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Communicating science in the age of GenAI: Can generative AI support the writing of better science communication products? - Leibniz-Institut für die Pädagogik der Naturwissenschaften und Mathematik
Effective science communication (SciComm) is crucial, but training scalability remains challenging. We explored whether generative AI (GenAI) could provide feedback to enhance SciComm strategies. In an online iterative distillation exercise, SciComm trainees (N = 78) condensed their research. An experimental group (n = 41) received jargon-oriented and GenAI feedback; controls (n = 37) received only jargon feedback. Participants preferred revised texts, with slightly higher preference in the GenAI group. SciComm-based rubric assessment revealed GenAI-supported texts significantly improved in SciComm strategies, particularly connecting science to everyday life and narrative use. Findings highlight GenAI’s potential to enhance SciComm content and scalable feedback, supporting its careful integration into training.
Originalsprache Englisch Erschienen in Science Communication, 48(3) Seiten 438–464 Herausgeber (Verlag) SAGE
Communicating science in the age of GenAI: Can generative AI support the writing of better science communication products? - Leibniz-Institut für die Pädagogik der Naturwissenschaften und Mathematik
https://www.leibniz-ipn.de/de/forschen/publikationen/communicating-science-in-the-age-of-genai
Effective science communication (SciComm) is crucial, but training scalability remains challenging. We explored whether generative AI (GenAI) could provide feedback to enhance SciComm strategies. In an online iterative distillation exercise, SciComm trainees (N = 78) condensed their research. An experimental group (n = 41) received jargon-oriented and GenAI feedback; controls (n = 37) received only jargon feedback. Participants preferred revised texts, with slightly higher preference in the GenAI group. SciComm-based rubric assessment revealed GenAI-supported texts significantly improved in SciComm strategies, particularly connecting science to everyday life and narrative use. Findings highlight GenAI’s potential to enhance SciComm content and scalable feedback, supporting its careful integration into training.
Originalsprache Englisch Erschienen in Science Communication, 48(3) Seiten 438–464 Herausgeber (Verlag) SAGE
A framework for learning from erroneous examples and meta-analysis of empirical research - Leibniz-Institut für die Pädagogik der Naturwissenschaften und Mathematik
While there is ample theoretical and empirical evidence detailing which conditions benefit learning from one’s own errors, the evidence on learning from others’ errors has not yet been synthesized. In this meta-analysis, we examine the overall impact of erroneous examples on learning and the effects of potential moderating variables based on a novel framework. Following the robust variance estimation method, we synthesized findings from 42 papers (177 effect sizes) comparing erroneous examples with correct examples or problem-solving in experimental studies. The results revealed a statistically significant but weak effect of erroneous examples on learning (g = .136). Further analysis indicated a statistically significant moderating effect of the design of error-explanation activities. Specifically, providing self-explanation prompts or instructional explanations enhanced learning from erroneous examples more than not providing any error explanations. Our findings draw attention to the design of error explanation activities as well as several areas for future research.
Narciss Originalsprache Englisch Erschienen in Review of Educational Research Herausgeber (Verlag) SAGE
