Implementation of Project-Based Learning Using Generative AI for Designing Environmental Research Posters
DOI:
https://doi.org/10.70609/g-tech.v10i2.9449Keywords:
Generative AI, Project-Based Learning, Environmental Education, Scientific PosterAbstract
This study aims to analyze the effectiveness of Project-Based Learning (PBL) integrated with Generative Artificial Intelligence (AI) in improving students’ ability to design environmental research posters. The background of this study is the limited ability of students to present research data in a clear, structured, and visually engaging manner. The research applied two approaches: an adapted Waterfall model to guide the development of AI-assisted scientific posters, and a one-group pre-test and post-test design to measure learning effectiveness. The study involved 8 students from the Plantation Management Study Program. Data were collected through pre-test and post-test, poster assessment rubrics, and student perception questionnaires. The results showed a significant improvement in student performance, with the average score increasing from 32 to 78. Statistical analysis using the Wilcoxon Signed-Rank Test indicated a significant difference between pre-test and post-test scores (Z = -2.52; p = 0.012 < 0.05), confirming the effectiveness of the implemented learning model. Poster results showed good performance, while students reported positive perceptions of AI in usefulness, creativity, and motivation. These findings indicate that Generative AI in PBL improves data presentation and scientific communication skills.
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