Overview
Welcome to Introduction to Generative AI for Educators!
This online module aims to provide you with an understanding of generative AI to help you think through how these technologies intersect with your teaching practices. Whether you have reservations or enthusiasm about AI in education, “Introduction to Generative AI for Educators” offers a space for exploration and thoughtful consideration.
Topics include what generative AI is, what can/can’t generative AI tools do, how to use generative AI tools, and how generative AI is changing the teaching and learning landscape.
Intended Learning Outcomes
By the end of this module, you should be able to:
- Recognize the differences in AI models and tools and how they can be used in teaching and learning.
- Use an AI tool to complete a teaching/learning task.
- Identify ways in which generative AI could impact teaching and learning in your classroom.
Introduction to Generative AI for Educators: What is Gen AI?
Generative artificial intelligence (GenAI) is artificial intelligence that can generate text, images, or other media, using predictive modeling. Here’s how it works.
GenAI models are initially trained on large datasets.
- Text generators are trained on large datasets of existing text, such as books, articles, or websites.
- Image generators are trained on extensive datasets of images. Each image consists of a grid of pixels, with each pixel having color values and positions.
- Audio and video generators are trained on datasets containing audio clips or video frames, which are sequences of images displayed rapidly.
GenAI models learn to recognize patterns in the training data and build predictive models based on this learning.
- Text generators learn the context in which words and phrases commonly appear and use linguistic and grammatical rules to predict the next word or phrase and generate sentences or paragraphs.
- Image generators learn patterns in images, identifying shapes, objects, colours, and textures, and use spatial relationships between elements and colours to predict and generate pixels.
- Audio/video generators, in addition to recognizing static image features, learn how sounds or images evolve in a sequence, and use these temporal and spatial relationships to generate video frames and/or audio segments.
If you’re interested in learning more about how this process works, you can check out this visual explainer.
You can further refine the generated content – directly, by providing feedback to the AI tool, or by editing your original prompt – to meet your specific needs. You’ll learn more about this when we practice using and AI tool in the Practice! tab of this learning module.
What’s the difference between a GenAI model and a GenAI tool?
A GenAI model is the underlying technology or algorithm that enables the generation of content. A GenAI tool is the user interface or service that allows users to access and interact with the generative AI model. For example, GPT (Generative Pre-trained Transformer) is one of the most popular LLMs (there are currently two versions – GPT-3.5 and GPT-4), whereas ChatGPT is the natural language chatbot that uses GPT-3.5 or GPT-4 to generate content based on user inputs.
There are many GenAI tools available – resource directories like There’s an AI for That list thousands, with more being added each day. But it’s most helpful to start with the core foundational models because most AI tools are running on top of or taking advantage of these models. Understanding how to use these foundational models directly is the most powerful and easiest way to gain experience with AI.
Click on the cards below to learn more about some of the features and functionality of the most common GenAI models.
Foundation Models and Large Language Models
Foundation models describe a class of AI systems that can learn from a large amount of data and perform a wide range of tasks across different domains. Foundation models are not limited to language, but can also handle other modalities like images, audio, and video. Foundation models are so called because they act as the “foundation” for many other uses, like answering questions, making summaries, translating, and more. Large language models (LLMs) are a specific type of foundation models that are trained on massive amounts of text data and can generate natural language responses or perform text-based tasks.
Foundation models are very general and broad, and they may not capture the nuances and details of every domain or task. You can “fine-tune” or adapt foundation models to improve the performance and quality of the model outputs by providing additional data and training that are relevant to a specific subject area or task. For example, if you want to use a foundation model like GPT-4 to generate summaries of news articles, you can fine-tune it on a dataset of news articles and their summaries. This helps the model learn the specific style, vocabulary, and structure of news summaries, and generate more accurate and coherent outputs.
Information Box Group
Learn More
Want to learn more about Large Language Models? Check out Wharton Interactive’s video
References
Andrei. There’s An AI For That (TAAFT)—The #1 AI Aggregator. There’s An AI For That. Retrieved October 26, 2023, from https://theresanaiforthat.com
Anthropic. (2023, May 11). Introducing 100K Context Windows. Anthropic. https://www.anthropic.com/index/100k-context-windows
Anthropic. (2024, March 4). Introducing the next generation of Claude. Announcements. https://www.anthropic.com/news/claude-3-family.
Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., Chen, C., Olsson, C., Olah, C., Hernandez, D., Drain, D., Ganguli, D., Li, D., Tran-Johnson, E., Perez, E., … Kaplan, J. (2022). Constitutional AI: Harmlessness from AI Feedback (arXiv:2212.08073). arXiv. https://doi.org/10.48550/arXiv.2212.08073
Mollick, E. (2023d, September 16). Power and Weirdness: How to Use Bing AI. One Useful Thing. https://www.oneusefulthing.org/p/power-and-weirdness-how-to-use-bing
Mollick, E. (2024, February 8). Google’s Gemini Advanced: Tasting Notes and Implications. One Useful Thing. https://www.oneusefulthing.org/p/google-gemini-advanced-tasting-notes.
Murgia, M. and the Visual Storytelling Team. (2023, September 12). Generative AI exists because of the transformer. Financial Times. https://ig.ft.com/generative-ai.
OpenAI. (2023, September 25). ChatGPT can now see, hear, and speak. https://openai.com/blog/chatgpt-can-now-see-hear-and-speak
Reuters. (2023, September 27). ChatGPT users can now browse internet, OpenAI says. Reuters. https://www.reuters.com/technology/openai-says-chatgpt-can-now-browse-internet-2023-09-27/
Stewart, E. (2024, February 14). Google’s Bard Has Just Become Gemini. What’s Different? Enterprise Management 360. https://em360tech.com/tech-article/gemini-vs-bard.
Wharton School. (2023b, August 1). Practical AI for Instructors and Students Part 2: Large Language Models (LLMs)—YouTube. YouTube. https://www.youtube.com/watch?v=ZRf2BfDLlIA
Introduction to Generative AI for Educators: What can it do?
The content on this page was adapted from Ethan Mollick’s blog post: How to Use AI to Do Stuff: An Opinionated Guide . GenAI can create, compose, and produce a diverse array of content. Click on the accordions below to learn more about different ways to use AI and which tools are most suitable. If you’re using ChatGPT for any of these uses, you might consider turning off data collection so that your prompts and conversations are not collected and stored.
Expandable List
- Write drafts of anything – blog posts, essays, promotional material, lectures, scripts, short stories. All you have to do is prompt it. Basic prompts result in boring writing, but getting better at prompting is not that hard. AI systems are more capable as writers with a little practice and user feedback.
- Make your writing better. You can paste your text into an AI and ask it to improve the content, check for grammar and improve paragraphing. Or ask for suggestions about how to make it better for a particular audience. Ask it to create 5 drafts in radically different styles. Ask it to make things more vivid or add examples.
- Help you with tasks. AI can do things you don’t have the time to do. Use it to write emails, create project templates, and a lot more. Later in this module, you’ll have a chance to try out using an AI tool to help you complete a teaching task.
- Unblock yourself. It’s very easy to get distracted from a task when you get stuck. AI can provide a way of giving yourself momentum. Ask it for ideas to help you get started. You often need to have a lot of ideas to have good ideas, and AI is good at volume. With the right prompting, you can also get it to be very creative. Or you can ask it for possible next steps in a project or a work schedule to keep you organized. The key is dialog.
AI tools are being integrated directly into common office applications. Microsoft Office includes Copilot* powered by GPT and Google Docs will integrate suggestions from Gemini. The implications of what these new innovations mean for writing are pretty profound.
*As of November 2023, McMaster faculty and staff have access to Microsoft Copilot (formerly known as Bing Chat Enterprise) with their McMaster Microsoft licenses. When you use this version of Copilot with your McMaster credentials, none of the information exchanged in the chat is stored or used to train AI models. Neither McMaster nor Microsoft can access or use the data in any way. McMaster University will continue to evaluate other AI-powered enterprise services and tools from a budgetary, security, risk, and privacy perspective. This includes Copilot for Microsoft 365, which requires additional licensing costs and will not be available to McMaster employees at this time.
There are four big image generators most people use:
- Stable Diffusion: is open source and can be run from any high-end computer. It takes effort to get started, since you have to learn to craft prompts properly, but once you do it can produce great results. It is especially good at combining AI with images from other sources. Here is a guide to using Stable Diffusion (be sure to read both parts 1 and part 2).
- DALL-E: is incorporated into Copilot (in creative mode) and Copilot image creator. This system is solid, but not as good as Midjourney.
