of different demographic attributes, but also different mindsets with inherent cognitive biases. By providing equal and easily accessible channels for AI ethics issues, we aim to encourage the public to reflect on AI ethics incidents from multiple perspectives and develop the habit of continuous learning to adapt to evolving AI technologies and ethical risks.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper proposes a conceptual framework combining inclusive learning analytics with embedded data comics to promote public understanding of AI ethics. It argues that data comics can convert complex AI ethics cases into relatable stories that foster empathy and introspection, while inclusive analytics addresses diverse demographics and cognitive biases to enable multi-perspective reflection and continuous learning habits.
Significance. If operationalized and validated, the framework could meaningfully advance accessible public engagement with AI ethics by leveraging visualization techniques to overcome cognitive barriers, potentially supporting more informed societal discourse on AI risks. The proposal's attention to both demographic inclusivity and mindset diversity is a constructive extension of existing visualization and learning analytics work, though its significance remains prospective given the lack of empirical support.
major comments (3)
- Abstract: The central claim that embedding data comics will 'foster public empathy and introspection' and encourage 'reflection on AI ethics incidents from multiple perspectives' is asserted without any concrete design principles, examples of comic structures targeting specific biases, or mechanisms linking story-based transformation to measurable empathy gains.
- Abstract: The description of 'inclusive learning analytics' that 'targets not only people of different demographic attributes, but also different mindsets with inherent cognitive biases' provides no operational definition, accessibility guidelines for low-literacy audiences, or criteria for reaching high-bias groups, leaving the inclusivity mechanism underspecified.
- Abstract: No evaluation criteria, pilot study design, or falsification plan is outlined to test whether the framework produces sustained changes in reflection habits or continuous learning, rendering the causal link between the proposed elements and claimed outcomes untestable as presented.
minor comments (1)
- The abstract uses several compound terms (e.g., 'inclusive learning analytics with embedded data comics') without initial definitions or references to foundational literature on data comics or learning analytics, which could be clarified for readers outside the subfield.
Simulated Author's Rebuttal
We thank the referee for the constructive feedback on our conceptual framework paper. The comments correctly identify areas where the abstract could more explicitly convey the framework's mechanisms and testability. We address each point below and indicate planned revisions.
read point-by-point responses
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Referee: Abstract: The central claim that embedding data comics will 'foster public empathy and introspection' and encourage 'reflection on AI ethics incidents from multiple perspectives' is asserted without any concrete design principles, examples of comic structures targeting specific biases, or mechanisms linking story-based transformation to measurable empathy gains.
Authors: We agree that the abstract summarizes outcomes at a high level without specifying mechanisms. In the revision we will expand the abstract to include concrete design principles (e.g., sequential narrative panels that contrast biased and unbiased decision paths to counter confirmation bias) and a brief example of a comic structure for an AI ethics incident, together with the proposed link from perspective-taking stories to measurable empathy via validated scales. revision: yes
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Referee: Abstract: The description of 'inclusive learning analytics' that 'targets not only people of different demographic attributes, but also different mindsets with inherent cognitive biases' provides no operational definition, accessibility guidelines for low-literacy audiences, or criteria for reaching high-bias groups, leaving the inclusivity mechanism underspecified.
Authors: We acknowledge the abstract's description is underspecified. The revision will add a concise operational definition of inclusive learning analytics, accessibility guidelines (simplified vocabulary, icon-based navigation, and audio narration for low-literacy users), and explicit criteria such as adaptive content selection based on pre-assessed cognitive bias profiles to reach high-bias groups. revision: yes
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Referee: Abstract: No evaluation criteria, pilot study design, or falsification plan is outlined to test whether the framework produces sustained changes in reflection habits or continuous learning, rendering the causal link between the proposed elements and claimed outcomes untestable as presented.
Authors: As this is a conceptual framework proposal rather than an empirical study, the current manuscript does not contain an evaluation plan. To address the concern we will add a new section proposing evaluation criteria (pre/post empathy and reflection-habit scales plus longitudinal engagement logs), a pilot study design with stratified sampling across demographics and mindsets, and falsification criteria (e.g., absence of sustained reflection-habit change after six months). revision: yes
Circularity Check
Conceptual framework proposal exhibits no circularity
full rationale
The manuscript is a review-style conceptual proposal that outlines a framework combining inclusive learning analytics and data comics to address public understanding of AI ethics. It relies on general principles from visualization, education, and ethics literature without any equations, parameter fitting, predictive derivations, or load-bearing self-citations. Claims about fostering empathy and continuous learning are presented as intended outcomes of the proposed approach rather than results reduced to inputs by construction. No self-definitional steps, fitted inputs renamed as predictions, or uniqueness theorems imported from prior author work appear in the derivation chain.
