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Since the public release of OpenAI’s ChatGPT on November 30, 2022, questions and concerns have rapidly circulated concerning the role of generative artificial intelligence (AI) in higher education, particularly in instructional or curricular contexts. While ChatGPT and other large language model (LLM) generative AI tools produce text-based output, other generative AI can output text, data, images, sound, video and mixed media formats. More recent developments have seen generative AI integrated into existing software platforms.

The International Association of Privacy Professionals defines generative AI as “[a] field of AI that uses machine learning models trained on large data sets to create new content, such as written text, code, images, music, simulations and videos. These models are capable of generating novel outputs based on input data or user prompts.”1

Sidney Dobrin, author of AI and Writing, explains that “[w]e can think of a GenAI as participating in a rudimentary conversation with a user” who “ask[s] the AI to create a specific deliverable — an essay, a song, an image, the solution to a math problem or so on. The AI then scrubs through all of the data available, looking for patterns and recurring information about the requested task. It then reorganizes that data into a pattern that it deems to answer the prompt.”2

Over the past year, access to generative AI technologies has expanded rapidly from ChatGPT to a range of tools such as ChatGPT Plus, Google Bard/Gemini, Microsoft/Bing Copilot, Anthropic’s Claude, Perplexity, xAI’s Grok, Meta’s Llama (with its open-source modifications), and more. Access to these tools ranges as well, from pay-based subscriptions to free accounts, open use (i.e., no account needed), and open source. In addition, generative AI is increasingly embedded in software such as media production and office productivity suites. LLM-based generative AI tools have gained multimodal capabilities; some can interpret and/or produce images as well as other artifacts such as spreadsheets and HTML files.3 Others can access the world wide web.

In June 2023, the University of Kentucky empaneled UK ADVANCE, a broad-based committee of experts to examine and make recommendations to help the campus and community regarding the implications of generative AI for higher education, research and beyond. UK ADVANCE is taking an evidence-based approach with experts from many disciplines and ongoing monitoring of experiences among our campus, community, and nationally. For these guidelines, initially released on August 11, 2023 and updated on December 14, 2023, UK ADVANCE has sought input from multiple stakeholders as well.

AI already is heralding tremendous changes in academia and the economy, from innovations in farming and the development of therapeutics to customer service and workplace innovations.4 At the same time, there are significant concerns over disruption and displacement of the workforce, embedded bias, data security and privacy and the spread of misinformation.

Within academia, there is the potential to create even greater access to personalized and customized learning, expanded student engagement, intelligent tutoring systems and innovative approaches to curriculum design. At the same time, there are concerns over academic integrity, infringements on privacy and the ability to develop data and information literacies for AI tools.5

After reviewing emerging evidence and experiences related to instruction and learning, UK ADVANCE offers the following guidelines and recommendations for the use of generative AI in instructional contexts, specifically regarding (1) the development of course policies concerning generative AI, (2) the response to potential misuse of generative AI in instructional contexts, and (3) approaches to assignment and learning design that mitigate the risk of misuse and leverage the positive potential of generative AI. Guidelines for the use of generative AI in research are currently available on the UK ADVANCE website.

It’s important to note that the following guidelines and recommendations primarily focus on text-based generative AI tools. At the same time, many of the recommendations and insights are transferrable to situations involving other modalities of generative AI (e.g., image).

Generative AI is a rapidly evolving technology. These guidelines reflect our best understanding at the current time and may be updated to reflect the nature of the field as it continues to change.


Download faculty guidelines here. 


1 IAPP 2023.

2 Dobrin 2023.

3 For example, see Topol 2023 and Yan, et al. 2023.

4 For example, studies have examined the impact on productivity and quality of work, e.g., Cambon, et al. 2023; Choi, Monahan, and Schwarcz 2023; Dell'Acqua, et al. 2023.

5 Passages adapted from UKNow, challenges-campus-commonwealth.