التصدي للمعلومات المضللة المنشأة بواسطة الذكاء الاصطناعي التوليدي: نتائج تحليل تجميعي للأدلة العلمية

The report finds that text-based generative AI misinformation poses a higher persuasive risk than visual misinformation. It identifies a significant knowledge gap due to research focusing primarily on high-income, English-speaking countries. Among countermeasures, preemptive corrective information is the most consistent and effective intervention, while content labeling shows promising but inconsistent results depending on design and application.

• Text-based misinformation generated by generative AI is currently more persuasive and poses a greater risk than visual misinformation. While deepfakes are often perceived as less credible than real content, AI-generated text is frequently seen as more accurate and credible than true information.
• There is a substantial global knowledge gap because existing research is heavily concentrated in high-income, English-speaking nations such as the US, UK, Germany, Austria, the Netherlands, and South Korea. Less than five percent of screened publications were in languages other than English.
• Preemptive corrective information—such as brief educational warnings or reminders—is the most consistently effective intervention for reducing the perceived accuracy and credibility of AI-generated misinformation. This effect is most pronounced when individuals are asked to evaluate the content's credibility rather than simply detect the misinformation.
• Content labeling (using visual or text tags to indicate AI generation) can reduce the perceived credibility of misinformation, but its effectiveness is inconsistent and highly dependent on the specific design, wording, and application of the labels.

APA

Herasimenka, A., Valenzuela, S., Boulianne, S., Esser, F., Given, L. M., Lewandowsky, S., Navarro-López, E. M., & Howard, P. N. (2026). التصدي للمعلومات المضللة المنشأة بواسطة الذكاء الاصطناعي التوليدي: نتائج تحليل تجميعي للأدلة العلمية. International Panel on the Information Environment. https://cdn.prod.website-files.com/643ecb10be528d2c1da863cb/6a3b8d5ddd2fbb0a215c5d7a_SFP%20AR%20FINAL.pdf

Chicago

Herasimenka, A., S. Valenzuela, S. Boulianne, F. Esser, L. M. Given, S. Lewandowsky, E. M. Navarro-López, and P. N. Howard. التصدي للمعلومات المضللة المنشأة بواسطة الذكاء الاصطناعي التوليدي: نتائج تحليل تجميعي للأدلة العلمية. International Panel on the Information Environment, 2026. https://cdn.prod.website-files.com/643ecb10be528d2c1da863cb/6a3b8d5ddd2fbb0a215c5d7a_SFP%20AR%20FINAL.pdf.

Wikipedia

{{cite report |last1=Herasimenka |first1=A. |last2=Valenzuela |first2=S. |last3=Boulianne |first3=S. |last4=Esser |first4=F. |last5=Given |first5=L. M. |last6=Lewandowsky |first6=S. |last7=Navarro-López |first7=E. M. |last8=Howard |first8=P. N. |title=التصدي للمعلومات المضللة المنشأة بواسطة الذكاء الاصطناعي التوليدي: نتائج تحليل تجميعي للأدلة العلمية |publisher=International Panel on the Information Environment |date=2026 |url=https://cdn.prod.website-files.com/643ecb10be528d2c1da863cb/6a3b8d5ddd2fbb0a215c5d7a_SFP%20AR%20FINAL.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{herasimenka2026, author = {Herasimenka, A. and Valenzuela, S. and Boulianne, S. and Esser, F. and Given, L. M. and Lewandowsky, S. and Navarro-López, E. M. and Howard, P. N.}, title = {{التصدي للمعلومات المضللة المنشأة بواسطة الذكاء الاصطناعي التوليدي: نتائج تحليل تجميعي للأدلة العلمية}}, institution = {International Panel on the Information Environment}, year = {2026}, url = {https://cdn.prod.website-files.com/643ecb10be528d2c1da863cb/6a3b8d5ddd2fbb0a215c5d7a_SFP%20AR%20FINAL.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }