Journal of Artificial Intelligence and Science Communication

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AI Literacy as Science Communication: Building Public Understanding through Pedagogical Innovation

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https://doi.org/10.54963/jaisc.v1i1.2228
Volume 1 Issue 1 (2025)
Copyright (c) 2025 Dongxing Yu
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This work is licensed under a Creative Commons Attribution 4.0 International License.

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Journal of Artificial Intelligence and Science Communication (JAISC)  publishes accepted manuscripts under Creative Commons Attribution 4.0 International (CC BY 4.0). Authors who submit their papers for publication by Journal of Artificial Intelligence and Science Communication (JAISC) agree to have the CC BY 4.0 license applied to their work, and that anyone is allowed to reuse the article or part of it free of charge for any purpose, including commercial use. As long as the author and original source is properly cited, anyone may copy, redistribute, reuse and transform the content.

Authors

  • Dongxing Yu

    Institute for Sustainable Development in Education, School of Education, Sanda University, Shanghai 201209, China

Received: 31 March 2025; Revised: 5 June 2025; Accepted: 16 July 2025; Published: 17 September 2025

As artificial intelligence (AI) systems become embedded in everyday communication, education, and decision-making, public AI literacy has become a pressing science communication challenge. This paper reconceptualizes AI literacy as a science communication task rather than a narrowly technical educational objective. Using a scoping-review-informed synthesis of interdisciplinary literature, the manuscript integrates scholarship from AI literacy, science communication, informal learning, and educational technology to derive the COMMUNICATE framework. The review drew on iterative searches of Google Scholar, Scopus-indexed sources available to the author, and backward reference tracing. Sources were included when they addressed AI literacy frameworks, public engagement and science communication theory, or empirical findings relevant to pedagogy, trust, participation, and AI-supported learning design. The resulting framework organizes eleven principles: Contextualized understanding, Open dialogue, Multimodal representation, Meaning-making, Universal accessibility, Narrative engagement, Interactive exploration, Critical evaluation, Adaptive scaffolding, Transformative learning, and Ethical reflection. The paper argues that a science communication perspective adds value by foregrounding audience diversity, public trust, dialogic participation, and interpretive context, which are often underdeveloped in technically oriented AI literacy models. The manuscript concludes by outlining practical implications for educators, communicators, and policymakers, while explicitly acknowledging the framework's conceptual status and the need for future empirical validation across formal and informal settings.

Keywords:

AI Literacy Science Communication Public Understanding of Science Pedagogical Innovation Generative AI Visualization Public Engagement

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