在扩展的DigComp框架中,应用Delphi方法来预测面向人工智能驱动的虚假信息检测能力
Cristina M Arribas1, Rubén Arcos1, Manuel Gertrudix1
1Department of Audiovisual Communication and Advertising, University Rey Juan Carlos (ROR 01v5cv687), Camino del Molino 5, Fuenlabrada, Madrid 28942, Spain.
MethodsX
|June 23, 2025
概括
人工智能的进步为打击虚假信息和外国信息操纵和干扰 (FIMI) 创造了新的挑战. 这项研究确定了未来的趋势,并提出了培训模式,以建立必要的数字素养技能来应对这些不断变化的威胁.
科学领域:
- 信息科学 信息科学 信息科学
- 人工智能的人工智能
- 数字识字数字识字
背景情况:
- 人工智能 (AI) 能力正在迅速发展,为外国信息操纵和干扰 (FIMI) 创造了新的途径.
- 检测和应对人工智能驱动的虚假信息对信息完整性构成重大挑战.
- 数字素养被确定为对抗这些不断变化的威胁的关键预防策略.
研究的目的:
- 积极识别未来十年塑造虚假信息和FIMI格局的新兴技术趋势.
- 提出基于能力的培训和课程模式,以应对新技术带来的潜在挑战.
- 为识别人工智能和错误信息背景下的培训需求提供可复制的Delphi研究模型.
主要方法:
- 采用了德尔菲研究方法来收集专家对未来趋势和培训需求的共识.
- 研究设计是详细的,以促进复制和进一步研究.
- 提供了问卷和数据集,用于比较和纵向分析.
主要成果:
- 该研究概述了影响虚假信息和FIMI未来的关键技术趋势.
- 它提出了针对新出现的人工智能驱动威胁的特定基于能力的培训模式.
- 该研究为理解和解决未来培训需求提供了一个框架.
结论:
- 积极识别技术趋势对于制定有效的对策来打击虚假信息和FIMI至关重要.
- 基于能力的培训模式对于为个人提供必要的数字素养技能至关重要.
- 提供的研究模型和数据支持正在进行的研究和适应信息生态系统中的新技术挑战.
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