Typeface
- machine learning for individuation
- are fixed-width fonts still relevant to research?
- are fixed-width fonts still relevant for everyday use?
- fixed-width fonts for reading numerals (tables, clock time)
- better fonts and layouts in subtitles
- multi-language fonts that meet accessibility needs
- fonts for dyslexia and other reading disorders
- use machine learning to find or generate ideal fonts
- text for special cases / specific purposes (software coding, mathematics)
- customization of display and font for individual readers
- identifying the fonts best suited to training children to read
- does font variety increase engagement?
- better fonts and layouts in games
- individualized font adjustment
- individualized/adaptive fonts
- make learning to read fun through “cool” fonts
- see if people who prefer static fonts sit in the variable font space; slowly change static font features to the settings that actually help people
- what are the best ways to use generative tools to create new fonts?
- understanding the hierarchical structure that constitutes a font
- making fonts as much sensemaking as possible
- customizable reading
- have people create their own font / put variable font axes together and then see if this resulting customized font is their “best”/most efficient
- move crowding into larger space in perception
