<![CDATA[This study examines student information-seeking behavior within an artificial intelligence-based digital ecosystem using the Ellis model as an analytical framework. Employing a qualitative descriptive approach, data were collected through semi-structured interviews, observation of chat histories, and document analysis involving eight undergraduate students of Library Science. The findings reveal that students systematically adapt all eight stages of the Ellis model when interacting with generative AI, particularly ChatGPT, indicating a structured and reflective search process. The integration of AI enhances efficiency, accessibility, and cognitive support, especially in generating initial understanding and structuring academic tasks. However, the study identifies critical challenges related to information accuracy, including AI hallucination, which necessitates rigorous verification practices. Students demonstrate adaptive verification strategies, although their consistency varies depending on task urgency and academic stakes. The findings contribute to theoretical enrichment by contextualizing classical information behavior models within AI-driven environments. Practically, the study highlights the need for strengthening digital literacy competencies and institutional interventions to ensure ethical and critical AI use in academic contexts.]]>
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