- Midjourney: is the best system as of mid-2023. It has the lowest learning-curve: just type in “thing-you-want-to-see –v 5.2” (the –v 5.2 at the end is important, it uses the latest model) and you get a great result. Midjourney requires Discord. Here is a guide to using Discord.
- Adobe Firefly: is built into a variety of Adobe products, but it lags behind DALL-E and Midjourney in terms of quality. However, while the other two models have been unclear about the source images that they used to train their AIs, Adobe has declared that it is only using images it has the right to use. One of the major benefits of Firefly is generative fill – you can use it while editing an image in Photoshop to add something to or alter that image based on your prompting.
Here are the first images that were created by each model when provided with the prompt:
“Fashion photoshoot of sneakers inspired by Van Gogh” (each image is labelled with the AI model)
An AI video generator is a web-based or standalone software that allows you to easily create video assets without needing prior video editing experience. These tools can assist with tasks like erasing video elements, creating green screens, using text to video to construct scripts from a URL or blog post, and more. It is now easy to generate a video with a completely AI generated character, reading a completely AI-written script, talking in an AI-made voice, animated by AI. It can also deepfake people. Runway v2 was the first commercially available text-to-video tool and is a useful demonstration of what is to come.
Code Interpreter is a mode of GPT-4 that lets you upload files to the AI, allows the AI to write and run code, and lets you download the results provided by the AI. It can be used to execute programs, run data analysis, and create all sorts of files, web pages, etc. Though there has been a lot of debate since its release about the risks associated with untrained people using it for analysis, many experts testing Code Interpreter are impressed, one paper event suggesting it will require changing the way we train data scientists.
Claude 3 is excellent for working with text, especially PDFs. It’s possible to post entire books into the tool. You can also give it several complex academic articles and ask it to summarize the results, with reasonable results! You can then interrogate the material by asking follow-up questions: what is the evidence for that approach? What do the authors conclude? And so on.
Similarly, Gemini 1.5 Pro has a 128K-token context window. A limited group of developers and enterprise customers can try it with a context window of up to 1 million tokens [link: https://blog.google/technology/ai/long-context-window-ai-models/], but this is a computationally intensive process that requires further optimizations.
It’s currently not recommended to use AI as a search engine. The risk of hallucination is high (an explanation of hallucinations is provided in the What’s the catch tab). However, there is some evidence that AI can often provide more useful answers than search when used carefully, according to a recent pilot study. Especially in cases where search engines aren’t very good, like tech support, deciding where to eat, or getting advice, Copilot is often better than Google as a starting point. This is an area that is evolving rapidly, but you should be careful about these uses for now.
What’s more exciting is the possibility of using AI to help us learn. You can ask the AI to explain concepts and get very good results. This prompt is a good automated tutor, and use can find a direct link to activate the tutor in ChatGPT here. Because we know the AI could be hallucinating, you would be wise to double-check any critical data against another source.
References
Clark, P. (2023, May 23). Dream bigger: Get started with Generative Fill. Adobe Blog. https://blog.adobe.com/en/publish/2023/05/23/future-of-photoshop-powered-by-adobe-firefly
Gartenbert, C. (2024, February 16). What is a long context window? Google Blog. https://blog.google/technology/ai/long-context-window-ai-models.
