Developing University Students' Information Evaluation Competence Through Artificial Intelligence Tools

Generative artificial intelligence (AI) provides rapid explanations and summaries, but fluent output may contain unsupported claims, bias, or fabricated references. This conceptual paper examines how AI-supported learning can develop university students’ information evaluation competence. It argues that competence emerges from structured tasks requiring students to question claims, trace evidence, compare sources, detect limitations, and justify conclusions. A seven-stage instructional sequence is proposed for higher education, positioning the teacher as a designer of inquiry and ethical guidance within the digital transformation of higher education in Uzbekistan.