Mathematical texts, often learning texts, are generally “perceived” graphically: a receiver reads the texts and formulas with his or her eyes and makes sense of it. How do persons using a different channel do?
The current technology makes it possible to claim to “just read-aloud” but a lot of hurdles remain before the perception process is on par with the reading process when mathematics is involved. Formulas in particular can be read-aloud with specialised systems but we are far from a mainstream practice in many languages.
The case that interests us is the learning process in a university of applied sciences where many different mathematics is being taught. The presentation will report on a survey on the expectations and the experience so far. We aim at answering questions such as:

• How complex can a formula be, for it be understandable when first read?
• How do various tools compare in their fidelity, trust, and understandability when reading which formulas?
  In particular, can generative AI bring enhancements or losses?
• Which usage situation is plausible to listen to texts with formulas? Jogging? Learning at a desk without reading? Driving?

The presentation will demonstrate how MathML-enabled read-aloud works with state of the art technology and what enhancements can be made to make the perception of a text with mathematics an integral part of an effective learning process. In particular, it will echo scientific experiments which indicate features or aspects in very diverging manners.