This paper describes a system for the dynamic generation of learning objects in support of individually personalized teaching, which represents intelligent and adaptable e-learning systems. E-learning systems that aim to take on the role of teachers are desirable to be adaptable to each individual according to selected criteria, so in the title itself the term individually personalized teaching is used where personalization is a higher gender term and individualization as a lower one, teaching applied to the needs of one person, ie teaching that respects the individual differences of students. This requirement for the e-learning system, which is intended to support individually personalized teaching, places a number of additional requirements in its construction, often using some of the decision-making methods in assigning learning objects to the user according to selected criteria. Previous research shows that the decision-making method most often used is the decision-making rules that the authors of the system themselves define, and the primary criterion for adjustment in systems is the acquisition of knowledge. For the purposes of this thesis, a study was conducted on the choice of adjustment criteria in the teaching process in classical teaching. Based on the obtained results using the multicriteria method of decision-making of the analytical hierarchical process (AHP), a decision was made on the choice of adaptation criteria applied in the system: cognitive style (approach strategy), learning style (visual, auditory and kinesthetic) and level of learning objectives Bloom's taxonomy (6 levels). As the main purpose of the application of such systems is to learn as efficiently as possible, to acquire knowledge, it is necessary to assess the criteria of adaptation in the learning objects used in the system by an appropriate method. A method for assessing learning objects according to selected adaptation criteria was developed, and research was conducted using the Delphi method, the final result of which is an instrument for assessing learning objects. The built system for dynamic generation of learning objects to support individually personalized teaching consists of several basic modules such as user interface, initial student module, student module, evaluation module, course domain, decision rules module and content generation module (learning objects). For the purpose of an individually personalized algorithm for selecting learning objects according to defined adaptation criteria, a multi-criteria decision-making method, analytical network process (ANP) and a method for structuring decision-making problems (DEMATEL) were used. A study was conducted on the effectiveness of the system among students of the University of Rijeka, University of Split and Polytechnic of Rijeka, which found better knowledge acquisition in those students who used the system with an algorithm for dynamic allocation of learning objects than those who did not.