Incorporating Generative Artificial Intelligence into Engineering Writing Courses
An NSF-Funded Grant at the University of California, Irvine
Senior Personnel
Tamara Tate (Principal Investigator)
Beth Harnick-Shapiro (Co-Principal Investigator)
Michael Dennin (Co-Principal Investigator)
Mark Warschauer (Co-Principal Investigator)
Award
NSF #23152984
$400,000
October 1, 2023 to February 28, 2026
Proposal
Key UCI Partners
School of Engineering
Office of the Vice Provost of Teaching and Learning
School of Education Digital Learning Lab
Abstract
This project aims to serve the national interest by developing an engineering writing curriculum that incorporates generative artificial intelligence (AI). Pursuant to the NSF program for Improving Undergraduate STEM Education: Directorate for STEM Education (IUSE: EDU), Engaged Student Learning (Level 1), this project is designed to improve undergraduate teaching and learning for engineering students and enable them to be skillful writers and knowledgeable and ethical users of AI.
Writing and communication are crucial to engineers and new generative AI tools such as ChatGPT pose both significant opportunities and challenges for helping engineering students become better writers and communicators. This project thus seeks to study the integration of AI writing tools in an undergraduate engineering writing course and create open-source products to help instructors integrate AI writing tools into such courses. The project seeks to maximize the critically necessary digital and AI literacy of emerging engineers to foster writing and communication both in college and in their future careers. It is imperative that engineers both understand the potential pitfalls of generative AI related to incorrect information, bias, privacy, and intellectual property rights, and learn to write well with generative AI. The knowledge to understand when and why to use generative AI in writing and the skills to write prompts that generate useful text and to ethically make use of those texts to support one’s own writing should be developed in all engineering students, regardless of socioeconomic status, race/ethnicity, or gender.
The project will use design-based implementation research to understand the best practices for integrating AI writing tools into an undergraduate engineering writing course and the impact such integration has on students’ development as writers. To meet these goals, the researchers will iteratively develop, implement, and evaluate (a) a curriculum that can be used to integrate AI writing tools into undergraduate engineering writing and communication courses, (b) a pedagogically-informed AI writing platform to ensure reliable access to generative AI for the purposes of the curriculum, and (c) professional development to support instructor use of the curriculum and platform. The curriculum will support the AI learning goals of understanding AI's strengths, weaknesses, biases, and legal issues; understanding that there are various tools appropriate for different communication tasks and when and how to use the tools; learning to prompt AI tools effectively; ensuring that students corroborate AI output; and incorporating AI ethically in research and writing.
The platform will enable instructors to customize by class, and even by assignment, the permitted uses of AI and guardrails in place for student use through prepared (but transparent) prompts for uses such as providing feedback, brainstorming, outlining, and summarizing. The platform will be free for students’ class-related use and researchers will have access to both students’ input and AI output for research purposes.
Each phase of the project will be implemented following the Plan-Do-Study-Act framework: the team will collaboratively plan (set goals, compare practices, select use cases); do (test the plan by implementing the curriculum and platform in small scale); study (analyze the pilot cases); and act (identify the adaptations and next steps for scaling up). The PDSA framework will allow us to continuously improve the integration of AI writing tools into instruction and evaluate their impact on student learning and teaching practices over time, so that we can understand natural variation in our setting.
Ultimately, the team will make the curriculum, tool, and professional development publicly available on the project website; conduct in-person and online workshops for instructors to support their adoption of the curriculum; offer quarterly webinars to a national audience; and disseminate findings in open access publications, all with the goal of informing STEM instructors across the U.S. about how to incorporate generative AI in writing instruction. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.
Tamara Tate (Principal Investigator)
Beth Harnick-Shapiro (Co-Principal Investigator)
Michael Dennin (Co-Principal Investigator)
Mark Warschauer (Co-Principal Investigator)
Award
NSF #23152984
$400,000
October 1, 2023 to February 28, 2026
Proposal
Key UCI Partners
School of Engineering
Office of the Vice Provost of Teaching and Learning
School of Education Digital Learning Lab
Abstract
This project aims to serve the national interest by developing an engineering writing curriculum that incorporates generative artificial intelligence (AI). Pursuant to the NSF program for Improving Undergraduate STEM Education: Directorate for STEM Education (IUSE: EDU), Engaged Student Learning (Level 1), this project is designed to improve undergraduate teaching and learning for engineering students and enable them to be skillful writers and knowledgeable and ethical users of AI.
Writing and communication are crucial to engineers and new generative AI tools such as ChatGPT pose both significant opportunities and challenges for helping engineering students become better writers and communicators. This project thus seeks to study the integration of AI writing tools in an undergraduate engineering writing course and create open-source products to help instructors integrate AI writing tools into such courses. The project seeks to maximize the critically necessary digital and AI literacy of emerging engineers to foster writing and communication both in college and in their future careers. It is imperative that engineers both understand the potential pitfalls of generative AI related to incorrect information, bias, privacy, and intellectual property rights, and learn to write well with generative AI. The knowledge to understand when and why to use generative AI in writing and the skills to write prompts that generate useful text and to ethically make use of those texts to support one’s own writing should be developed in all engineering students, regardless of socioeconomic status, race/ethnicity, or gender.
The project will use design-based implementation research to understand the best practices for integrating AI writing tools into an undergraduate engineering writing course and the impact such integration has on students’ development as writers. To meet these goals, the researchers will iteratively develop, implement, and evaluate (a) a curriculum that can be used to integrate AI writing tools into undergraduate engineering writing and communication courses, (b) a pedagogically-informed AI writing platform to ensure reliable access to generative AI for the purposes of the curriculum, and (c) professional development to support instructor use of the curriculum and platform. The curriculum will support the AI learning goals of understanding AI's strengths, weaknesses, biases, and legal issues; understanding that there are various tools appropriate for different communication tasks and when and how to use the tools; learning to prompt AI tools effectively; ensuring that students corroborate AI output; and incorporating AI ethically in research and writing.
The platform will enable instructors to customize by class, and even by assignment, the permitted uses of AI and guardrails in place for student use through prepared (but transparent) prompts for uses such as providing feedback, brainstorming, outlining, and summarizing. The platform will be free for students’ class-related use and researchers will have access to both students’ input and AI output for research purposes.
Each phase of the project will be implemented following the Plan-Do-Study-Act framework: the team will collaboratively plan (set goals, compare practices, select use cases); do (test the plan by implementing the curriculum and platform in small scale); study (analyze the pilot cases); and act (identify the adaptations and next steps for scaling up). The PDSA framework will allow us to continuously improve the integration of AI writing tools into instruction and evaluate their impact on student learning and teaching practices over time, so that we can understand natural variation in our setting.
Ultimately, the team will make the curriculum, tool, and professional development publicly available on the project website; conduct in-person and online workshops for instructors to support their adoption of the curriculum; offer quarterly webinars to a national audience; and disseminate findings in open access publications, all with the goal of informing STEM instructors across the U.S. about how to incorporate generative AI in writing instruction. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.