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Integrating Machine Learning and Color Chemistry: Developing a High-School Curriculum toward Real-World
Shiyan Jiang1, Jeanne McClure1, Hongjing Mao2
1Department of Teacher Education and Learning Sciences, North Carolina State University, 2310 Stinson Drive, Raleigh, NC, 27695, USA.
Journal of Chemical Education
|June 28, 2024
Summary
This study introduces an AI and chemistry curriculum for high schoolers, using machine learning (ML) to build a virtual pH meter. The engaging activity boosts student interest in science and its real-world applications.
Area of Science:
- Cross-disciplinary STEM education
- Artificial Intelligence and Chemistry Integration
- Machine Learning Applications in Science Education
Background:
- Artificial intelligence (AI) necessitates early education on its benefits and challenges.
- High school curricula require innovative approaches to integrate emerging technologies like AI.
- Analytical chemistry concepts, such as pH measurement, can serve as accessible entry points for complex scientific principles.
Purpose of the Study:
- To develop and evaluate a cross-disciplinary curriculum connecting AI and chemistry for high school students.
- To foster student interest and engagement in analytical chemistry and machine learning.
- To demonstrate the practical application of AI in scientific problem-solving through a hands-on activity.
Main Methods:
- Leveraged machine learning (ML), a subset of AI, using the codeless software Orange.
- Developed an ML-based virtual pH meter activity for high school students with basic science backgrounds.
- Students used pH strips to collect data on color changes and built ML neural network models to predict pH values.
Main Results:
- The integrated curriculum successfully increased student interest in both chemistry and ML.
- Students demonstrated an understanding of the relevance of science to their daily lives and global issues.
- The activity provided a transformative approach to integrating chemistry and ML concepts.
Conclusions:
- Connecting AI and chemistry through practical ML applications enhances high school science education.
- Hands-on activities using accessible tools like Orange can demystify AI and ML for students.
- This curriculum model offers a scalable framework for interdisciplinary STEM learning and highlights the global impact of scientific innovation.
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