Gunnell, M. (2022, April 11). How to use Discord: A beginner’s guide. PCWorld. https://www.pcworld.com/article/540080/how-to-use-discord-a-beginners-guide.html
J., J. (2023). Data Controls FAQ. Open AI. https://help.openai.com/en/articles/7730893-data-controls-faq
Mollick, E. [@emollick]. (2023a, April 5). There are big categories of common problems that, in retrospect, were never good applications for Google search. Bing AI, even with occasional inaccuracies, is just better for things like: 🧑💻Tech support 📍Deciding what to do/where to eat ⚒️How-to advice ❓Getting started advice https://t.co/9gIBxq86It [Tweet]. Twitter. https://twitter.com/emollick/status/1643718474668097538
Mollick, E.[@emollick]. (2023b, June 15). There is a lot of excitement for AI to be a universal tutor. And it shows real promise, but there are some important problems that need to be solved. To get a sense of how good it is, try this prompt (in GPT-4): Https://chat.openai.com/share/ec1018ec-1d86-4160-b587-354253c7d5cb More in our paper: Https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4475995 https://t.co/X8kpg08DEr [Tweet]. Twitter. https://twitter.com/emollick/status/1669434927761313807
Mollick, E. [@emollick]. (2023c, July 11). Every field of professional education needs to be working on a paper like this right now. This one tests Code Interpreter’s ability to do data science (90% on exams, the field is “on the verge of a paradigm shift”) Then it suggests how to change training. Https://arxiv.org/pdf/2307.02792v2.pdf https://t.co/OnUk22ZZ06 [Tweet]. Twitter. https://twitter.com/emollick/status/1678615507128164354
Mollick, E. (2023a, September 16). A quick and sobering guide to cloning yourself. One Useful Thing. https://www.oneusefulthing.org/p/a-quick-and-sobering-guide-to-cloning
Mollick, E. (2023b, September 16). How to Use AI to Do Stuff: An Opinionated Guide. One Useful Thing. https://www.oneusefulthing.org/p/how-to-use-ai-to-do-stuff-an-opinionated?utm_medium=reader2
Mollick, E. (2023c, September 16). On-boarding your AI Intern. One Useful Thing. https://www.oneusefulthing.org/p/on-boarding-your-ai-intern
Mollick, E. (2023e, September 16). Setting time on fire and the temptation of The Button. One Useful Thing. https://www.oneusefulthing.org/p/setting-time-on-fire-and-the-temptation
Mollick, E. (2023f, September 16). What AI can do with a toolbox… Getting started with Code Interpreter [Now called Advanced Data Analytics]. One Useful Thing. https://www.oneusefulthing.org/p/what-ai-can-do-with-a-toolbox-getting
Mollick, E. (2023g, September 16). What happens when AI reads a book 🤖📖. One Useful Thing. https://www.oneusefulthing.org/p/what-happens-when-ai-reads-a-book
OpenAI. (2023). ChatGPT (GPT-4) Friendly Tutor Explains Concepts. [Large language model]. https://chat.openai.com/share/ec1018ec-1d86-4160-b587-354253c7d5cb.
Prateek K. Keshari [@prkeshari]. (2023, July 9). 20 mins and 3 prompts later, ChatGPT code interpreter gives me 2 branded downloadable html files. Result 👇 https://t.co/NPMrW72g2A [Tweet]. Twitter. https://twitter.com/prkeshari/status/1678155933606637568
Stokes, J. (2022, September 29). Stable Diffusion 2.0 & 2.1: An Overview. Johnstokes.Com. https://www.jonstokes.com/p/stable-diffusion-20-and-21-an-overview
Xu, R., Feng, Y., & Chen, H. (2023). ChatGPT vs. Google: A Comparative Study of Search Performance and User Experience (arXiv:2307.01135). arXiv. https://doi.org/10.48550/arXiv.2307.01135 .
Introduction to Generative AI for Educators: What’s the catch?
While the innovation and creativity of generative AI is exciting, these systems do not come without limitations or ethical challenges. One of the biggest criticisms levelled against GenAI tools is that they make things up. As probabilistic models they are designed to generate the most likely response to any given prompt. Given that these tools do not ‘know’ anything and are – in most instances – limited in their ability to fact check, the responses they generate can include factual errors and invented citations/references. This known phenomenon has been termed ‘hallucination’.
You can learn more about general limitations and risks in the Generative Artificial Intelligence in Teaching and Learning at McMaster University Pressbook.
Thompson Rivers University developed a Critical AI Framework to help you weigh these limitations and risks when deciding whether a generative AI tool is right for your project, classroom context, or workflow. Click on the plus icons to help you in your decision-making process.
Prior to (or instead of) using AI with your students
Ignoring the “problem” won’t make it go away. If you’re unsure about using AI, it can be helpful to make space for conversation and engage in collective knowledge building before you consider integrating these systems into your classroom.
