Integrating AI into Introductory Physics Labs: Enhancing Data Analysis and Student Learning
ORAL
Abstract
The extraordinary and rapidly advancing capabilities of artificial intelligence are transforming science and engineering, reshaping how we conduct experiments, analyze data, and interpret results. As educators, we face a dual challenge: preparing the next generation of scientists and engineers to harness AI effectively while ensuring that students develop strong foundations in physics and critical thinking.
This presentation explores how generative AI can be integrated into introductory physics labs to enhance student engagement and improve data analysis. We will showcase several hands-on experiments where students collect complex data sets using smartphones and analyze them with AI-driven tools. Examples include: 1) Data Visualization and Statistical Analysis: AI-assisted plotting and interpretation of large experimental data sets to help students identify patterns, assess uncertainties, and build statistical reasoning skills. 2) Numerical Integration of Sensor Data: Using AI to perform real-time integration of acceleration and angular velocity data, allowing students to connect kinematic equations with sensor-based motion tracking. 3) AI-Assisted Video Analysis: Leveraging machine learning models to extract spectroscopic information from video recordings of biological phenomena, such as blood flow and the cardiac cycle, making physics more relevant to students in health sciences. Each of these investigations can use AI to provide instant feedback, help students troubleshoot errors in their data, and guide them through complex calculations.
By incorporating AI tools into physics labs, we can reduce barriers to advanced data analysis, encourage deeper conceptual understanding, and prepare students for a future where AI is an essential part of scientific inquiry. Attendees will gain practical insights into how these methods can be implemented in their own courses, along with discussions on pedagogical strategies to ensure AI enhances—not replaces—critical thinking and problem-solving.
This presentation explores how generative AI can be integrated into introductory physics labs to enhance student engagement and improve data analysis. We will showcase several hands-on experiments where students collect complex data sets using smartphones and analyze them with AI-driven tools. Examples include: 1) Data Visualization and Statistical Analysis: AI-assisted plotting and interpretation of large experimental data sets to help students identify patterns, assess uncertainties, and build statistical reasoning skills. 2) Numerical Integration of Sensor Data: Using AI to perform real-time integration of acceleration and angular velocity data, allowing students to connect kinematic equations with sensor-based motion tracking. 3) AI-Assisted Video Analysis: Leveraging machine learning models to extract spectroscopic information from video recordings of biological phenomena, such as blood flow and the cardiac cycle, making physics more relevant to students in health sciences. Each of these investigations can use AI to provide instant feedback, help students troubleshoot errors in their data, and guide them through complex calculations.
By incorporating AI tools into physics labs, we can reduce barriers to advanced data analysis, encourage deeper conceptual understanding, and prepare students for a future where AI is an essential part of scientific inquiry. Attendees will gain practical insights into how these methods can be implemented in their own courses, along with discussions on pedagogical strategies to ensure AI enhances—not replaces—critical thinking and problem-solving.
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Presenters
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David Rakestraw
Lawrence Livermore National Laboratory
Authors
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David Rakestraw
Lawrence Livermore National Laboratory