Using artificial intelligence teaching assistants to guide students in solar energy engineering design

Abstract

Engineering projects, such as designing a solar farm that converts solar radiation shined on the Earth into electricity, engage students in addressing real-world challenges by learning and applying geoscience knowledge. To improve their designs, students benefit from frequent and informative feedback as they iterate. However, teacher attention may be limited or inadequate, both during COVID-19 and beyond. We present Aladdin, a web-based computer-aided design (CAD) platform for engineering design with a built-in artificial intelligence teaching assistant (AITA). We also present two curriculum units (Solar Energy Science and Solar Farm Design), where students explore the Sun-Earth relationship and optimize the energy output and yearly profit of a solar farm with the help of the AITA. We tested the software and curriculum units with over 100 students in two Midwestern high schools. Pre- and post-survey data showed improvements in understanding of science concepts and self-efficacy in engineering design. Pre-post analysis of design performance gains reveals that AI helped lower achievers more than higher achievers. Interviews revealed students’ values and preferences when receiving feedback. Our findings suggest that AITAs may be helpful as an additional feedback mechanism for geoscience and engineering education. Future efforts should focus on improving the usability of the software and providing multiple types of feedback to promote inclusive and equitable use of AI in education.

Keywords:

• Engineering design
• artificial intelligence
• renewable energy
• technology enhanced learning

Acknowledgements

The authors are indebted to the assistance of science teachers DK and RC, who allowed us to conduct research in their classrooms and implement the curriculum. The authors would also like to thank Dr. Alex Barco and Isaac Lyss-Loren for their contributions to data collection and analysis. This work was supported by the National Science Foundation (NSF) under grant numbers 2105695, 2131097, and 2301164. Any opinions, findings, and conclusions or recommendations expressed in this paper, however, are those of the authors and do not necessarily reflect the views of the NSF.

Disclosure statement

The authors report there are no competing interests to declare.