Autumn Caines, an instructional designer at the University of Michigan, provides some activity suggestions for instructors to do with their students prior to or instead of directly using ChatGPT. These include:
- Socially annotating OpenAI’s privacy and service Terms
- Playing the data, privacy, and identity game with your students
- Discussing big issues around AI (e.g., labour, climate)
- Conducting a techno-ethical audit
References
Caines, A. (2023, January 19). Prior to (or instead of) using ChatGPT with your students. Is a Liminal Space. https://autumm.edtech.fm/2023/01/18/prior-to-or-instead-of-using-chatgpt-with-your-students/
Hao, K. (2019, June 6). Training a single AI model can emit as much carbon as five cars in their lifetimes. MIT Technology Review. https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/
Perrigo, B. (2023, January 18). Exclusive: The $2 Per Hour Workers Who Made ChatGPT Safer. Time. https://time.com/6247678/openai-chatgpt-kenya-workers/
Satia, A., Verkoeyen, S., Kehoe, J., Mordell, D., Allard, E., & Aspenlieder, E. (2023). Generative Artifcial Intelligence in Teaching and Learning McMaster. Paul R. MacPherson Institute for Leadership, Innovation and Excellence in Teaching. https://ecampusontario.pressbooks.pub/mcmasterteachgenerativeai/chapter/generative-ai-limitations-and-potential-risks-for-student-learning/
Society & Ethics. (n.d.). StableDiffusionBiasExplorer. Retrieved October 26, 2023, from https://huggingface.co/spaces/society-ethics/DiffusionBiasExplorer
Thompson Rivers University. Critical AI Framework – AI in Education. Critical AI Framework. Retrieved October 26, 2023, from https://aieducation.trubox.ca/critical-ai-framework/
Vincent, J. (2022, November 15). The scary truth about AI copyright is nobody knows what will happen next. The Verge. https://www.theverge.com/23444685/generative-ai-copyright-infringement-legal-fair-use-training-data
Introduction to Generative AI for Educators: Practice!
The best way to learn what GenAI is capable of, and where it falls short, is by experimenting with the tools.
Using GenAI tools begins with a “prompt”. This is the information you give to the tool to get it to generate what you want. Prompting is mostly about experience – it takes practice to learn what works well and what doesn’t work.
Ethan Mollick differentiates two paths to prompting: conversational prompting and structured prompting.
With conversational prompting, talk to the AI to ask for what you want or might need and see what happens. For most people, today, a conversational approach is enough to help you with your work.
For some uses, at least for now, a more formal structured approach has value. Structured prompting is about getting the AI tool to do a single task well in a way that is repeatable and adaptable. It usually takes experimentation and effort to make a prompt work somewhat consistently.
Structured prompts allow you to take what you learned and apply it to different contexts. For example, Microsoft has collected a set of education prompts, which can be adapted or experimented with.
Regardless of which approach you use, it’s good practice to tell the tool:
- Who it is – this gives the AI the right context to start from (e.g., you’re an experienced instructor teaching a second-year Economics course)
- Context for its task - the more context you give it, the more effective it can be (e.g., include points about information you want it to include
- What you want it to do – include the format of the response, or the number of examples
- What you don’t want it to do (if relevant)
- Examples or steps – this helps it learn what you want and helps it think step-by-step, which means it will do a better job.
- End with a question like “what questions do you have before you begin” or “what else do you need to know before you start” for even further clarification of the task
Learning how to prompt is just part of the equation – push back and interact with AI to improve the response (e.g., ask to expand on a particular point, add an additional point, or change an example). Ultimately, AI is just giving suggestions for us to build upon. We can give feedback to make the response better, take and adapt or combine ideas, or discard what doesn’t work. This is where you use your own knowledge to evaluate and improve the result and untap the real potential of using AI.
Information Box Group
Want to learn more about prompting? Check out these resources:
Practical AI for Instructors and Students Part 3: Prompting AI
GenAI Chatbot Prompt Library for Educators
Prompting Higher Education Towards AI-Augmented Teaching and Learning Practice
Worrying about prompting is likely a temporary state of affairs. As AI systems improve, the need for esoteric prompting decreases, because AI will become better at figuring out what you want.
Using AI for a teaching and learning task
In their Practical AI for Instructors and Students video series, Ethan Mollick and Lilach Mollick share some prompts you can use to test out AI on a teaching and learning task. Choose one of the prompts below to try it out yourself. Or you could run one of your assessments through a GenAI tool and see what it comes up with.
For best results, use ChatGPT-4 or Copilot in Creative Mode. (You can use Copilot without having to create an account – download the Microsoft Edge browser and access Copilot through the sidebar). If you’re not interested in using a tool yourself, you can watch Ethan and Lilach experiment with the provided prompts in Practical AI for Instructors and Students Part 4: AI for Teachers.
Expandable List
You are an experienced teacher and can generate clear, accurate examples for students of concepts. I want you to ask me two questions. What concept do I want explained. Wait for me to answer before asking me the second question. Who is the audience for the explanation? Then look up the concept and examples of the concept. Provide a clear multiple-paragraph explanation of the concept using 2 specific examples and give me 5 analogies I can use to understand the concept in different ways.
