An Investigation into ChatGPT’s Application for a Scientific Writing Assignment

Rojas, Anthony J.

doi:10.1021/acs.jchemed.4c00034

Cited by 8 publications

(2 citation statements)

References 35 publications

(50 reference statements)

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“…In the Fall of 2023, my course used one of these AI tools (ChatGPT-4) as the predominant means for creating and refining data visualizations, and this manuscript discusses the approach used as well as its relative merits and limitations as well as student perceptions and outcomes. It should be mentioned that there is already significant discussion of the role of AI tools in chemical education, focusing on topics such as writing aids, lesson planning aids, , strengths and shortcomings for problem solving, − concerns over academic integrity, and enhancing student learning activities. ,, However, to my knowledge, there are yet no articles discussing the use of AI to help teach the design of data visualizations. It is hoped the approach discussed below could be incorporated into other courses, thereby better preparing students to effectively communicate their science.…”

Section: Introductionmentioning

confidence: 99%

Using ChatGPT-4 to Teach the Design of Data Visualizations

Lear

2024

J. Chem. Educ.

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Modern scientific communication revolves around data visualizations. While chemistry curricula include basic instruction on constructing data visualizations, the design of these visualizations is rarely taught. There are two main reasons for this lack of instruction: (i) most instructors were never taught design themselves and thus struggle to articulate design concepts, and (ii) refining data visualizations has traditionally required detailed knowledge of specialized software, which required excessive time to teach within existing curricula. This article reports my recent experience using the AI tool ChatGPT-4 to teach the design of data visualizations. ChatGPT-4 can process data, create, and display data visualizations�eliminating the need to teach specialized software. The article makes the case that this tool can help overcome the above barriers, by leveraging everyday language as the interface for creating data visualizations. The use of everyday language means that one need not possess a specialized design lexicon, but only understand the basics of design�which can be surprisingly easy to learn and teach. This article introduces basic graphic design principles, demonstrates using them with ChatGPT-4 to produce data visualizations, discusses the relative strengths and weaknesses of this approach, reports student perceptions of their experience with the tool, and touches on outcomes from teaching the design of data visualizations using this tool. The overall conclusion is that ChatGPT-4 offers an opportunity to provide meaningful instruction that moves beyond the mechanics of constructing data visualizations, focusing instead on designing effective visualizations. This approach helps prepare students to communicate their science more effectively.

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Section: Introductionmentioning

confidence: 99%

Using ChatGPT-4 to Teach the Design of Data Visualizations

Lear

2024

J. Chem. Educ.

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“…Following these advances in LLMs and more generally, generative AI, more attention has been paid to education and learning in chemistry [32,33,34,35]. Promising potential applications have been discussed and implemented [36,37,38,39,40,41,42,43], that highlight promising opportunities while also discussing potential negative effects of already publicly-accessible tools [44,33]. Indeed, authors cite issues such as threats to the traditional essay writing for evaluation of understanding [44], plagiarism concerns [33], and more broadly the effect of Generative AI on student's learning and understanding [45,46,47,48].…”

Section: Introductionmentioning

confidence: 99%

Large Language Models are Catalyzing Chemistry Education

Du,

Duan,

Bran

et al. 2024

Preprint

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Large language models (LLMs) have demonstrated outstanding capabilities in general problem-solving and been shown to improve productivity in certain domains. Thanks to their flexibility, recent work has leveraged them for diverse scientific applications, ranging from predictive modeling, scientific Q&A, and even as autonomous agents towards automation in chemistry. The democratization of high-quality chemistry education faces several challenges, including heterogeneity among sub-fields, limited access to personalized guidance, and an uneven distribution of resources. Additionally, hands-on laboratory experiments, a crucial component of chemistry education, are difficult to scale due to inherent safety risks that necessitate close supervision. We propose that LLMs can help overcome these obstacles by providing scalable solutions that tailor educational content to individual needs, enhancing the overall learning experience. In this perspective, we discuss how LLMs can catalyze chemistry education across multiple dimensions, from preparing and delivering lectures and tackling guidance in both wet lab and computational experiments, to re-thinking evaluation methodologies in the classroom. We also discuss some potential risks of this technology, such as the possibility of generating inaccurate or biased content, and emphasize the need for further development to ensure the successful integration of LLMs in the chemistry classroom.

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