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Title: 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. more »« less
Kim, Choah; Preston, Kiam; Braga, Alice; Fankhauser, Sarah C.
(, Journal of Microbiology & Biology Education)
McCartney, Melissa
(Ed.)
ABSTRACT In various formats, students at the secondary and postsecondary levels participate in multiweek authentic science research projects. There have been many papers explaining the operations of such programs, but few have provided explicit instruction on how to incorporate authentic communication practices into the student research process. In this paper, we describe how we integrated primary literature into an 8-week online research program for 8th to 11th graders. Each week, students were introduced to a specific section of a primary research article reflecting different stages of their research project, and they were guided on how to write that specific section for their own research paper. By the end of the program, students had an outline or first draft of a primary research paper based on their research. Following completion of the program, student participants reported greater self-efficacy and confidence in scientific writing. Here, we describe our approach and provide an adaptable framework for integrating primary literature into research projects.
Black, Rebecca W; Tomlinson, Bill
(, Scientific Reports)
University students have begun to use Artificial Intelligence (AI) in many different ways in their undergraduate education, some beneficial to their learning, and some simply expedient to completing assignments with as little work as possible. This exploratory qualitative study examines how undergraduate students used AI in a large General Education course on sustainability and technology at a research university in the United States in 2023. Thirty-nine students documented their use of AI in their final course project, which involved analyzing conceptual networks connecting core sustainability concepts. Through iterative qualitative coding, we identified key patterns in students’ AI use, including higher-order writing tasks (understanding complex topics, finding evidence), lower-order writing tasks (revising, editing, proofreading), and other learning activities (efficiency enhancement, independent research). Students primarily used AI to improve communication of their original ideas, though some leveraged it for more complex tasks like finding evidence and developing arguments. Many students expressed skepticism about AI-generated content and emphasized maintaining their intellectual independence. While some viewed AI as vital for improving their work, others explicitly distinguished between AI-assisted editing and their original thinking. This analysis provides insight into how students navigate AI use when it is explicitly permitted in coursework, with implications for effectively integrating AI into higher education to support student learning.
Work-in-Progress: Uncovering AI Adoption Trends Among University Engineering Students for Learning and Career Preparedness-progress study explores self-reported data on AI use by university engineering students. The purpose of this study is to investigate how students are utilizing AI technologies and to understand their views on the role of AI in their future. The primary research question formulated was: How does the adoption of AI technologies for learning vary across demographic groups among university engineering students? Advances in technology and the emergence of AI tools have attracted attention from academia, research, and industry. The rapid growth of deep learning technologies has changed the landscape in the work environment, and universities may need to adapt to keep pace. Dynamic changes in the workplace have accelerated as these AI technologies are being leveraged to complete tasks at a high-speed rate. Research indicates that the workforce is increasingly demanding higher skill levels, including specialized AI skills. Formal education in AI basics could be crucial for future career readiness. Over 150 engineering students reported their demographics, including age, race, gender, year in school, and if they identify as having any form of disability. Currently, the survey remains open. The final study will incorporate more responses, and additional data will come from semi-structured interviews. This research explores the ways in which undergraduate and graduate students at a major R1 land-grant university in the western United States interact with AI tools. Students reported on using AI technologies, like ChatGPT, to aid in their learning. Preliminary findings suggest that freshman students are less likely to have used AI technologies than those later in their college careers. Encouragingly, students closest to entering the workforce are the ones with the most exposure to these technologies. Interestingly, students who identify as having any form of a disability or condition that impacts their learning (e.g., learning disability, neurodiversity, physical disability, etc.) initially reported lower usage of AI technologies compared to their classmates. The lower use by freshmen and increasing exposure to generative AI throughout students’ university experience is noteworthy. Students were also asked for their views on the formal integration of AI technologies into the College of Engineering courses. It could be valuable for universities to explore adding formal training to help equip students for the workforce. We anticipate that this study will highlight how exposure to AI technologies may prove essential for engineering students in preparing for a rapidly evolving workplace, as AI has the potential to enhance real-world problem-solving skills and help students become more equipped for workplace demands.
