What is ChatGPT? Exploring AI Tools and Their Relationship with Education

Artificial Intelligence (AI) and machine-generated content have become prominent in educational discussions. Amidst technical jargon and concerns about the impact of traditional writing and learning, understanding these topics can be overwhelming. This toolbox guide simplifies the generative AI landscape, providing clear definitions and insights into some commonly used generative AI tools.

What is Generative AI?

To provide an introduction to generative AI, CATL has created an informative video presentation. This video, paired with interactive PowerPoints slides, serves as a valuable resource for understanding how generative AI tools work, their capabilities, and limitations.

Introduction to Generative AI – CATL Presentation Slides (PDF)

Common Generative AI Tools

One of the most popular AI-powered text generators is ChatGPT by OpenAI. Since its November 2022 release, various companies have developed their own generative AI applications based on or in direct competitive with OpenAI’s framework. Learn more about common generative AI tools below.

Note: For UWGB faculty, staff, and students, we recommend using Microsoft Copilot and other tools that do not require users to provide personal information in the sign-up process.

What Can Generative AI Tools Do?

The generative AI tools we’ve discussed so far are all trained on large datasets that produce outputs based on patterns in that dataset. User prompts and feedback can be used to improve their outputs and models, so these tools are constantly evolving. Explore below to learn about general use cases for generative AI tools and their limitations.

Generative AI tools can be used in a multitude of ways. Some common uses cases for text-based generative AI tools include: 

  • Language generation: Users can ask them to create essays, poems, or code snippets on a given topic.  
  • Information retrieval: Users can ask them to answer simple non-academic questions like “explain the rules of football to me” or “what is the correct way to use a semicolon?”  
  • Language translation: Users can ask them to translate words or phrases into different languages.  
  • Text summarization: Users can ask them to condense notes from a lecture and or long texts, including entire books, into shorter summaries. 
  • Idea generation & editorial assistance: Users can ask them to brainstorm and generate ideas for a story or a research outline or provide feedback on writing to make it more concise or formal.  

However, these tools also have some limitations, including but not limited to:  

  • Lack of real-world understanding: They do not understand the context and/or logic of the real world. They do not understand sarcasm, analogies, jokes, and satire. For example, an output created by the technology may be grammatically correct, but semantically is nonsensical or contradictory.  
  • Dependent upon the data it is trained on: They may produce outputs that are not accurate, relevant, or current because they rely on the data they are trained on. 
  • False results or hallucinated responses: They may produce outputs that are false, misleading, or plagiarized from other sources, and are unable to verify the accuracy of their outputs.  
  • Machine learning bias: They may produce outputs that are discriminatory or harmful due to bias in the data they are trained on.  

The possibilities of tools like ChatGPT seem to be almost endless — writing complete essays, creating poetry, summarizing books and large texts, creating games, and translating languages and data. ChatGPT and its contemporaries can understand text and spoken words similar to how human beings can. These tools have become more conversational and corrective with each update, making it difficult to discern between what is generated by an AI and what is produced by a human. In addition, the data and algorithms they draw from imitate the way humans learn and can gradually improve their accuracy the more you interact with it. As explored above, they offer large potential in their use cases, yet they still come with their own set of limitations to consider.

What Does This Mean for Educators?

The existence of this technology raises questions about which tasks will be completed all or in part by machines in the future and what that means for our learning outcomes and assessments. Some experts are also discussing to what extent it should become part of the educational enterprise to teach students how to write effective AI prompts and use tools like ChatGPT thoughtfully to produce work that balances quality with efficiency.

One way to approach the conversation surrounding AI technology is to consider these applications as tools that educators can choose either to work with or without in their classes. Some may also consider teaching their students how to use these tools most effectively and/or integrating lessons on AI ethics into their teaching. With any teaching tool we look to incorporate, we must provide proper thought, scaffolding, and framing around what it can do and where it falls short so that students can use the tool responsibly.

