Large language models (LLMs) are AI systems that are trained to generate human-like language. They are trained using massive datasets of text (imagine being able to read 9 million word books once a day for an entire lifetime \/ 80 years, that\u2019s how much text these models are trained on). They learn to recognize and generate words and phrases in the same way that humans do. This allows them to understand generate new content based on specific inputs or prompts.\u00a0<\/span><\/p>\n These LLMs are applied in a number of different applications and services. The one that you might have heard about is chatGPT but there are countless others like Jenni.ai (specifically for helping the writing process) or explainpaper.com<\/a> (a service for helping read dense academic papers). For this blog post we\u2019ll focus primarily on chatGPT. chatGPT, created by openAI, is a conversational chatbot that is sensationally good a generating human-like text. This has raised concerns within the education domain about academic integrity and whether writing is something our students will offload to artificial intelligence.\u00a0<\/span><\/p>\n Faculty Hub Recommendation #1: Develop a policy for AI use in your course and communicate it to your students<\/b><\/p>\n Article IV of the University of Richmond\u2019s Honor code requires student\u2019s pledge that they \u201chave neither received nor given unauthorized assistance during the completion of [their] work\u201d. Artificially intelligence generated text certainly could be considered unauthorized assistance but our recommendation is to determine a specific policy for AI generated text and then communicate that policy with your students. Whether it is a specific syllabi policy or a conversation in class, we recommend articulating a specific vision\u00a0 <\/span>for what your expectations are.\u00a0<\/span><\/p>\n Faculty Hub Recommendation #<\/b>2<\/b>: Gain a better understanding of what LLMs can and can\u2019t do for your discipline<\/b><\/p>\n Because these models are only as good as the text they are trained on, the impacts are dependent on what discipline you teach \/ research in. For instance, most court proceedings and scholarship is freely available and therefore likely part of the corpus that models like GPT-3 (backbone on chatGPT) are trained on. That\u2019s in contrast to some humanities disciplines like modern literature whose primary literature and scholarship are copywrited and\/or behind paywalls. This will primarily impact how \u2018knowledge\u2019 the models are (whether or not the resulting text is actually rooted an any sort of truth or just BS). We encourage you to investigate the abilities of chatGPT or if you aren\u2019t interested in creating an account, schedule a consultation with the Faculty Hub and we can facilitate that investigate with you.\u00a0<\/span><\/p>\n Faculty Hub Recommendation #<\/b>3<\/b>: Identify processes \/ tasks that might benefit from AI assistance\u00a0<\/span><\/b><\/p>\n It is important to note that we are just at the beginning to understand the utility of these LLMs. There is still a lot of uncertainty about the impact they have. We have identified a few ways chatGPT can improve various processes that are a part of faculty workflows. Below are a few ideas but we encourage you to reach out to use to learn more about how it might impact your specific workflows:<\/p>\n Those are our three specific recommendations for thinking about AI impacts on teaching and scholarship. Likely there will be more as the tools and service quickly evolve over the next few months. Please reach out to use at facultyhub@richmond.edu<\/a> if you have any questions!<\/p>\n","protected":false},"excerpt":{"rendered":" AI Impacts on Teaching and Scholarship Blog posts about artificial intelligence are everywhere you look. To help you make sense of all the noise, we are writing another one :). This one will focus on you, University of Richmond faculty, who are curious how artificial intelligence will impact your teaching and scholarship. We\u2019ll be focused Continue reading.<\/a><\/p>\n","protected":false},"author":2184,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-332","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/blog.richmond.edu\/facultyhub\/wp-json\/wp\/v2\/posts\/332","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.richmond.edu\/facultyhub\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.richmond.edu\/facultyhub\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.richmond.edu\/facultyhub\/wp-json\/wp\/v2\/users\/2184"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.richmond.edu\/facultyhub\/wp-json\/wp\/v2\/comments?post=332"}],"version-history":[{"count":1,"href":"https:\/\/blog.richmond.edu\/facultyhub\/wp-json\/wp\/v2\/posts\/332\/revisions"}],"predecessor-version":[{"id":333,"href":"https:\/\/blog.richmond.edu\/facultyhub\/wp-json\/wp\/v2\/posts\/332\/revisions\/333"}],"wp:attachment":[{"href":"https:\/\/blog.richmond.edu\/facultyhub\/wp-json\/wp\/v2\/media?parent=332"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.richmond.edu\/facultyhub\/wp-json\/wp\/v2\/categories?post=332"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.richmond.edu\/facultyhub\/wp-json\/wp\/v2\/tags?post=332"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
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