Generative AI in Teaching and Learning: Definitions

Research into the implications of using generative AI tools in higher education has started to emerge. In 2023, a survey was conducted on the use of chatbots and other AI tools among groups of students in Sweden. The majority of students reported positive experiences in using these tools and believe that they make the learning process more efficient. At the same time, more than half of the students express a concern about the impact of chatbots on future educational experiences, but are less concerned about other AI-based language tools. More than sixty percent of students believe that using chatbots during exams is cheating, but the majority of students are against a ban on AI-based tools in education. Here is a brief summary of the survey.  

As reported by the Daily Texan at the University of Texas at Austin, UT students demonstrate a similarly mixed understanding of the benefits and challenges posed by the use of these tools. Mechanical engineering freshman Simon Gross discussed how he has benefited from using ChatGPT, but also provided insight into certain ethical considerations around ChatGPT. “I think the main, best ethical use of ChatGPT is for generating ideas,” Gross said. “I think other ways that it’s pretty useful is you could say, create a template for an email … you’re not asking it to write the email. You’re asking it to help you write the email.” 

“You attend college for a reason, and it’s not just to complete your assignments,” Gross said. “If you’re getting ChatGPT to write your essays and emails for you, you’re not becoming a better student. While ChatGPT might be able to do a lot of things for you, it’s still important to learn how to do those things for yourself.”

The emergence of new and accessible AI tools has different implications for different subjects and disciplines. It is therefore important that disciplinary-specific and professional communities assess the use of such tools in their subjects and areas of study. Legal frameworks, regulations for plagiarism and cheating, and for citing sources must be respected.  

Connecting university learning and professional expectations

Aligning classroom-based uses of generative AI tools with disciplinary-specific skills and the expectations of professional communities can contribute to how and to what extent these tools can be used in teaching and learning. Indeed, connecting college performance to career expectations is a powerful argument for the exploration of generative AI tools with students.

Implications for assessment

Assessment is clearly a major concern when it comes to the use of these tools. At the extremes, on the one hand, there is "no use" of generative models, and on the other, "extensive use", where the generative model has produced the entire content. Depending on which line you take in your own subject, gray areas may emerge as to what is acceptable use. If you suspect your students are using these tools in an inappropriate way, you may contact Student Conduct and Academic Integrity in the Office of the Dean of Students at UT.

In the case of forms of assessment other than classroom exams, instructors could consider which tasks are given to students such that they cannot easily use generative models to complete the tasks. 

In the short term, instructors can emphasize forms of critical reflection that bring out the students' points of view, make thought processes visible during course work, and which encourage the creation of something new. Assessment forms linked to various formats such as video, presentations and oral assessments, project work, portfolio assessments or assessment forms where students receive feedback along the way and improve their work can also limit the possibilities to areas that the instructor clearly indicates as acceptable.

In the longer term, instructors may wish to consider shifting their focus from a strict control of learning environments to encouraging the process of learning and thus exploring how these tools can be included to support learning.

References

Chaudhuri, T. (2023, February 16). Reconsider ChatGPT for academic use. The Daily Texan. https://thedailytexan.com/2023/02/16/reconsider-chatgpt-for-academic-use/

Malmström, H., Stöhr, C., & Ou, A. W. (2023). Chatbots and other AI for learning: A survey of use and views among university students in Sweden. (Chalmers Studies in Communication and Learning in Higher Education 2023:1) https://doi.org/10.17196/cls.csclhe/2023/01

What’s the future of generative AI? An early view in 15 charts. (2023, August 25). McKinsey & Company. https://www.mckinsey.com/featured-insights/mckinsey-explainers/whats-the-future-of-generative-ai-an-early-view-in-15-charts 

In instructional settings, the benefits and costs of the use of generative AI tools must be carefully weighed, as these tools provide both an opportunity to enhance student learning as well as deeply challenge it. For example, in their "Statement on Artificial Intelligence in Writing Flag Courses," the Center for Skills and Experience Flags at UT-Austin states:

"Writing is an iterative process requiring drafting, feedback, and revision. Human feedback, specifically, is required for writing skills development, because writing is a process of communication between human subjects, with social and emotional components. AI might be used to augment some of these processes in more formulaic writing, serving as something like a template for a human writer (for example, to generate a draft of a letter that indicates the type of content that might appear in the letter but requires human discernment to evaluate those choices, add specific ideas and detail, and confirm the letter’s accuracy), but it should never replace them. Because good writing requires human input, writing that lacks such input will usually fall short of college-level standards for success. 

In addition, the Office of Academic Technology (OAT) at UT-Austin, in their "Addressing the Limitations of Using Generative AI for Learning" resource, has identified six limitations to the use of GenAi in teaching and learning: "the purpose of generative AI is to create brand-new content. Synthetic content can lead to wonderful, creative output – and that same output is often rife with limitations. Being AI-forward requires being aware of and taking action to mitigate the limitations." 

Beyond the "hallucination" effect that these tools generate (confidently asserting facts or sources that do not exist), these tools can construct larger patterns of social, cultural, gender-based, and ethnic discrimination. As shared by the Center for Teaching and Learning at Queen's University, in the graphic below, Rebecca Sweetman (2023) highlights some of the harms that need consideration, including environmental harms, economic harms, as well as epistemic harms. Click on the image in order to be taken to an interactive form of this graph.

References

Calvert, B. (2024, March 28). AI already uses as much energy as a small country. It’s only the beginning. Vox. https://www.vox.com/climate/2024/3/28/24111721/climate-ai-tech-energy-demand-rising

Luccioni, S. (2023, April 12). The mounting human and environmental costs of generative AI. Ars Technica. https://arstechnica.com/gadgets/2023/04/generative-ai-is-cool-but-lets-not-forget-its-human-and-environmental-costs/

Sweetman, R. Some Harm Considerations of LLMs | eCampusOntario H5P Studio. (2023, March 2). https://h5pstudio.ecampusontario.ca/content/51741 

What is Generative Artificial Intelligence (AI) | Centre for Teaching and Learning. (n.d.). Centre for Teaching and Learning, Queen's University. https://www.queensu.ca/ctl/resources/educational-technology/generative-ai-teaching-and-learning/what-generative-artificial