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Title: University students describe how they adopt AI for writing and research in a general education course
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. more »« less
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.
Zheng, Dailin; Chen, Yu; Chan, Yee Kit; Lai, Erica; Albert, Leslie J
(, Proceedings of the AAAI Conference on Artificial Intelligence)
This paper presents an experiential learning pedagogy that teaches undergraduate business management information systems students hands-on AI skills through the lens of sustainability. The learning modules aim to empower undergraduate business students to gain interest and confidence in AI knowledge, skills, and careers, to sharpen their higher order thinking abilities, and to help them gain a deeper understanding of sustainability issues. Students learn AI through developing chatbots that address pressing sustainability issues within their own communities. Results of the pilot study indicate that students have increased self-efficacy in AI, more positive attitudes towards AI learning and AI-related careers, enhanced sustainability awareness, and more confidence in their ability to innovate.
Engineering undergraduate programs offer a variety of laboratory courses that aim to give students hands-on experience with engineering practices while also assigning lab report writing that builds communication skills within their major. This study aims to investigate how engineering programs of a branch campus in a land-grant research university offer writing education in undergraduate lab courses. Among numerous electrical engineering and mechanical engineering course offerings at the university, nine undergraduate engineering lab courses were chosen for this study. To begin, the purpose, content, environment, and grading contribution of the chosen labs were surveyed. Then, the materials provided to students in relation to lab report assignment were investigated using nine lab report writing outcomes defined in earlier studies. Finally, the provided evaluation criteria of the lab reports were studied using the same nine outcomes. The lab report writing outcomes used in the study include 1) address technical audience expectations, 2) present experimental processes, 3) illustrate lab data using appropriate graphic/table forms, 4) analyze lab data, 5) interpret lab data, 6) provide an effective conclusion, 7) develop ideas using effective reasoning and productive patterns, 8) demonstrate appropriate genre conventions, and 9) establish control of conventions for a technical audience. We concluded that, regardless of major or program level, the primary purpose and contents of the course materials were usually categorized as educational and experimental, respectively. The secondary purpose and contents were predominantly developmental and analytical. Additionally, we found that most courses explicitly addressed outcomes related to report organization, data presentation/analysis/interpretation, and writing conventions. However, the outcome related to developing ideas using effective reasoning and productive patterns was not proven to have been explicitly covered in any of the courses studied. Finally, we found that though many of the courses studied had explicitly addressed these outcomes, fewer courses directly assessed the nine outcomes. It can be interpreted that engineering students might struggle with the inconsistency between the assignment and the assessment in lab report writing.
Williamson, Francesca A.; Rollings, Amber J.; Fore, Grant A.; Angstmann, Julia L.; Sorge, Brandon H.
(, Environmental Education Research)
Given the ongoing socio-ecological crises, higher education institutions need curricular interventions to support students in developing the knowledge, skills, and perspectives needed to create a sustainable future. Campus farms are increasingly becoming sites for sustainability and environmental education toward this end. This paper describes the design and outcomes of a farm-situated place-based experiential learning (PBEL) intervention in two undergraduate biology courses and one environmental studies course over two academic years. We conducted a mixed-method study using pre/post-surveys and focus groups to examine the relationship between the PBEL intervention and students’ sense of place and expressions of pro-environmentalism. The quantitative analysis indicated measurable shifts in students’ place attachment and place-meaning scores. The qualitative findings illustrate a complex relationship between students’ academic/career interests, backgrounds, and pro-environmentalism. We integrated these findings to generate a model of sustainability learning through PBEL and argue for deepening learning to encourage active participation in socio-ecological change.
Lee, E.; Brunhaver, S.; Bekki, J.
(, Proceedings of the American Society for Engineering Education)
The purpose of this study is to develop an instrument to measure student perceptions about the learning experiences in their online undergraduate engineering courses. Online education continues to grow broadly in higher education, but the movement toward acceptance and comprehensive utilization of online learning has generally been slower in engineering. Recently, however, there have been indicators that this could be changing. For example, ABET has accredited online undergraduate engineering degrees at Stony Brook University and Arizona State University (ASU), and an increasing number of other undergraduate engineering programs also offer online courses. During this period of transition in engineering education, further investigation about the online modality in the context of engineering education is needed, and survey instrumentation can support such investigations. The instrument presented in this paper is grounded in a Model for Online Course-level Persistence in Engineering (MOCPE), which was developed by our research team by combining two motivational frameworks used to study student persistence: the Expectancy x Value Theory of Achievement Motivation (EVT), and the ARCS model of motivational design. The initial MOCPE instrument contained 79 items related to students’ perceptions about the characteristics of their courses (i.e., the online learning management system, instructor practices, and peer support), expectancies of course success, course task values, perceived course difficulties, and intention to persist in the course. Evidence of validity and reliability was collected using a three-step process. First, we tested face and content validity of the instrument with experts in online engineering education and online undergraduate engineering students. Next, the survey was administered to the online undergraduate engineering student population at a large, Southwestern public university, and an exploratory factor analysis (EFA) was conducted on the responses. Lastly, evidence of reliability was obtained by computing the internal consistency of each resulting scale. The final instrument has seven scales with 67 items across 10 factors. The Cronbach alpha values for these scales range from 0.85 to 0.97. The full paper will provide complete details about the development and psychometric evaluation of the instrument, including evidence of and reliability. The instrument described in this paper will ultimately be used as part of a larger, National Science Foundation-funded project investigating the factors influencing online undergraduate engineering student persistence. It is currently being used in the context of this project to conduct a longitudinal study intended to understand the relationships between the experiences of online undergraduate engineering students in their courses and their intentions to persist in the course. We anticipate that the instrument will be of interest and use to other engineering education researchers who are also interested in studying the population of online students.
Black, Rebecca W, and Tomlinson, Bill. University students describe how they adopt AI for writing and research in a general education course. Retrieved from https://par.nsf.gov/biblio/10637317. Scientific Reports 15.1 Web. doi:10.1038/s41598-025-92937-2.
Black, Rebecca W, & Tomlinson, Bill. University students describe how they adopt AI for writing and research in a general education course. Scientific Reports, 15 (1). Retrieved from https://par.nsf.gov/biblio/10637317. https://doi.org/10.1038/s41598-025-92937-2
@article{osti_10637317,
place = {Country unknown/Code not available},
title = {University students describe how they adopt AI for writing and research in a general education course},
url = {https://par.nsf.gov/biblio/10637317},
DOI = {10.1038/s41598-025-92937-2},
abstractNote = {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.},
journal = {Scientific Reports},
volume = {15},
number = {1},
publisher = {Nature},
author = {Black, Rebecca W and Tomlinson, Bill},
}
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