Learn More

Explore even more CATL resources related to AI in education:

If you have questions, concerns, or ideas specific to generative AI tools in education and the classroom, please email us at catl@uwgb.edu or set up a consultation!

Readings and Resources About AI in Education

To help you as you research and explore AI tools, we have provided a list of resources and additional readings on the topic of Generative AI technology below.

Additionally, CATL developed a GenAI checklist for instructors that will help you assess the extent to which generative AI will affect your courses and provide guidance on steps for moving forward.

Generative Artificial Intelligence In the Classroom

ChatGPT, built on the GPT-4 system, and other Generative AI platforms, offer unique opportunities for instructors and students to leverage the technology while still providing robust, comprehensive learning experiences. However, some instructors are apprehensive about its potential misuse by learning activities. Below you will find a variety of resources on how to use generative AI in classroom activities, with examples of activities that may not require any usage of AI.

Add a Generative AI Syllabus Statement

Incorporating Generative AI

Working Around Generative AI

Additional Resources on Assessment and Generative AI

Learning to Use AI Yourself

Playing Around with AI

Additional Commentary on AI (Articles, Podcast, etc.)

Other Center Resources

Learn More

Explore even more CATL resources related to AI in education:

If you have questions, concerns, or ideas specific to generative AI tools in education and the classroom, please email us at catl@uwgb.edu or set up a consultation!

Supporting First-Generation College Students (Mar. 24 & Apr. 28, 2023)

CATL is excited to partner with Lisa Lamson, Assistant Teaching Professor in Humanities and 2022-23 EDI Consultant, to offer two workshops this spring on supporting the success of first-generation college students.

Transparency in Syllabus Design for First-Gen Student Success (Mar. 24, 1 – 2 p.m.)

The first of the two workshops, Transparency in Syllabus Design for First Gen Student Success, will be held in person on Friday, Mar. 24 from 1 – 2 p.m. in the Alumni Room (University Union 103). This workshop addresses the whys and hows of syllabi – how can faculty best utilize the first-day foundational document throughout the semester to best support first-generation students as they navigate college? Despite best efforts, there seems to be a disconnect between how faculty see class syllabi and how students engage with the syllabi. This workshop intends to act as a bridge to help faculty articulate how their syllabi and learning outcomes shape the learning experiences throughout the semester and how it connects to their “genre knowledge” to help students see the value in a syllabus. In doing so, this workshop seeks to help faculty support first-generation students’ sense of belonging in the classroom and in the university by making clear the activities in the classroom’s connection to the university’s learning outcomes and beyond.

Unpacking the Hidden Curriculum for First-Generation Student Success (Apr. 28, 1 – 2 p.m.)

The the second workshop, Unpacking the Hidden Curriculum for First-Generation Student Success, will be held in person on Friday, Apr. 28 from 1 – 2 p.m. in MAC 107. “How do you know what you know?” – “Hidden Curriculum,” or the unspoken expectations of college in and outside of the classroom, often acts as a barrier to first-generation student success. While much of the academic scholarship on the “hidden curriculum” focuses on student experiences, this workshop intends to bring the conversation explicitly into the classroom – how can we uncover the information we, as faculty, just know and translate that for our students? How can we teach something we have learned through doing? This workshop proposes an opportunity for faculty to articulate the hidden “just knows” for their classroom to improve student achievement and, ultimately, success. Working through an assignment of their choosing, faculty will identify hidden expectations in their assessments and rubrics, and develop ways to make clear how the expectations of the assignment align with the course outcomes and beyond.

Small Teaching: Ways to Make Quick, Impactful Changes on Student Learning

While the spring semester is now partially completed, it is still critical to engage in reflective practices as a constant component of teaching students. While analyzing how your courses have gone throughout the first couple of months and looking to make improvements throughout the remainder of the semester, you may notice small changes you can make to adapt your curricular delivery, assignments, or assessments for the betterment of student learning and engagement. In February, we posted on The Cowbell a blog post that centered around the TILT (Transparency in Learning and Teaching) Framework. In this blog post, we will take the tenants or ideas of the TILT framework a step further, and focus on ‘small teaching’ – ways to incorporate a one-time modification or intervention that can be done in a period of no more than 5-15 minutes. 