You are a quiz creator of highly diagnostic quizzes. You will look up how to develop low-stakes tests and diagnostics. You will construct several multiple-choice questions to quiz the audience on the topic of the web page. The questions should be highly relevant and go beyond just facts. Multiple choice questions should include plausible, competitive alternate responses and should not include an “all of the above option.” At the end of the quiz, you will provide an answer key and explain the right answer.
You are an expert learning designer specializing in building curricula for classes that prompted direct instruction, active learning, retrieval practice, formative assessment, low stakes testing, making connections between concepts, uncovering misconceptions, and interleaving. First ask me what course I’m teaching, including subject matter. Wait for my response. Then ask what learning levels my students are (high school or college). Wait for my response. Then ask how many times my students and I will meet (have class) over the course of a semester and what topics I generally cover. Wait for my response. Then design a curriculum that makes sure students learn effectively.
Reflecting on your experience
Once you’ve had a chance to play around with the prompts and refine your responses, consider the following questions:
- Did the generative AI tool effectively generate the content I needed in terms of quality and relevance?
- Was the generated content easily customizable to suit the specific needs and preferences of my teaching approach and my students?
- How did the use of the AI tool impact the time required to create educational materials compared to traditional methods?
- What questions or concerns do I have about using GenAI in this way?
References
Bickerstaff, A., Kosta, D., Pinkas, C., Distol, P., & Blevins, M. (2023). GenAI Chatbot Prompt Library for Educators. AI for Education. https://www.aiforeducation.io/prompt-library
Eager, B., & Brunton, R.(2023). Prompting Higher Education Towards AI-Augmented Teaching and Learning Practice. Journal of University Teaching and Learning Practice, 20(5). https://doi.org/10.53761/1.20.5.02
Mollick, E. (2023h, November 1). Working with AI: Two paths to prompting. One Useful Thing. https://www.oneusefulthing.org/p/working-with-ai-two-paths-to-prompting?utm_source=post-email-title&publication_id=1180644&post_id=138388046&utm_campaign=email-post-title&isFreemail=true&r=2sc7cm&utm_medium=email
Rice, C., & Kaminski, P. (2023). Prompts for Education: Enhancing Productivity & Learning [Computer software]. Microsoft. https://github.com/microsoft/prompts-for-edu (Original work published 2023)
Schuloff, S., Khan, A., & Yanni, F. Learn Prompting: Your Guide to Communicating with AI. Retrieved October 26, 2023, from https://learnprompting.org/
Wharton School. (2023a, July 31). Practical AI for Instructors and Students Part 1: Introduction to AI for Teachers and Students [Video file]. YouTube. https://www.youtube.com/watch?v=wbGKfAPlZVA
Wharton School. (2023c, August 2). Practical AI for Instructors and Students Part 3: Prompting AI [Video file]. YouTube. https://www.youtube.com/watch?v=wbGKfAPlZVA
Wharton School. (2023d, August 3). Practical AI for Instructors and Students Part 4: AI for Teachers [Video file]. YouTube. https://www.youtube.com/watch?v=SBxb5xW7qFo
Introduction to Generative AI for Educators: Teaching impacts
In higher education, we tend to privilege the outcomes of learning – usually via performance through the production of a particular kind of artefact – over the process of learning. We have relied heavily on using these artefacts to infer learning because we can’t see the learning directly (and it’s far easier than trying to develop a deep understanding of our students and their progress over time). With the widespread use of GenAI tools, which can just produce the artefact, these tools perform a neat circumvention of the association between the product and the process. This is problematic for a number of reasons.
Friction “challenges the linear, totalized, and technological solutionist narratives of ‘clean interfaces and tightly controlled interactions’” – Peter Krapp (2011), Noise Channels: Glitch and Error in Digital Culture
Technology aims to reduce user “friction”, striving for a scenario where users encounter no barriers in achieving their goals. It prioritizes ease of use, convenience, and minimal cognitive load. This runs counter to what we actually want – we want friction in the learning process. As Julie Dirksen describes, “If you don’t want the material to flow smoothly past (or around) your learners, then you want to provide a little friction—something that requires learners to chew on the material, cognitively speaking” (p. 166). Cognitive friction introduces challenges to spark critical thinking and problem-solving, help with knowledge transfer and retention, cultivate resilience and perseverance, and encourage reflection and meta-cognition.
