Inclusive Learning Analytics with Embedded Data Comics: A Conceptual Framework for Public Understanding of AI Ethics

2026-04-24

AI Summary

This study addresses the public’s limited awareness of AI ethics risks and the absence of inclusive, effective educational mechanisms. It proposes the first integrative framework that combines data comics with inclusive learning analytics to translate complex AI ethics issues into accessible, empathetic narratives. Designed to account for diverse demographic characteristics and cognitive biases, the framework enhances both the accessibility of public understanding and the depth of critical reflection. By innovatively merging data visualization with learning analytics, this work not only advances methodological approaches to public engagement but also establishes a theoretical foundation and practical pathway for fostering broader participation in AI ethics discourse and elevating societal AI literacy.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

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.

Problem

• AI ethics
• public understanding
• inclusive learning
• cognitive biases
• ethical risks

Innovation

• inclusive learning analytics
• data comics
• AI ethics
• public understanding
• cognitive bias