Integrating Generative Artificial Intelligence into Inquiry-Based Science Learning: A Case Study with the STEAM Baseball Robot [ID 7]
This study explores the integration of Generative Artificial Intelligence (GenAI) into robotics programming education to enrich inquiry-based science learning, particularly in the STEAM (Science, Technology, Engineering, Arts, and Mathematics) domains, with a focus on its impact on elementary science education. Through hands-on STEAM activities, students enhance problem-solving skills, collaboration, and develop a strong interest in science learning. Utilizing Scratch, a free and open programming language, students not only learn programming basics but also deepen their understanding and application of scientific concepts. The research targets elementary school students, incorporating technology, mathematics, and physical education into a series of STEAM education experiments. Results underscore the importance of open educational resources in supporting STEAM education, enhancing scientific learning, fostering creativity, and teamwork, thereby positively influencing educational equity and quality. The example of a baseball robot illustrates the potential benefits and challenges of utilizing open educational resources.
Inquiry-based science learning encourages questioning, investigation, and knowledge construction through exploration and experimentation. Recent advancements in Artificial Intelligence, especially Generative AI (GenAI), offer novel tools to enhance this educational approach. This paper examines how integrating GenAI can enrich the learning experience, focusing on a STEAM project involving the design and implementation of a baseball robot.
Utilizing the 6E experiential learning model, GenAI assumes multiple roles across the learning stages. Initially, in the Engage phase, GenAI acts as a catalyst, captivating student interest through Scratch, thereby igniting curiosity. In the subsequent Explore phase, GenAI transitions into a mentor, providing tailored learning pathways and resources, facilitating guided exploration. As the learning progresses into the Explain phase, GenAI transforms into an instructor, simplifying intricate concepts and theories through textual content. During the Engineer phase, GenAI serves as a design assistant, assisting students in utilizing tools like LEGO SPIKE for project development. Moving forward to the Enrich phase, GenAI becomes an inspiration, expanding students’ knowledge and fostering interdisciplinary integration and innovative thinking. Finally, in the Evaluate phase, GenAI transitions into an assessor, delivering real-time feedback and assessments to aid students and teachers in reviewing and reflecting on learning outcomes. GenAI plays a crucial role in scientific inquiry activities, offering expertise, guidance, and support throughout the project phases, thereby enriching students’ learning experiences and fostering knowledge exchange in STEAM fields.
The combination of GenAI and Open Educational Resources (OER) in STEAM education enhances learning by personalizing pathways, improving accessibility, and ensuring quality education for all. This model fosters students’ passion for science and technology, enhances problem-solving skills, and cultivates future innovators. It demonstrates the potential of Generative AI in modern education, emphasizing the importance of open education in global learning initiatives.
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Author Keywords
STEAM, Programming Education, Exploratory Learning, Generative Artificial Intelligence, Computational Thinking
Session Details
Format: Lightning Talk
Presenter(s): SHENG WEN CHUANG, HUI-CHUN HUNG
Brisbane Time: November 13, 3:10 PM → 3:25 PM AEST
Your Local Time: →
Room: P4
Topic Area: Digital Capability,Artificial Intelligence
Sched: View in conference schedule
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