Innovating Chemical Education: Leveraging Artificial Intelligence and Effective Teaching Strategies to Enhance Public Engagement in Environmental and Organic Chemistry
Keywords:
Artificial Intelligence in Education, Organic Chemistry, Environmental Chemistry, Mixed-Methods Research, Public Engagement, Adaptive Learning, Machine Learning Algorithms, Educational TechnologyAbstract
This research examined the effects of evidence-based teaching strategies and artificial intelligence (AI) on learning outcomes and public involvement in the teaching of organic and environmental chemistry. Using a hybrid approach, we created an adaptive AI model that integrates important teaching techniques including inquiry-based learning (IBL), problem-based learning (PBL), and collaborative learning while tailoring information according to the participant's success. Over the course of 12 weeks, 300 participants—including high school students, college students, teachers, and members of the general public—participated in the research, which assessed both quantitative and qualitative data. Quantitative results demonstrated considerable gains in understanding (85% retention), a noteworthy 33.2% increase in high school students' comprehension, and an increase in the AI model's accuracy from 82% to 93%. With a 99.9% uptime and a quick reaction time (0.3 seconds), the AI model—which was created utilizing a variety of machine learning techniques—showed great flexibility. Core engagement themes were identified using thematic analysis, including real-world applications (78%) and individualized feedback (92%), as well as interactive learning (85%), enhancing comprehension and accessibility of difficult chemical ideas. 94% of participants were far more satisfied when organized teaching techniques were included into the AI framework, especially in collaborative and problem-solving settings. This research demonstrates how combining adaptive learning systems with successful teaching strategies may result in learning experiences that are powerful, accessible, and engaging. It also illustrates the revolutionary potential of AI in chemistry education. These results underline AI's potential for scalable, individualized teaching, with wider ramifications for public participation and scientific literacy.
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