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Developing Critical Algorithmic Literacy Through the Analysis of Historical Revisionism in Generative Artificial Intelligence Outputs

Leyla KholmuradovaAssociate Professor, Samarkand State Institute of Foreign Languages Samarkand , UzbekistanDilsoraxon AmanbayevaAndijan Institute of AgriculturalGulxan JUMAYEVAJizzakh State Pedagogical University Jizzakh , UzbekistanGulchehra MuminovaNational University of UzbekistanEleonora KhamdamovaSenior Lecturer , Samarkand State Medical University Samarkand , UzbekistanShakhnoza AyupovaLecturer, Tashkent State Medical University , Tashkent , UzbekistanSokhiba DusaevaDepartment of English Language and Literature , Gulistan State University , Gulistan , Uzbekistan
2026
ABI

Annotatsiya

GenAI technology is broadly used in practice within education, science, and information distribution in general, although occasionally GenAI systems have factual errors, unsupported claims, omissions, biases, and distorted representations of history. The present research paper emphasizes the importance of the critical algorithmic literacy skill for Uzbek university students regarding the problem of historical revisionism in GenAI products. It should be noted that within this paper, the quasi-experimental method was chosen with the usage of the experimental and control groups. While students participating in the experimental group were trained in developing their skills to be critically literate towards algorithms in terms of verifying the truthfulness of claims, comparing sources, identifying bias, understanding the context, and evaluating the ethical considerations, the control group studied digital literacy in general. In addition, a set of 50 historical prompts regarding the history of Uzbekistan, Central Asia, and the world was created, resulting in 150 GenAI-generated narratives analyzed using such indicators as factual inaccuracy, omission, unsubstantiated assertion, narrative falsification, weak basis in sources, and cultural re-framing. According to the results, the experimental group progressed from 52.4% to 84.7%, while the control group increased its performance scores from 51.8% to 63.2%. As for indicator-based scores, a high improvement in skills to verify factual information (54.1% vs. 86.5%) and identify biases in the presented information (50.8% vs. 83.9%) was demonstrated. This study indicates that analysis of outputs generated by GenAI may aid learners in transitioning from passive AI utilization to critical judgment and ethical usage of AI. The suggested model is applicable for AI-supported education in Uzbekistan due to cultural sensitivity toward the examination of historical facts.

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