<![CDATA[This study aimed to investigate to investigate learning needs and establish specifications for an interactive web-based learning medium using Canva and deep learning technology for a university Food Chemistry course, with a focus on fish preservation. A qualitative needs analysis was conducted through semi-structured interviews with a Food Chemistry lecturer selected through purposive sampling, along with document analysis of the Semester Learning Plan (RPS) and course instructional materials. Two key findings were identified. First, existing learning resources, including textbooks, e-books, and PowerPoint presentations, provided limited support for the development of Higher-Order Thinking Skills (HOTS). Document analysis further revealed that fish preservation was not explicitly addressed in the RPS and that available learning media lacked interactive and visual elements. Second, interview findings indicated a strong need for contextual and engaging learning materials that connect course content with real-world applications. Based on these findings, specifications were formulated for an interactive web-based learning platform using Canva and incorporating a proposed CNN-based fish freshness detection feature that utilizes fish eye characteristics as visual indicators of freshness. The findings suggest that such a platform has the potential to enhance student engagement and support the development of HOTS through authentic learning experiences. This study provides an empirically informed framework for integrating contextual learning, interactive web media, and artificial intelligence in Food Chemistry education.]]>
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