A 12 months in the past, NWEA, now a part of HMH, shared their revolutionary strategy to make math extra accessible for college students. The intention was to determine the largest challenges and gaps in arithmetic for college students who use display readers and refreshable braille units, as a result of classroom supplies should not all the time tailored to codecs similar to braille or massive print, and supplies should 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 method, 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 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 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 might hear a phrase that described the type 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 crew 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 jot 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 had been formatted in the easiest way for the app.
Demo of utilizing the equations with a display reader: