Personalized Learning Path Planning in Educational Information Systems: Research on a New Model for Achieving Quality Education

This research investigates the current state of a personalized learning path planning model for integrating into educational information systems in order to improve the quality of education through the use of AI and ML. The intended outcome was to compare the effectiveness of the personalized model in terms of academic achievement and engagement levels, student satisfaction in comparison to the conventional form of learning. A quasi-experimental design was employed, involving 200 students divided into two groups: an experimental group that implemented The findings revealed an enhanced mean score of 16.2 points in achievement for the experimental group and 7.1 points for the control group. Moreover, the students in the experimental group showed improved activity levels, time on task and number of turns, and educational content use. Overall, student satisfaction tangent was higher for the personalized learning model due to significant enhancement of course content, speed, and system convenience. By the end of the course, low-ability students demonstrated the greatest amount of academic gain; the AI-driven system often modified learning paths. However, some difficulties connected with accessibility and quantification of interaction were revealed, which underlined the significance of the enhanced approaches to the individualisation of learning. Finally, the study supports the enhanced use of educational outcomes with the help of personal learning path planning based on artificial intelligence. Further studies should be directed toward practical use of these technologies to extend this opportunity to various students and definite analysis of the possible effects of personalized environment in learning systems on students’ critical thinking and creativity.