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Accelerating math accessibility with using AI

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Accelerating math accessibility with using AI

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A 12 months in the past, NWEA, now a part of HMH, shared their modern strategy to make math extra accessible for college kids. The purpose was to determine the largest challenges and gaps in arithmetic for college kids who use display readers and refreshable braille units, as a result of classroom supplies are usually not all the time tailored to codecs similar to braille or massive print, and supplies are usually not all the time appropriate for a screen-reader navigation, voice enter, or a mix of those designs. NWEA developed prototypes that enabled display readers to work together with equations in a extra intuitive means, primarily based on a technique referred to as course of pushed math (PDM). 

NWEA continued to innovate and construct on their earlier analysis to create other ways of presenting advanced math, particularly for math taught in grades six to 9. In addition they labored on other ways of outputting math that included screen-reader performance and refreshable braille units in each UEB (Unified English Braille) and Nemeth codecs. Furthermore, they developed a prototype for a voice-activated chatbot.  

To account for the accessibility of math equations, they used two markup languages, HTML and ARIA, to separate equations into elements or areas. Every area, in addition to the entire equation, had a hidden label {that a} display reader would say to customers as they explored the equation or expression. When college students moved from one area to a different, they’d hear a phrase that described the form of math in that area (for instance, “time period” or “fixed”). College students might then resolve to enter the area and listen to the precise math, or they may simply skip to the following area.
 

Using generative AI  

By utilizing AI, particularly GPT-4, the staff was in a position to enhance each the standard of the mathematics in addition to the time required to transform the equations to HTML, and to make use of code technology to put in writing the code for the primary prototype. The mannequin solely wanted just a few examples to discover ways to change the preliminary check set of equations from MathML to the HTML construction that was essentially the most accessible. From there, the mannequin required context to make sure that responses had been formatted in the easiest way for the app.  

Demo of utilizing the equations with a display reader:



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