EMPOWERING PRIMARY EDUCATORS IN THE AI ERA: AN EVALUATION OF GEMINI AI TRAINING SATISFACTION AT AN-NIZAM PRIMARY SCHOOL
Keywords
Gemini AI, Prompt Engineering, Task-Based Language Teaching (TBLT), Deep Learning, Teacher SatisfactionAbstract
The rapid development of Generative Artificial Intelligence (GenAI) has transformed primary education by offering solutions to administrative burdens and creating new opportunities for innovative instructional design. This study aimed to measure the professional satisfaction and perceived usefulness of an intensive Gemini AI prompt engineering training and mentoring program among 23 homeroom teachers at SD An-Nizam. The program focused on utilizing structured prompting techniques to create Deep Learning materials based on the Task-Based Language Teaching (TBLT) approach, specifically embodying Mindful, Meaningful, and Joyful (MMJ) principles. By applying specialized tools such as Nano Banana for educational illustrations, Gemini Canvas for task-based classroom games, and Deep Research for robust lesson planning (RPP), the training guided teachers in building a sustainable Prompt Library. Using a descriptive survey design with a 5-point Likert scale and open-ended questions, the results showed a highly positive reception toward the training content and instructor quality. A large majority of teachers (19 respondents) reported feeling significantly more confident in applying Generative AI-driven instructional design. Qualitative feedback highlighted that specific tool like Nano Banana effectively simplified complex concepts for younger students by combining text and vibrant images. The study concludes that this training successfully improved educators' prompt engineering skills and instructional efficiency, providing a useful blueprint for future professional development that incorporates peer-mentoring and localized AI integration in primary schools. While these results are promising, the study is limited by its small, single-site sample size and a focus on immediate outcomes rather than long-term pedagogical shifts. Nevertheless, these findings establish a foundation for larger-scale implementations, emphasizing that sustainable AI adoption in early education depends on human-centered support and context-specific curriculum design.
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