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Accelerating math accessibility with using AI

A yr in the past, NWEA, now a part of HMH, shared their revolutionary method to make math extra accessible for college kids. The intention was to determine the most important challenges and gaps in arithmetic for college kids who use display screen readers and refreshable braille units, as a result of classroom supplies aren’t all the time tailored to codecs corresponding to braille or massive print, and supplies aren’t all the time appropriate for a screen-reader navigation, voice enter, or a mix of those designs. NWEA developed prototypes that enabled display screen readers to work together with equations in a extra intuitive means, based mostly on a way known as course of pushed math (PDM). 

NWEA continued to innovate and construct on their earlier analysis to create alternative ways of presenting complicated math, particularly for math taught in grades six to 9. In addition they labored on alternative 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 components or areas. Every area, in addition to the entire equation, had a hidden label {that a} display screen reader would say to customers as they explored the equation or expression. When college students moved from one area to a different, they might hear a phrase that described the form of math in that area (for instance, “time period” or “fixed”). College students may then resolve to enter the area and listen to the precise math, or they may simply skip to the following area.

Using generative AI  

Through the use of AI, particularly GPT-4, the staff was in a position to enhance each the standard of the maths in addition to the time required to transform the equations to HTML, and to make use of code technology to write down the code for the primary prototype. The mannequin solely wanted a couple of examples to learn to change the preliminary check set of equations from MathML to the HTML construction that was probably the most accessible. From there, the mannequin required context to make sure that responses have been formatted in the easiest way for the app.  

Demo of utilizing the equations with a display screen reader:

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