Instructional Design of E-Modules and E-Courses for AI Literacy: Enhancing Conceptual, Critical, and Ethical Understanding
Keywords:
Development Research, E-course, E-module, Literacy AI, StudentsAbstract
The rapid development of artificial intelligence (AI) in higher education has created an urgent need to strengthen students’ AI literacy, particularly in understanding foundational concepts, critically evaluating AI outputs, and applying ethical considerations. This study aims to analyze students’ AI literacy skills and to develop e-modules and e-courses designed to enhance their conceptual, critical, and practical competencies. A design-based research approach was employed, involving needs analysis through interviews, instructional design development, expert validation, and implementation testing with student users. Data were collected through qualitative interviews, expert review sheets, and user response questionnaires, and were analyzed using systematic reduction, interpretation, and triangulation techniques. The findings reveal that students predominantly possess functional rather than conceptual or ethical AI literacy. They frequently rely on AI tools without adequate prompt construction, verification strategies, or awareness of potential algorithmic bias. The developed e-module and e-course received high validation scores, demonstrating strong content quality, effective structure, and high levels of interactivity, particularly in the video-based e-course. User responses further indicate that digital learning materials significantly improved students’ understanding of AI fundamentals and responsible use. This study emphasizes the importance of higher education institutions incorporating AI literacy into their curricula through structured digital learning resources and explicit ethical guidelines for AI-assisted academic work. Limitations include the small interview sample, restricted institutional context, and absence of longitudinal effectiveness testing. Future research should involve larger participant groups and long-term evaluation to strengthen the generalizability and sustainability of the model.
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