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A Study on Pathway Development for Evidence-Based Science Communication under the AI-Hallucination Challenge
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Xi Cheng1, 2, 3, Yue Su1, 2, 3, Liting Wu1, 2, 3, Haoran Wang4
Studies on Science Popularization | 2026, 21(2) : 106 - 114
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Studies on Science Popularization | 2026, 21(2): 106-114
Science Communication
A Study on Pathway Development for Evidence-Based Science Communication under the AI-Hallucination Challenge
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Xi Cheng1, 2, 3, Yue Su1, 2, 3, Liting Wu1, 2, 3, Haoran Wang4
Affiliations
  • 1School of Communication, Soochow University, Suzhou 215000
  • 2Center for Science Communication Research, Soochow University, Suzhou 215000
  • 3International Joint Laboratory for Science Communication Research, Soochow University, Suzhou 215000
  • 4Department of Computer Science, Western University, Ontario, Canada N6A 3K7
Published: 2026-04-20 doi: 10.19293/j.cnki.1673-8357.2026.02.011
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As an approach grounded in objective facts and systematic research evidence, evidence-based science communication holds potential advantages in mitigating Artificial Intelligence (AI) hallucinations. From the perspective of evidence-based science communication, this study analyzes the dual relationship between AI and evidence-based communication. (1) The empowering role of AI in evidence-based science communication: AI demonstrates enabling potential in stages such as data aggregation, evidence screening, and impact feedback, thereby enhancing the efficiency and reach of evidence-based communication. (2) The challenges posed by AI hallucinations to science communication: These challenges are reflected not only in content authenticity but also in the procedural legitimacy of knowledge production. They affect dimensions including content foundation, knowledge production, knowledge stability, and the efficiency of consensus-building, causing misinformation to escalate from isolated inaccuracies to structural errors. Based on these findings, this study proposes a human-AI collaboration pathway for evidence-based science communication, guided by the principles of evidentiality, traceability, adaptability, and publicness. By clarifying the AI-side intervention mechanisms and the key nodes of human-AI collaboration under each principle, this study constructs a comprehensive evidence-based science communication system spanning “content production—process control—pace regulation—impact feedback.”

AI hallucination  /  evidence-based science communication  /  knowledge production  /  Artificial Intelligence
Xi Cheng, Yue Su, Liting Wu, Haoran Wang. A Study on Pathway Development for Evidence-Based Science Communication under the AI-Hallucination Challenge[J]. Studies on Science Popularization, 2026 , 21 (2) : 106 -114 . DOI: 10.19293/j.cnki.1673-8357.2026.02.011
Year 2026 volume 21 Issue 2
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doi: 10.19293/j.cnki.1673-8357.2026.02.011
  • Receive Date:2025-09-15
  • Online Date:2026-07-28
  • Published:2026-04-20
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  • Received:2025-09-15
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Affiliations
    1School of Communication, Soochow University, Suzhou 215000
    2Center for Science Communication Research, Soochow University, Suzhou 215000
    3International Joint Laboratory for Science Communication Research, Soochow University, Suzhou 215000
    4Department of Computer Science, Western University, Ontario, Canada N6A 3K7
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表12种不同金属材料的力学参数

Family
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Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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