Comparative study evaluates digital reading tools using adaptive algorithms for children's learning outcomes.
These digital tools draw on concepts from psycholinguistics, as well as knowledge about the stages of language development in childhood, in order to propose adaptive algorithms. These algorithms take as input multiple parameters (students’ level, speed or decoding scores, elements of spoken reading such as fundamental frequency) and produce as output a text intended to be readable for a given age and profile (for example in terms of average sentence length, syntactic embedding, or level of abstraction of the story).
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Andrea Briglia (2026) studied this question.
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