EduAdapt: An Exploration of AI in Education
How might AI Education tools aid students with learning disabilities?​​
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Despite technological advancements in education, students with disabilities frequently rely on conventional adaptations (such as note-taking services), which may not entirely meet their needs in the classroom. Many recent studies and interventions have focused on disability diagnostics, leaving an absence of technology that helps students who are already aware of their disability.​
In collaboration with Esther Fishman
AI Education and Learning Disabilities
Why AI?
Considering the rise of AI and advanced digital education tools, we wanted to explore how AI might help students with disabilities where previous systems may have failed.
Though already existing products like ChatGPT and Khanmigo show great promise, we feel that the specific needs and concerns of our stakeholders (teachers and students with disabilities) need to be taken into account.
AI Education Research
Preliminary research on AI and education for students with disabilities indicated a noticeable gap in the field, with studies primarily clumping around 2006, and then 2014–2016. The majority of these studies were focused on diagnostics, a topic we did not address in this project. ​
Informed by research, we also laid out ethical considerations for our prototype:
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​Inherent biases found in AI algorithms, especially those concerning learning disabilities and education
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Student privacy, particularly around disability and demographic information
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The role of teachers using this prototype, as we want to ensure we are not replacing educators
​Further research confirmed 3 key points:​
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​There is a lack of research concerning AI and digital interventions for students with disabilities.​
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Digital Interventions can help students with disabilities more than non-digital interventions.
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Interventions should consult teachers as well as existing educational theory.
How Does EduAdapt Work?
Audience:
Based on initial feedback from educators and peers, we decided to refine our focus to address college students with ADHD. Creating a platform with a specific disability in mind helped us avoid a one-size-fits-all solution, as we could implement tools proven to help students with ADHD.
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Although the American Psychiatric Association does not classify ADHD as a learning disability, students with ADHD, specifically in higher education, face significant barriers to academic success. Despite this, ADHD is frequently overlooked in educational disabilities studies.​​​​​​
​​​Research-Backed Features:
​EduAdapt is a prototype of an AI system built to enhance learning experiences for students with ADHD by providing personalized support. We decided to make EduAdapt a website in order to more easily connect it with educational tools, such as Learning Management Systems, in the future.
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​Both Digital Interventions and Organizational Tools are proven to help students with ADHD, and we took this into account when creating EduAdapt. For example, we implemented a schedule organizer to help students keep track of classes and assignments, as well as progress bars to encourage students to maintain focus.
Our primary focus, though, was an AI-powered chat trained to help students easily sort through and study coursework.​ This working prototype shows one function of EDUAdapt in the form of quiz generation.​​
Interactive Figma Prototype AI Chat Flow
Next Steps and Limitations
Integrating Educators
Involving teachers is essential to improving student learning and ensuring that digital resources are handled appropriately. However, there isn't a clear way to integrate teachers into our current prototype. Responses from colleagues point to the need for a clearer way to introduce the tool to educators. This would make it easier for teachers to seamlessly add their thoughts and modifications to the individualized learning plans that our AI system generates.​
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Prototype Evaluation
Although there are pre-established advantages to our prototype, there are still a lot of unanswered questions about how students would feel about our idea. We were not able to conduct enough user testing, as we were operating without proper, ethical access to students with ADHD.
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Next Steps​
Our next step would be to integrate teachers and existing tools into our system. ​To further ensure our tool's usability, we would want to test it extensively with both faculty and students. By doing this, we can improve our product, help teachers more successfully serve their students, and make sure we are truly meeting our goal of enhancing the education of students with ADHD.