Incorporating generative AI into a writing-intensive undergraduate course without off-loading learning

Abstract As generative AI becomes ubiquitous, writers must decide if, when, and how to incorporate generative AI into their writing process. Educators must sort through their role in preparing students to make these decisions in a quickly evolving technological landscape. We created an AI-enabled writing tool that provides scaffolded use of a large language model as part of a research study on integrating generative AI into an upper division STEM writing-intensive course. Drawing on decades of research on integrating digital tools into instruction and writing research, we discuss the framework that drove our initial design considerations and instructional resources. We then share our findings from a year of design-based implementation research during the 2023–2024 academic year. Our original instruction framework identified the need for students to understand, access, prompt, corroborate, and incorporate the generative AI use effectively. In this paper, we explain the need for students to think first, before using AI, move through good enough prompting to agentic iterative prompting, and reflect on their use at the end. We also provide emerging best practices for instructors, beginning with identifying learning objectives, determining the appropriate AI role, revising the content, reflecting on the revised curriculum, and reintroducing learning as needed. We end with an indication of our future directions.

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Institutions

Publication Details

Journal
Discover Computing
Published
2025-05-08
DOI
https://doi.org/10.1007/s10791-025-09563-9
Citations
5
Primary Topic
Innovative Teaching and Learning Methods
Type
article
Field-Weighted Citation Impact
17.22

Funders

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article

Incorporating generative AI into a writing-intensive undergraduate course without off-loading learning

Waverly Tseng, Michael Dennin, Daniel Ritchie, Mark Warschauer et al.
5 citations
Discover Computing
Innovative Teaching and Learning Methods
17.22
article

Incorporating generative AI into a writing-intensive undergraduate course without off-loading learning

Waverly Tseng, Michael Dennin, Daniel Ritchie, Mark Warschauer, Tamara Tate, Beth Harnick-Shapiro
article en
5 citations

Abstract

Abstract As generative AI becomes ubiquitous, writers must decide if, when, and how to incorporate generative AI into their writing process. Educators must sort through their role in preparing students to make these decisions in a quickly evolving technological landscape. We created an AI-enabled writing tool that provides scaffolded use of a large language model as part of a research study on integrating generative AI into an upper division STEM writing-intensive course. Drawing on decades of research on integrating digital tools into instruction and writing research, we discuss the framework that drove our initial design considerations and instructional resources. We then share our findings from a year of design-based implementation research during the 2023–2024 academic year. Our original instruction framework identified the need for students to understand, access, prompt, corroborate, and incorporate the generative AI use effectively. In this paper, we explain the need for students to think first, before using AI, move through good enough prompting to agentic iterative prompting, and reflect on their use at the end. We also provide emerging best practices for instructors, beginning with identifying learning objectives, determining the appropriate AI role, revising the content, reflecting on the revised curriculum, and reintroducing learning as needed. We end with an indication of our future directions.

Discover ComputingVol. 28(1)
University of California, Irvine (US), Irvine University (US)
National Science Foundation
Openalex Percentile: Top 1%
Innovative Teaching and Learning Methods
17.22
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Incorporating generative AI into a writing-intensive undergraduate course without off-loading learning — Waverly Tseng, Michael Dennin, et al. · Discover Computing (2025) | TGRS Research Map | TGRS