I have a confession to make: My university degree sits atop a mountain of lies I told to mask my dyslexia. Now AI is positioned to make education more accessible so future students don’t have to lie to get where they need to go.
I read maybe a quarter of the required materials for my university studies. My term papers are filled with quotes from books I never even opened. I became a master at convincing my fellow students to give me summaries, quick explainers, and relevant quotes from materials I knew I’d never be able to finish in time to meet the inaccessible demands of academia. And after 5 years and a degree, I abandoned my hopes for a graduate degree in philosophy to pursue other avenues where my reading disability was not a constant blocker.
Today, as we stand at the beginning of a new era of computing, one shining beacon in the infinite possibility space of AI is making education more accessible.
AI can make education more accessible today
Here are a few ideas for how we can implement existing AI tools in education right now to dramatically improve accessibility:
- AI audiobooks on demand: AI can generate natural sounding audiobooks from any written text. Audiobooks of academic texts are hard to come by and prohibitively expensive. AI can solve that problem and allow the reader to choose their preferred reading modality. This tech already exists (Apple has shipped it).
- AI translations to any language: AI models are very good at translating text from one language to another. This means academic texts written in one language can now be accessible in any language. Again, the technology already exists in the form of Google Translate etc.
- AI reading level adjustment: You know that “explain it to me like I’m five” meme? AI language models like ChatGPT can do that, and ensure the salient points and meaning of the text is preserved. Academic texts are often superfluously arcane and turgid. I predict in the near future we’ll have browser plugins and other AI-powered services where you can set the reading level and writing style of any text to your preference and preserve its meaning.
- AI summaries on demand: Using summaries of long texts to enforce learning has a long tradition in academia. There’s a lucrative industry and pop culture mythology around services like Coles Notes and CliffsNotes. AI can be used to generate custom summaries from any text, large or small to make it more accessible to people like me who can’t read the whole thing.
- AI assessments, flashcards, and other learning tools: Dump any text into an AI and ask it to generate assessments, flashcards, questions, examples, or other things. The possibilities here are limitless.
- AI auto-captions and transcripts of audio, video, and in-person events: 5 years ago captions were an expensive nice-to-have very few could afford or cared about. Today, auto-captions are available on every LinkedIn and YouTube video, and most platforms also provide verbose transcripts on the fly. These same technologies are used for live captioning in video chat apps like Teams, and can be used at live events including in classrooms. The technology is available, and quite frankly I can’t think of any good reason this tech is not immediately implemented across all educational campuses world wide to provide improved accessibility. Imagine knowing you will have a transcript at the end of every lecture so you can focus on understanding what is being taught instead of just writing it down!
- The possibility space here is infinite!
Let me guess: You have concerns. About the accuracy of AI summaries and transcripts and translations. About whether leaving AIs to do this work will take jobs away from humans. About whether students relying on AIs will result in the students not learning anything.
Here’s my reality check to you: The alternative is students not learning at all. The alternative is students like me lying through their teeth to pass arbitrary tests of reading speed (that’s what mandatory minimum page counts are). The alternative is large numbers of people being excluded from education because we’ve chosen to tie access to education to reading ability and speed rather than literally anything that has to do with actual learning.
As we build our future with AI, our conversations about AI in education should be about how these technologies can lower barriers and make education accessible and possible for everyone.
Reading ? Learning
Much handwringing and professional dread has been published in the past months around the risks of AI models like #ChatGPT being used by students to plagiarize materials and generally cheat on their papers and exams. Headlines like “ChatGPT passes exams for MBA courses and medical licences” are addictive clickbait in an environment of economic uncertainty and pandemic fatigue. But look beyond the immediate “the graphing calculator / internet / smartphones / YouTube / chat apps / smart watches / AI will destroy education and plunge us back into the dark ages” response and you see the potential of this technology to make education not only better, but more available and accessible to more people. If we build the tools to make that future happen.
I have moderate dyslexia meaning I read at about speaking pace and when I read the cognitive load of just getting the words right and keeping track of where I am is exhausting. Reading a regular length novel cover to cover can take me 6 months. Reading a densely worded academic textbook is a multi-year project. But in university, the expectation was for me to consume up to 5,000 pages of material each semester. So while I never wanted to lie to my fellow students, my professors, and my academic advisors about my reading, I was forced to. Academia, and education in general, is built on a core assumption that people’s ability to read and read fast is tied to their ability to learn, grow, innovate, and build the future. I am walking proof this is not the case, and I’m also a prime example of how reading speed is a permanent blocker for many educational paths and the careers that follow.
Which is absurd because it is not the reading that matters, it’s understanding the ideas captured in the reading materials.
Enter AI
Last week I was trying to explain the ethics of Amartya Sen’s Capability Approach to a friend. One of the core ideas of Capability Approach is that people should have the capabilities and functionings to be and do what they value and have reason to value in their lives and environments. Which begs the question: What is the difference between what we value and what we have reason to value?
This is not something Sen answers directly. Instead it is something you glean from reading several of his published works. As with much of academic philosophy, this distinction is at the same time essential to understanding and lacking in common sense explanation.
So I asked ChatGPT: “What is the difference between what we value and what we have reason to value?”
Here’s what the AI answered:
“What we value refers to the things that we personally consider important or desirable, such as material possessions, relationships, or personal achievements.
What we have reason to value, as described by Amartya Sen in the capability approach, refers to the things that we have the capability to achieve and that are consistent with our personal values and goals. In other words, it refers to the things that we would value if we had the opportunity to achieve them.
For example, one might value owning a house, but may not have the capability to buy one due to financial constraints. In this case, having a safe and affordable place to live would be something they have reason to value. In this way, the distinction between what we value and what we have reason to value is one of actual opportunity or capability. The capability approach takes into account the real opportunities for people to achieve the things they would value if they had the capability to do so.”
This is extraordinary. Notice how the system not only provides an understandable and plain language explanation of the subject, but also manages to pick up on the obscure origins of my question without the prompt providing what I consider sufficient context?
This explanation of the difference between what we value and what we have reason to value is so good it should be used in introductory textbooks on Sen’s work. And it serves as an example of what AI can do for education if we build AI-powered systems for education.
And Sen’s distinction between what we value and what we have reason to value is salient to this issue: We as a society value education, and we have reason to value education for all. Even so there are millions of people around the world whose real opportunities to access education are limited for a an endless list of reasons. AI can grant us all the capabilities to access education in new and better ways, allowing more of us to do and be what we have reason to value in our lives, our schools, and our work!
Our future with AI is the future we build
We are at the beginning of the beginning of our future with AI. Ahead of us is an infinite possibility space. And more now than at any time in human history we have the capabilities and agency and communal connection and global wealth and power to build positive and meaningful futures for ourselves and those around us together.
It starts with looking beyond the perpetual shock of disruption every new technology brings to what those new technologies can do for us as we integrate them into our lives and our work. It starts with talking about the futures we want to build for ourselves and how we make them real. It starts with seeing the world, thinking about how to make it better, and then making it happen.
Header image: Screenshot of the Dyslexia Simulator from Harvard University.