Leveraging ChatGPT for Developing Learning Object Material: A Multi-representation Approach to Teaching Water Pollution

Abstract

This study aims to explore the utilization of ChatGPT, in conjunction with Canva and Renderforest applications, to develop learning object material with a practical multi-representational approach. The objective is to create engaging and interactive educational content on water pollution, catering to diverse learning preferences and enhancing students' understanding of the subject. The study employs a mixed-methods research design, combining qualitative data from the developed learning object material with quantitative data gathered through a USE questionnaire. The research participants consisted of 23 seventh-grade students grouped into four teams who engaged in a 30-minute group learning session with the learning object material. Following the group activity, a 10-minute question-and-answer session addresses any queries. Subsequently, students individually attempt test questions in multiple-choice, matching, and true/false formats. The results indicate positive student responses to the learning object material, demonstrating its effectiveness in promoting water pollution awareness. Most students agree on the material's usefulness, ease of use, ease of learning, and satisfaction. The findings emphasize the potential of AI-driven content creation and its seamless integration with creative tools in transforming the educational landscape. The study recommends continued research and development in AI integration in education to enhance the application's performance, usability, and adaptability to cater to diverse learning needs and preferences. The positive impact of AI integration in education holds promise for fostering better understanding and promoting the transformation of traditional learning approaches into technology-driven.

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