As the SVP of Engineering at Instructure, AI integration and capability is top of mind. I’d like to share some of my thoughts on AI with you as we continuously explore this new technology and how it can benefit students and educators everywhere.
With AI, Context Matters
To get value out of AI like ChatGPT, sending the right prompt makes a big difference. An AI Prompt is any form of information–like text, a question or code–that tells AI what you are looking for. The better the prompt and context, the better the result. We want the tremendous benefits that AI promises to educators and students, but how do we balance data sharing with an AI with privacy rights, potential bias and data ownership?
We have been running a bunch of experiments with OpenAI’s programme interface for a while now. During these experiments, we have been impressed with the configurability of that interface to significantly tailor and tune the responses to be as useful as possible in the educational context. To successfully prompt these tools, however, we've found that they require the right context and data. A well-crafted prompt augmented with the right context produces correct outputs more often than just a prompt alone. Balancing this kind of data sharing against student and educator privacy must be first priority. As education technologists seeking opportunities to advance edtech, we must preserve data privacy and prevent bias in the use of AI.
Nuance vs. Bias
As an example of this balancing act and its impact on system correctness and bias, let's consider one of our experiments in this space. As all educators know, one of the most labour-intensive teaching activities is grading. One option we are exploring is an AI grading assistant. A tool like this could use a lot of data, for example:
- The original question
- The student’s response
- The grading rubric or ideal response
- Full or partial credit allowed
- Grade level
- Relevant teaching materials from the course
- English language proficiency level
As we increase the amount of data we send, we receive better, more nuanced answers from the tool. For example, if an instructor is asking for help creating a rubric about the Grand Canyon, what data can be sent to get a useful result?
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Prompt to ChatGPT |
How was the Grand Canyon formed? |
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Response |
The Grand Canyon is a remarkable natural wonder located in Arizona, USA. It was formed through the erosive actions of the Colorado River over millions of years. The formation of the Grand Canyon can be attributed to several key geological processes, primarily the uplift of the Colorado Plateau and the downcutting of the Colorado River. Approximately 70 million years ago, …(200 words later)… Overall, the formation of the Grand Canyon is the result of a combination of tectonic uplift and the persistent erosive forces of the Colorado River over a vast span of time. This unique geological history is what has shaped the stunning landscape we admire today. |
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Prompt |
How was the Grand Canyon formed? In summary for a college student? |
|
The Grand Canyon was formed through the combined processes of tectonic uplift and river erosion. Around 70 million years ago, the Colorado Plateau experienced uplift due to tectonic forces, exposing the rock layers. About 6 million years ago, the Colorado River began flowing across the region and gradually carved out the deep, narrow canyon we see today. The river's erosive power, aided by occasional flooding, eroded the sedimentary rocks, widening and deepening the canyon over millions of years. The Grand Canyon showcases a variety of rock layers that represent millions of years of Earth's geological history. |
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Prompt |
How was the Grand Canyon formed summarised for a 10-year-old? |
|
The Grand Canyon was formed a very long time ago when the Earth's crust moved and pushed up a big area called the Colorado Plateau. Then, a river called the Colorado River started flowing through that area. Over millions of years, the river kept flowing and carving into the rocks, making a deep and wide canyon. Sometimes, when it rained a lot or when snow melted, the river became very strong and made the canyon even deeper. That's how the beautiful Grand Canyon was formed! |
It’s clear that if I were a 5th grade teacher, the example response for a 10-year-old is much more appropriate. However, do we know what the AI considers to be a “10-year-old”? What if that 10-year-old is speaking English as a second language? Should we share that with the AI? What bias does that inject? Should edtech tools be in the position to collect and store that data?
Another Example in Action
In another iteration of the experiment, we tried to get the most value with the lowest amount of context sharing to balance privacy, bias and effectiveness by sending just the student response and the grading rubric.
The rubric sent to the model:
- Must mention that the Grand Canyon took around 6 million years to form.
- Must also mention something about the Colorado River.
- Must mention that there are 3 primary rock layer sets.
- Must describe the 4 states that the Grand Canyon runs through.
As you can see in these examples, additional context and extra data matters. It produces better responses and, in some cases, can defeat latent bias in the model. Before we send any piece of student, educator, or school data, though, we must ensure that we're proper stewards of your data and use it only in a transparent, ethical way with prior consent.
To add to what our Chief Privacy Officer, Daisy, shared in her post on the ethical use of AI, we also want to magnify the value that AI gives us by including context as we interact with the model. Our commitment is that when these features are enabled in our products, we will disclose all of the data that is included in our interaction with the AI. We believe that this disclosure will give confidence that
- No inappropriate bias is being applied
- The AI has enough context to provide as useful a response as possible
- We are committed first and foremost to protecting the data of students, educators and institutions.
As we continue our experiments and learn more, we look forward to sharing it with you.
Check out more on Instructure’s product approach when it comes to AI here.
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