Khushal, Anum
(, University of Nebraska Digital Commons)
Quantitative reasoning (QR) is the ability to apply mathematics and statistics in the context of real-life situations and scientific problems. It is an important skill that students require to make sense of complex biological phenomena and handle large datasets in biology courses and research as well as in professional contexts. Biology educators and researchers are responding to the increasing need for QR through curricular reforms and research into biology education. This qualitative study investigates how undergraduate biology instructors implement QR into their teaching. The study used pedagogical content knowledge (PCK) and a QR framework to explore instructors’ instructional goals, strategies, and perceived challenges and affordances in undergraduate biology instruction. The participants included 21 biology faculty across various institutions in the United States, who intentionally integrated QR in their instruction. Semi-structured interviews were used to collect data focusing on participants’ beliefs, experiences, and classroom practices. Findings indicated that instructors adapt their QR instruction based on course level and student preparedness. In lower-division courses, strategies emphasized building foundational skills, reducing math anxiety, and using scaffolded instruction to promote confidence. In upper-division courses, instructors expected greater math fluency but still encountered a wide range of student abilities, prompting a focus on correcting misconceptions in integrating math knowledge and fostering deeper conceptual understanding in biology. Many instructors reported that their personal and educational experiences, especially struggles with math, often shaped their inclusive and empathetic teaching practices. Additionally, instructors’ research backgrounds influenced instructional design, particularly in the use of authentic data, statistical tools, and real-world applications. Instructors’ teaching experiences led to refinement in lesson planning, pacing, and active learning strategies. Despite their efforts, instructors faced both internal and external challenges in implementing QR, including discomfort with teaching math, time limitations, student resistance, and institutional barriers. However, affordances such as departmental support, interdisciplinary collaboration, and curricular flexibility helped to overcome some of these challenges. This study highlights the complex relationships between instructors’ experiences, beliefs, and contextual factors in shaping QR instruction. This calls for professional development that supports reflective practice, builds interdisciplinary competence, and promotes instructional strategies that bridge biology and mathematics and will help instructors design a learning environment that better support students’ development of QR skills. These findings offer valuable guidance for professional development aimed at helping biology instructors incorporate quantitative reasoning into their teaching. Such efforts can better equip students to meet the quantitative demands of modern biology and promote their continued engagement in STEM fields through more inclusive and integrated instructional approaches.
Garcia_Ramos, Jennifer; Wilson-Kennedy, Zakiya
(, Frontiers in Education)
Pandey, Sumali
(Ed.)
This original research article focuses on the investigation of the use of generative artificial intelligence (GAI) use among students in communication-intensive STEM courses and how this engagement shapes their scientific communication practices, competencies, confidence, and science identity. Using a mixed-methods approach, patterns were identified in how students perceived their current science identity and use of incorporating artificial intelligence (AI) into writing, oral, and technical tasks. Thematic analysis reveals that students use AI for a range of STEM communication endeavors such as structuring lab reports, brainstorming presentation ideas, and verifying code. While many minoritized students explicitly describe AI as a confidence-boosting, timesaving, and competence-enhancing tool, others—particularly those from privileged backgrounds—downplay its influence, despite evidence of its significant role in their science identity. These results suggest the reframing of science identity as being shaped by technological usage and social contingency. This research illuminates both the potential and pitfalls of AI-use in shaping the next generation of scientists.
Tate, Tamara P, Harnick-Shapiro, Beth, Ritchie, Daniel Robert, Tseng, Waverly, Dennin, Michael, and Warschauer, Mark. Incorporating generative AI into a writing-intensive undergraduate course without off-loading learning. Retrieved from https://par.nsf.gov/biblio/10627815. Discover Computing 28. Web. doi:10.1007/s10791-025-09563-9.
Tate, Tamara P, Harnick-Shapiro, Beth, Ritchie, Daniel Robert, Tseng, Waverly, Dennin, Michael, and Warschauer, Mark.
"Incorporating generative AI into a writing-intensive undergraduate course without off-loading learning". Discover Computing 28 (). Country unknown/Code not available: Discover Computing. https://doi.org/10.1007/s10791-025-09563-9.https://par.nsf.gov/biblio/10627815.
@article{osti_10627815,
place = {Country unknown/Code not available},
title = {Incorporating generative AI into a writing-intensive undergraduate course without off-loading learning},
url = {https://par.nsf.gov/biblio/10627815},
DOI = {10.1007/s10791-025-09563-9},
abstractNote = {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.},
journal = {Discover Computing},
volume = {28},
publisher = {Discover Computing},
author = {Tate, Tamara P and Harnick-Shapiro, Beth and Ritchie, Daniel Robert and Tseng, Waverly and Dennin, Michael and Warschauer, Mark},
}
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