Leslie Allison and Tiffany DeRewal highlight this tension using the research process as an example.
Technology user design values…. |
The research process values… |
Minimizing number of clicks to complete task | Lots of clicks — finding multiple types of information |
Reducing mental effort in interaction with product | High level of cognition — embrace ambiguity and complexity |
Exclusively positive emotional experiences of interaction with the product | Moments of productive frustration and negative emotions that can help one grow and learn |
Increasing profit | Increasing knowledge |
Speed | Deliberation |
Ultimately, LLMs are problematic for student learning because they collapse the varied tasks of a recursive research process, prioritize speed over deliberation, discourage ambiguity and uncertainty, and give a false sense of confidence.
So, what do we do?
Allison and DeRewal propose that to create friction in the learning process, we should be strategic and critical about our use of GenAI. They recommend the use of GenAI tools for one or fewer stages of the research process per project.
This recommendation underscores the importance of deliberate decision-making around if and how to incorporate GenAI tools into the learning process. To do so, also requires a shift in what we mean by academic integrity.
Rethinking “plagiarism” and “cheating”
Matt Miller suggests that we’re going to have to update our definitions of “plagiarism” and “cheating” to reflect different uses of AI if we want our education to be relevant to our students’ future. The question is: where will we draw the line?
Miller provides several possible uses of AI to reflect on which you would consider “cheating” or “plagiarism”. For example, would you consider a student using AI for brainstorming ideas to be considered cheating or plagiarism? What about using a spelling or grammar checker?
Within the spectrum of these practices, what are the ethical thresholds? At what point, in what contexts, or with what technologies do we cross into cheating? – Paul Fyfe (2022)
It’s likely your reaction to these questions relates to how our current education system operates or how you’ve taught in the past. Paul Fyfe suggests that educational institutions continue to uphold the concept of originality as an ideal. However, these technologies have blurred the boundaries of independent work. Some scholars advocate for a perspective that emphasizes honesty in the process of producing work, while others stress the importance of maintaining distinct boundaries around individual effort. There are even suggestions for the adoption of a new framework, such as Sarah Eaton‘s concept of ‘post-plagiarism’ through a standard of hybrid human writing.
Information Box Group
At McMaster, students should assume use of GenAI is prohibited unless otherwise stated. Undeclared and/or unauthorised use of AI tools to produce coursework is considered a form of academic misconduct. Instructors who incorporate GenAI into courses should explain to students how generative AI material should be acknowledged or cited.
Rethinking assessments
Many traditional assessments are vulnerable to inappropriate use of GenAI. In the short term, instructors are being advised to adapt assessments to render it challenging or onerous to use GenAI or have students engage with GenAI tools to develop digital literacy skills. In the long term, we may see a shift towards more authentic assessments that students are intrinsically motivated to complete.
This chapter on Designing Assessments in the Age of Generative AI offers a series of shorter-term, “quick fix” strategies to help counteract or embrace the easy access to generative AI, as well as a workbook to guide you through the redesign of an assessment. There are also a growing number of resource directories compiling examples of redesigned assignments that can be a useful starting point for ideas.
Information Box Group
Looking for ideas? Check out these resource directories:
100+ Creative Ideas to Use AI in Education
TextGenEd: An Introduction to Teaching with Text Generation Technologies
Reflecting on impacts in your classroom
- How might GenAI tools change the way assessments are conducted in your course? Are there certain types of assignments or exams that could be vulnerable to inappropriate use of GenAI?
- What ethical thresholds and guidelines need to be established regarding the use of GenAI in your classroom?
- In the long term, how might the widespread use of GenAI tools impact the skills students need and the nature of future assessments? Are there opportunities to use GenAI tools to complement, rather than replace, traditional learning and teaching methods?
References
Allison, L. & DeRewal, T. Critical AI: Situating Student Research Practices in the Era of LLMs. Rowan University.
