Presentation Schedule
Online Multitext Health Information Search and Evaluation Among University Students: An Intervention and Analysis of Scaffolding Strategies (105051)
Tuesday, 24 March 2026 14:30
Session: Poster Session 2
Room: Orion Hall (5F)
Presentation Type: Poster Presentation
This study examines how a reflective scaffolding strategy supports university students’ multitext health information search and integration in an AI-enriched online environment. Using a one-group pretest–posttest design, the intervention was implemented within a course-based learning activity involving 32 undergraduate students over a short instructional period. Students conducted guided multitext health information searches supported by a self-check and reflection prompt form. Data sources included pre–post self-reported measures of multitext processing and health information literacy, as well as written reflections on search and synthesis strategies. Paired-sample t-tests and qualitative analysis were employed. Quantitative findings indicated consistently high levels of perceived competence across key multitext processing dimensions at both pretest and posttest (M = 3.9–4.3 on a 5-point scale), including identifying relevant information, detecting inconsistencies across sources, integrating complementary perspectives, and understanding task demands. However, no significant pre–post differences were found in reflective scaffolding checklist-based measures related to understanding and integrating multiple perspectives (p > .05). Qualitative analysis revealed increased awareness of systematic planning, source credibility evaluation, and intentional synthesis when navigating multiple online health texts. Students also reported a shift in their perceived roles when using AI tools, indicating a tendency to rely on ChatGPT or search engine–embedded AI for labor-intensive organizational tasks such as content integration and summarization, while positioning themselves as final evaluators responsible for judgment and verification. These findings suggest the need for further research on instructional approaches that support effective AI use without compromising essential critical evaluation and verification skills in AI-supported learning contexts.
Authors:
Wan-Chen Hsu, National Kaohsiung University of Science and Technology, Taiwan
About the Presenter(s)
Dr. Wan-Chen Hsu is an Assistant Professor at the Center for Teacher Education, College of General Education, National Kaohsiung University of Science and Technology, Taiwan. She holds a PhD in Adult Education from National Kaohsiung Normal University, Taiwan. Her research interests include higher education, health literacy, and teacher efficacy.
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