Artificial Intelligence Aspects in Developed E-Material Formatting Application

Despite technology and screen use benefits in education providing and supporting, lots of users are having complains after long screen reading. It is based on the slower evolution of humans' perception system and reading paradigms to new reading conditions. Followed new public health problems of nowadays related to screen-reading, and users' needs are thought of new content-presentation improvement is need. Methodology: Literature research of AI approaches, app prototype descriptive analysis, and simple comparison analysis of app and theoretical AI approaches. Results: Newly developed application prototype for e-material formatting is created to improve screen-reading abilities and comfort and improve learning processes. The app works by using several AI approaches and elements: machine learning, perceptron, decision tree graphs, rule-based system, classification, and deep learning. The app collects data and analyses them based on the training database. After, app categorises data by decision tree method. Finally, it decides for formatting recommendation to suggest and make appropriate document formatting. The app receives users' feedback after use. Conclusions: The app uses all collected data for the deep learning process to improve personalised recommendations to create the user-centred design. Currently, it is a narrow range use app designed for e-study use on MOOC type platforms for user group without specific limitations or disabilities. The app is with several level formatting possibilities, including deeply personalised formatting. That create and provide more effective e-materials as it increases visual perception, legibility, readability, reading comprehension, and memorability of content. It is a learner-oriented education methodology with an AI approach.