TY - JOUR
T1 - How to make sense of generative AI as a science communication researcher? A conceptual framework in the context of critical engagement with scientific information
AU - Klein-Avraham, Inbal
AU - Greussing, Esther
AU - Taddicken, Monika
AU - Dabran-Zivan, Shakked
AU - Jonas, Evelyn
AU - Baram-Tsabari, Ayelet
N1 - Publisher Copyright:
© The Author(s). This article is licensed under the terms of the Creative Commons Attribution — NonCommercial — NoDerivativeWorks 4.0 License. All rights for Text and Data Mining, AI training, and similar technologies for commercial purposes, are reserved. ISSN 1824–2049. Published by SISSA Medialab. jcom.sissa.it
PY - 2024/1/1
Y1 - 2024/1/1
N2 - A guiding theory for a continuous and cohesive discussion regarding generative artificial intelligence (GenAI) in science communication is still unavailable. Here, we propose a framework for characterizing, evaluating, and comparing AI-based information technologies in the context of critical engagement with scientific information in online environments. Hierarchically constructed, the framework observes technological properties, user experience, content presentation, and the context in which the technology is being used. Understandable and applicable for non-experts in AI systems, the framework affords a holistic yet practical assessment of various AI-based information technologies, providing both a reflection aid and a conceptual baseline for scholarly references.
AB - A guiding theory for a continuous and cohesive discussion regarding generative artificial intelligence (GenAI) in science communication is still unavailable. Here, we propose a framework for characterizing, evaluating, and comparing AI-based information technologies in the context of critical engagement with scientific information in online environments. Hierarchically constructed, the framework observes technological properties, user experience, content presentation, and the context in which the technology is being used. Understandable and applicable for non-experts in AI systems, the framework affords a holistic yet practical assessment of various AI-based information technologies, providing both a reflection aid and a conceptual baseline for scholarly references.
KW - Informal learning
KW - Public engagement with science and technology
KW - Science communication: theory and models
UR - http://www.scopus.com/inward/record.url?scp=85206077785&partnerID=8YFLogxK
U2 - 10.22323/2.23060205
DO - 10.22323/2.23060205
M3 - Article
AN - SCOPUS:85206077785
SN - 1824-2049
VL - 23
JO - Journal of Science Communication
JF - Journal of Science Communication
IS - 6
M1 - A05
ER -
