Operational & Theoretical Overview for Using a Large Language Model [Resource]
Operational & Theoretical Overview for Using a Large Language Model [Resource]
Mary Landry
What You Will Learn in This Section
This section is designed to build confidence about what Generative Artificial Intelligence (GenAI) means for the future of education by closely studying the operations, limitations, and theoretical value of a Large Language Model (LLM) like ChatGPT. To this end, this section seeks to explain what language modeling is and how this process contributes to an LLM’s tendency to generate inaccurate information. Additionally, this section considers how the design of an LLM—specifically, the collective knowledge it is trained upon—can contribute to the perpetuation of biases. Lastly, this section encourages critical thinking about the value of an LLM from a theoretical standpoint regarding the writing process and collaborative learning. By the end of this section, you should be able to articulate how an LLM like ChatGPT operates, as well as the value and limitations of this design within the evolution of learning.
This chapter is divided in the following sections. Click the links below to jump down to a section that interests you:
- Author Reflection: How has my understanding of GenAI evolved?
- What does GenAI mean for the future of education?
- What is ChatGPT? How does it even work?
- Why does it make stuff up though?
- If it generates inaccuracies, why even use it?
- Why should educators share operational and theoretical considerations with students?
- What operational and theoretical considerations should educators share with students?
Author Reflection: How has my understanding of GenAI evolved?
The work of this section is a centaur—part me, part ChatGPT. This hybridity is the natural embodiment of the theoretical ideas I discuss: working with ChatGPT as a collaborator to augment the self-dialogue in my writing, all the while navigating its operational and ethical limitations. I wrote, and ChatGPT revised. I posed different questions, and ChatGPT generated new angles. I received external feedback, and ChatGPT produced possible ways to address the feedback. So the work went on—a fusion of human intuition and AI insights. In truth, this synergy was familiar to me. As an individual with Usher’s Syndrome—a genetic condition that affects eyesight and hearing—I already live in a blended world of balancing my deaf gain and peripheral blindness within normative expectations and environments. To live in such a blended world, being able to adapt has been an essential skill—sometimes for better, sometimes for worse. I have been both empowered by adaptation and made keenly aware of societal norms that necessitate adaptation. With this experience, I approach ChatGPT the same way I approach captioning devices at a movie theater: How can technological progress be a source of empowerment rather than forced adaptation? The answer lies in prioritizing human needs—in all their shapes and sizes—at the center of responsible AI development. With