James Lang wrote about small teaching in 2016 with his book entitled Small Teaching: Everyday Lessons from the Science of Learning. The book has since published a second edition. In it, he contends that for anything to be designated as an impactful technique regarding small teaching, it first must be accessible. This accessibility includes the ability for the technique to be translated for every content delivery mechanism, from small group instruction to large lectures. Secondly, it requires minimal prep and grading. This ensures that it is a small and incremental change, rather than a complete overhaul. Lastly, it must be foundationally rooted in the learning sciences.  

One adaptive instruction technique that embraces the small teaching criteria is to frame your curriculum with predictive questioning for analysis and background knowledge. This effectively challenges the students to go beyond their current level of understanding and ability to critically analyze and predict. If the prediction is incorrect, students can begin to analyze why they thought that way, where they may have thought differently, and develop a deeper understanding of what the correct response would be and why.  

Much like in the world of academia, the same patterned learning can be found in real-world examples. If you have ever taken leftovers from a meal and predicted incorrectly at what size container to utilize, or you have stepped out on an ice-covered driveway only to realize a better pair of shoes may provide more grip, you have engaged in predictive living. Our lives are in a constant predict-detect-correct cycle of learning. There are several ways predictive learning can be utilized in the classroom in small ways. You can activate prior knowledge through pre-quizzes or writing prompts, utilize polls or informal class predictions, or as a closing discussion about predicting upcoming lab experiments and results. These can be followed up with short discussions at the beginning of a future class. 

Another minor change that can impact your students is the practice of information retrieval. Dr. Pooja K. Agarwal has done extensive research around memory and retrieval practice, and in a recent publication of Educational Psychology Review, she concluded that “retrieval practice improved learning for a variety of education levels, content areas, experimental designs, retrieval practice timing, final test delays, retrieval and final test formats, and the timing of feedback” (p. 1427). This sort of retrieval practice lends itself to long-term learning, rather than short-term success. 

There are a few ways you can effectively implement retrieval practice in short amounts of time. For example, implementing small quizzes at the end of each Canvas module can help lead to a greater depth of understanding. This could be utilized for several modalities, such as asynchronous online teaching, for a version of conditional release to move on through other modules. You could implement a short writing analysis of the current day’s lesson and information presented, or, to achieve a similar result, conduct an ‘exit ticket’ question as students wrap up class for the day.  

These are just some of the ways to utilize small, incremental changes that provide deeper learning and student understanding to be enhanced. It is important to keep in mind that any sort of small teaching modification should continue to be aligned to course expectations and learning outcomes as students will be more successful when it is done with consistency in a holistic sense to maximize its impact.

If you would like to learn more about how to use the tenets of small teaching within your own course design, feel free to contact the CATL office by email (CATL@uwgb.edu) or schedule a consultation with us. If you are interested in reading more about small teaching and the science of learning, CATL has copies of Small Teaching: Everyday Lessons from the Science of Learning available for checkout as well. 

Call for Teaching Enhancement Grant Proposals (Due Friday, April 7, 2023)

Teaching Enhancement Grant: Open to faculty and instructional academic staff seeking to enhance their teaching skills or develop innovative teaching strategies. Applications due Friday, April 7.

The Instructional Development Council (IDC) is accepting applications for Teaching Enhancement Grants (TEG), through support from the Center for the Advancement of Teaching and Learning. The Teaching Enhancement Grant program is designed to support professional development activities that will enhance a faculty member’s teaching skills or result in the development of innovative teaching strategies.

Faculty and instructional academic staff whose primary responsibility is teaching for the current academic year are strongly encouraged to apply! Applications are due Friday, April 7, 2023. Click the button below for full details. If you have any questions about the application or TEG, please email the Instructional Development Council at idc@uwgb.edu.