Dirksen, J. Design for How People Learn (2nd Ed.), New Riders, Peachpit Press, Pearson Education. 2016, 296 pp
Eaton, S. E. (2022). The Academic Integrity Technological Arms Race and its Impact on Learning, Teaching, and Assessment. Canadian Journal of Learning and Technology, 48(2), Article 2. https://doi.org/10.21432/cjlt28388
Fyfe, P. (2023). How to cheat on your final paper: Assigning AI for student writing. AI & SOCIETY, 38(4), 1395–1405. https://doi.org/10.1007/s00146-022-01397-z
Keegin, J. M. (2023, May 23). ChatGPT Is a Plagiarism Machine. The Chronicle of Higher Education. https://www.chronicle.com/article/chatgpt-is-a-plagiarism-machine
Krapp, P. (2011). Noise Channels: Glitch and Error in Digital Culture
Laquintano, T., Schnitzler, C., & Vee, A. TextGenEd: An Introduction to Teaching with Text Generation Technologies. The WAC Clearinghouse. https://doi.org/10.37514/TWR-J.2023.1.1.02
Miller, M. (2022). It’s time to rethink “plagiarism” and “cheating.” https://ditchthattextbook.com/ai/
Nerantzi, C., Abegglen, S., Karatsiori, M., & Martinez-Arboleda, A. (2023). 101 creative ideas to use AI in education, A crowdsourced collection. Zenodo. https://doi.org/10.5281/zenodo.8355454
Paul R. MacPherson Institute for Leadership, innovation, and Excellence in Teaching. (2023). Designing Assessments in the Age of Generative AI. Paul R. MacPherson Institute for Leadership, Innovation and Excellence in Teaching. https://ecampusontario.pressbooks.pub/mcmasterteachgenerativeai/part/designing-assessments-in-the-age-of-generative-ai/
Perkovic, I. (2023, October 23). How Do I Cite Generative AI? McMaster LibGuides. https://libguides.mcmaster.ca/cite-gen-ai
Queens University. Designing Authentic Assessments. Teaching and Learning in Higher Education. Retrieved October 30, 2023, from https://www.queensu.ca/teachingandlearning/modules/assessments/20_s2_12_designing_authentic_assessments.html
Surovell, E. (2023, February 8). ChatGPT Has Everyone Freaking Out About Cheating. It’s Not the First Time. The Chronicle of Higher Education. https://www.chronicle.com/article/chatgpt-has-everyone-freaking-out-about-cheating-its-not-the-first-time
Task Force on Generative AI in Teaching and Learning. (2023, June). Provisional Guidelines on the Use of Generative AI in Teaching and Learning. Academic Excellence – Office of the Provost. https://provost.mcmaster.ca/office-of-the-provost-2/generative-artificial-intelligence/task-force-on-generative-ai-in-teaching-and-learning/provisional-guidelines-on-the-use-of-generative-ai-in-teaching-and-learning/
Terry, O. K. (2023, May 12). I’m a Student. You Have No Idea How Much We’re Using ChatGPT. The Chronicle of Higher Education. https://www.chronicle.com/article/im-a-student-you-have-no-idea-how-much-were-using-chatgpt
University of North Dakota. (2023). AI Assignment Library. University of North Dakota Scholarly Commons. https://commons.und.edu/ai-assignment-library/
Introduction to Generative AI for Educators: Summary
In this module, we covered how to:
- Recognize the differences in AI models and tools and how they can be used.
- Use an AI tool to complete a teaching/learning task.
- Identify ways in which generative AI could impact teaching and learning in your classroom.
In their September 2023 article, Neil Krammaand and Sioux McKenna suggest that AI amplifies the tough question: What is higher education really for? They argue that focusing on identifying AI usage in students’ work and implementing a surveillance-driven approach ignores the broader purposes of higher education as being a space for nurturing transformative relationships with knowledge. By engaging in active exploration and thoughtful discussions about AI, we can cultivate a critical perspective on the potential implications and risks associated with AI. This allows us, as educators, to create an environment where responsible AI use is carefully considered and thoughtfully integrated when appropriate. Such an approach empowers students to navigate the ethical challenges posed by AI technologies in their future academic and professional pursuits.
If you’d like to continue your learning about GenAI at McMaster Univeristy or engage in conversations with others, check out Generative AI for Educators on McMaster’s Office of the Provost website for additional resources and events.
References
Kramm, N., & McKenna, S. (2023). AI amplifies the tough question: What is higher education really for? Teaching in Higher Education, 1–6. https://doi.org/10.1080/13562517.2023.2263839
Office of the Provost & Vice-President (Academic). (2023). Generative AI for Educators. Academic Excellence – Office of the Provost. https://provost.mcmaster.ca/office-of-the-provost-2/generative-artificial-intelligence/generative-ai-for-educators/