Overview
Generative AI (GenAI) refers to technology that can create new content such as text, images, or code, based on user input. While the utilization of GenAI in education sparks numerous debates, its benefits and drawbacks in our classrooms are vital for educators to consider, discuss, and explore.
Understanding How LLMs Work
Large Language Models (LLMs) are among the most commonly-used forms of GenAI. Examples of LLMs include Google Gemini, ChatGPT, Claude by Anthropic, and Microsoft Copilot. An LLM has been trained on an enormous database of text. This training allows an LLM to output responses based on a series of predicted word patterns. When users enter a question or prompt into an LLM, it uses that information to output a response based on the predictions the system has made from its training.
Users of LLMs will often treat an LLM like a search engine. While an LLM can provide accurate answers to commonly-asked questions, it is not always a reliable source of factual information. As such, it is often worth cross-verifying responses from an LLM with other sources to verify the information.
Academic Senate GenAI Working Group Guidance
Campus leadership has left the decisions about whether the campus community uses GenAI up to individual courses and faculty. In May 2025, a working group of the Faculty Senate, convened within DIVCO, drafted guidance on the arguments for either the adoption or the prohibition of GenAI tools within an instructional context in order to support individual instructors in navigating their decision-making in their own instructional contexts. These points address the arguments that faculty may wish to consider when deciding what kinds of policies they may want to adopt.
Arguments for encouraging or requiring GenAI use:
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GenAI tools are pervasive in the workforce. Students need to learn how to effectively utilize GenAI tools to prepare for their future careers.
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GenAI tools may lead to innovative teaching methods, such as interaction with virtual teaching aids or debating alternative perspectives.
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Provides hands-on experience with the deficiencies and limitations of GenAI tools, particularly when these tools are properly contextualized and experimented with critically.
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Supports students in learning how to critically evaluate GenAI output, teaching students the appropriate use of prompting and co-learning with GenAI.
Arguments for allowing GenAI use:
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The use of GenAI tools is already ubiquitous, and students likely use these tools in other contexts.
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Students may benefit from using GenAI tools for research to guide the process of information-gathering and contextualization.
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GenAI tools may help students learn to write more effectively by correcting grammatical errors and providing real-time critical feedback.
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GenAI tools can accelerate data processing and programming tasks so students can focus on other relevant tasks or concepts.
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GenAI tools may help non-native speakers communicate more effectively by aiding in translation and providing guidance on potential points of confusion.
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Encouraging students to use campus-approved GenAI tools ensures that students take advantage of the enhanced data protection in these tools and potentially lowers the climate impact.
Arguments for prohibiting GenAI use:
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Use of GenAI on assignments short-circuits the learning process. If students rely on such tools to analyze, explain, research, synthesize, or create, then they may not develop their own abilities.
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Without learning to accomplish tasks manually, students may not be able to learn to judge the quality of a GenAI’s output.
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Using GenAI tools that were trained on unlicensed human work is ethically questionable.
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Submitting under your own name work that GenAI completed is academically dishonest.
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GenAI tools sometimes fabricate information, delivering false statements even when they appear to be citing published work.
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GenAI tools produce products that can be biased based on their training data.
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Some GenAI tools require substantial amounts of energy to run and are sustained by extensive physical infrastructure, resulting in adverse environmental impacts in terms of emissions and disruptions to natural habitats.