Objective: This study aims to investigate and design an artificial intelligence-based curriculum model with an emphasis on adaptive learning, in order to enhance the quality and efficiency of the educational process through the personalization of learning pathways.
Method: This research employed a quantitative approach based on the analysis of educational data. The dataset included variables such as prior knowledge level, learning style, level of engagement, time spent on content, assessment scores, and type of instructional content. Learners' performance was analyzed to evaluate the effectiveness of adaptive algorithms in adjusting difficulty levels and designing personalized learning pathways.
Results: The results indicated a significant positive relationship between prior knowledge level and academic performance. Furthermore, the type of instructional content and learning style were found to influence learners' engagement and success. It was also revealed that adaptive algorithms are effective in adjusting content difficulty and delivering personalized learning pathways. In addition, data analysis enabled more accurate prediction of learners' needs.
Conclusion: The implementation of an AI-based curriculum can lead to improved learning quality, increased learner engagement, and enhanced educational efficiency. The findings confirm the necessity of utilizing educational data and intelligent technologies in the development of future curricula and suggest that innovative models can offer viable solutions to the challenges of personalized education.