This paper presents the ontological design and implementation of the differentiated learning environment in the domain model of an intelligent tutoring system for children with specific learning disabilities. It addresses the learners need for differentiated instruction in a preferential learning environment. The proposed model helps to identify the most affected learning domains and related multiple-criteria's which effects the learners. The learning resources and problems diagnosis questionnaires are organized and used with various learning strategies to create various learning environments such as case-based learning environment, game-based learning environment, practice-based learning environment and visual-based learning environment. Different techniques can define a set of rules to decide the most preferred learning environment. Here, multiple criteria decision analysis approach map the information, learning resources and learning environments to create a differentiated learning environment for the learning disabled. The contribution of proposed model is to reduce the gap between learner and learning habits with special needs. Our model is implemented as domain model of an intelligent tutoring system to develop learner-centric learning environment. In the designed intelligent tutoring system (ITS), the differentiated learning environment domain model is further evaluated and validated by a set of fuzzy rules. The pilot test result shows that proposed model enables an ITS to improve the implementation of appropriate learning strategies with high accuracy and sensitivity for both learning and non-learning-disabled users.