Axiom & Free-Parameter Ledger
axioms (2)
- domain assumption Data comics can transform complex and abstract AI ethics cases into compelling and relatable stories that foster public empathy and introspection.
- domain assumption Inclusive learning analytics can effectively target people with different demographic attributes and mindsets that include inherent cognitive biases.
Reference graph
Works this paper leans on
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[1]
1 Inclusive Learning Analytics with Embedded Data Comics: A Conceptual Framework for Public Understanding of AI Ethics Mengyi Wei 1*, Chenyu Zuo2, Dongsheng Chen1, Liqiu Meng1 1Chair of Cartography and Visual Analytics, Technical University of Munich 2 Department of Civil, Environmental and Geomatic Engineering, ETH Zurich ABSTRACT Public awareness of AI ...
work page 2017
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[2]
because low public awareness of AI ethics can easily trigger deep-rooted social risks, such as the invisible erosion of individual rights and the absence of a foundation for regulatory public opinion (Douglas et al. 2024). However, the complexity of AI ethics and the limitations of the public’s knowledge make it difficult to raise awareness. The complexit...
work page 2024
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[3]
Two aspects of inclusive learning analytics 2.2 Data Comics Applying inclusive learning analytics to enhance public awareness of AI ethics requires an appropriate medium. As narrative beings, humans tend to make sense of their lives, construct their worlds, and connect with others through storytelling (Frank and Seale 1996; Baldwin 2005). Data comics are ...
work page 1996
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[4]
Cape Hellas and the extent of British advances at the end of the campaign (Moore and Cartwright 2014). 2.3 The conceptual framework We propose to combine data comics with inclusive learning analytics in a new conceptual framework (ILA-DC) (Figure
work page 2014
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[5]
for two main reasons. First, the specific learning barrier associated with complex AI ethics knowledge makes it difficult for the general public to understand the risks posed by AI technology. By combining data comics with a dashboard interface, real-world AI ethics events can be presented as engaging narratives. This can strengthen the connection between...
work page 2013
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[6]
The conceptual framework of inclusive learning analytics for studying AI incidents. 3 POTENTIALS OF THE ILA-DC 3.1 Data comics for promoting dynamic reflection on the real-world AI ethics. News of real-world AI ethics cases serve to challenge techno-utopian narratives and facilitate the materialization of legal and ethical governance structures. Currently...
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[7]
FUTURE IMPLICATIONS OF THE ILA-DC FRAMEWORK Enhancing public understanding of AI ethics, from passive acceptance to active comprehension, is a critical prerequisite for ensuring that artificial intelligence technologies are deployed in society in a responsible and beneficial manner (Figure 8). As AI becomes an integral part of everyday life, the public is...
work page 2025
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[8]
(Figure 8). The presence of "ordinary person" characters in the comics – such as a user who inadvertently exposes his privacy or a student misjudged by an algorithm - serves as a proxy for the audience. These characters are relatable, emotionally engaging, and situated in specific contexts, enabling readers to analogize their behavior with the experiences...
work page 2017
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[9]
or public psychology (Rempel et al. 2018), we use real-world AI ethics incidents as the data source, decompose complex AI ethics events into representative scenarios for inclusive learning, which serves as the foundation for creating data comics. By embedding data comics in inclusive learning analytics, we seek to promote public understanding of AI ethics...
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[10]
Policy Design and Practice 2:215–228. https://doi.org/10.1080/25741292.2019.1621032 Kuhl J, Beckmann J (2012) Action Control: From Cognition to Behavior. Springer Science & Business Media Machado H, Silva S (2025) Ethical Assemblages of Artificial Intelligence: Controversies, Uncertainties, and Networks. Springer Nature Machado H, Silva S, Neiva L (2025) ...
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[11]
Humanit Soc Sci Commun 12:1–13. https://doi.org/10.1057/s41599-025-04469-9 Williams LC (2013) Graphic medicine | 8 | The portrayal of illness in underground and aut. https://www.taylorfrancis.com/chapters/edit/10.4324/9780203079614-8/graphic-medicine-ian-williams. Accessed 10 Sept 2024 Zhao Z, Marr R, Elmqvist N (2015) Data Comics: Sequential Art for Data...
discussion (0)
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