<![CDATA[This study developed and evaluated a computer simulation–based Internet of Things (IoT) lecture module for the Artificial Intelligence for Science Education course to address the gap between abstract IoT concepts and classroom practice in science teacher education. Adopting an R&D approach with the ADDIE model, the module was structured into five subtopics aligned with explicit learning outcomes and supported by simulations, examples, and authentic projects. Expert validation (five validators: content, media, instructional, and evaluation) examined content, language, and presentation using Aiken’s V and Percentage of Agreement. Practicality was assessed via expert questionnaires (ease of use, attractiveness, usefulness, contextual relevance). Implementation involved a trial with 40 prospective science teachers, including a 5-point Likert readability survey. Content validity increased from 0.860 (valid) to 0.960 (very valid), and inter-rater reliability rose from 0.712 (fair) to 0.757 (good) after systematic revisions. Language validity improved from 0.833 to 0.950 (very valid); presentation remained strong at 0.893 (very valid) with qualitative enhancements to visual design. Expected practicality averaged 92% across dimensions (ease of use 91%; attractiveness 92%; usefulness 92%; contextual relevance 92%). Readability scores were consistently “highly readable” across seven dimensions (4.44–4.56), indicating clear language and style, logical sequencing, effective multimodal supports, manageable cognitive load, strong contextual relevance, and robust technical accessibility. The module is ready for classroom adoption, offering a scalable template for integrating IoT through simulations and authentic cases in preservice teacher education.]]>
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