Using Generative AI Reflectively and Responsibly in Teaching and Learning

A majority of students surveyed (61%) agree or strongly agree that they understand how GenAI can enhance their learning; at the same time, 78% agree or strongly agree that they understand how the use of GenAI can be detrimental to their learning (UCUES, 2024).

Possibilities of GenAI

Potential in teaching and learning

Instructors who are interested in incorporating GenAI into teaching and learning may see its potential benefits in making personalized learning feasible and scalable, as well as how the technology may support them in their teaching, relieving them of repetitive tasks (Hammond, 2024). There is potential for instructional content to be tailored to individual students’ needs, and for students to interact with GenAI chatbots and tutors, which may provide more automated grading and feedback (The role of AI, 2024).

Importance to students’ career readiness

Education leaders argue that because their future careers will require proficiency with the technology, universities need to teach students how to use GenAI tools effectively and responsibly (Schroeder, 2024).

Drawbacks of GenAI

Biased and inaccurate output

As shared during the UCLA Virtual Town Hall: What is ChatGPT and How Does it Relate to UCLA’s Academic Mission, there are concerns about the ethics and practices around GenAI tools. As discussed by Noble (2018), algorithms can and do replicate and produce biased, racist, and sexist outputs, along with incorrect and/or misleading information. 

These technologies are not infallible, and their accuracy is subject to a variety of factors, some listed below:

• Prone to fill in replies with incorrect data if there is not enough information available on a subject.
• Lack the ability to understand the context of a particular situation, which can result in inaccurate outputs.
• Large, uncurated datasets scraped from the internet are full of biased data that then informs the models.
• Data is collected from the past, and tends to have a regressive bias that fails to reflect the progress of social movements.

Environmental concerns

Another serious concern about GenAI tools relates to their environmental impact. Data centers for GenAI tools require huge amounts of raw materials, including rare earth elements, and produce electronic waste containing mercury and lead (AI has an environmental problem, 2024). Data centers also use and will continue to use massive amounts of water, with a projected water usage of 6.6 billion cubic meters by 2027 (Gordon, 2024). It is estimated that the carbon footprint of a single GenAI query is four to five times higher than using a search engine (Stokel-Walker, 2023); this does not take into account the energy consumed and CO2 produced to create each GenAI model.

How?

Before discussing GenAI tools with your students, creating a GenAI policy for your course, or deciding whether to implement tools in your class, consider taking some time to reflect on your views, interest in, and comfort level with GenAI.

The UC Instructional Design & Faculty Support Community of Practice’s AI Working Group created a chart with guiding questions for instructors relating to AI: AI Faculty Consultation Chart. The chart asks you to reflect on your experience with GenAI, its relevance to your course, your ethical concerns, and how and where you might rethink your course to incorporate GenAI. Consider working through these questions, and if you have further questions or concerns, you can get in touch with an instructional designer to discuss with you.

Create and communicate GenAI policies

Surveys and Discussions

Consider utilizing the questions on AI from the University of California Undergraduate Experience Survey in a survey with your students, or holding a discussion with your class exploring these topics. Learning about what your students know about GenAI, and how they currently use it, can inform whether you integrate these tools into your teaching, and can shape your course policies on GenAI tool use. 

We have created a sample survey of these UCUES questions, which you can find in Canvas Commons and import into your course. Filter Canvas Commons results for resources shared with the University of California, Los Angeles, and search for “GenAI Student Survey.”

Syllabus Statements

Make your course’s GenAI policy clear in your syllabus. Consider forming a policy in collaboration with your students at the beginning of the quarter, which can foster a sense of belonging among students in your course (see Preparing to Teach: Fostering and Sustaining Student Belonging Throughout the Term for more information on student belonging).

Below are three example syllabus statements adapted from UIC’s AI Writing Tools guide that instructors could include in their syllabus, as well as guiding questions to help you determine what to include in your AI statement.