Using Lessons from History to Guide the Implementation of AI in Science Education

Abstract

The purpose of this position paper is to describe a historical timeline of science education. Using historical documents and current science education research, the authors create an evolutionary description of science education changes over time and how these shifts could influence how Artificial Intelligence (AI) is used in science education. The focus is on how societal and educational catalyst events, spanning from the Industrial Revolution to the Next Generation Science Standards (NGSS) and the COVID-19 pandemic, influenced science education. Next, the authors suggest that teachers should meaningfully implement the use of AI in ways that focus on student-centered learning and restore the progress made by the K-12 Framework and NGSS. These include generating ideas about problems that students can solve in an interest area, analyzing large sets of real-world data, generating grade appropriate science readings to develop background knowledge, and using AI to grade unique student work to replace multiple-choice response exams. AI and science education may best be described by a Chat GPT response: “It’s important to note that while AI can enhance science education, it should not replace human teachers. Instead, it should be used as a tool to augment and support their expertise, fostering a blended learning environment that combines the benefits of technology with human guidance and mentorship.”

Keywords:

• History of Science Education
• Artificial Intelligence
• student-centered learning
• A Framework for K-12 Science Education
• Next Generation Science Standards (NGSS)
• Committee of Ten
• A Nation at Risk
• No Child Left Behind (NCLB)
• Rising Above the Gathering Storm and STEM Education
• COVID-19

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

SUPPLEMENTAL MATERIAL

Supplemental data for this article can be accessed online at http://dx.doi.org/10.1080/00368555.2024.2308315.

Additional information

Notes on contributors

Aria Hadley-Hulet

Aria Hadley-Hulet ([email protected]) (ORCID: https://orcid.org/0009-0001-6868-7748) is a science education doctoral student at Utah State University, as well as science department head and chemistry teacher at Desert Hills High School. Her research interests include teacher ownership of teaching practices and the experiences of students with learning disabilities in science education. Marc Ellis (ORCID: https://orcid.org/0009-0000-8993-8879) is a science education doctoral student at Utah State University and a science teacher at Glendale Middle School. His research interests include equity in middle school science and use of AI in science lessons. Austin Moore (ORCID: https://orcid.org/0009-0007-8116-606X) is a science education doctoral student at Utah State University and a science teacher at both Wayne Middle School and Wayne High School. His research interests include elementary science education, rural science education, and professional learning for rural teachers. Emily Lehnardt (ORCID: https://orcid.org/0009-0005-0439-0418) is a science education doctoral student at Utah State University, as well as NASA ambassador, director of the Utah Women Astronomical Society, and science and STEM Junior High teacher. Her research interests include STEM self-efficacy among upper elementary teachers. Dr. Max Longhurst (ORCID: https://orcid.org/0000-0003-3227-800X) is Associate Professor of Professional Practice at Utah State University. His research interests include professional learning and appropriation.