# Intelligent Educational Environments: Recent Trends, Modeling, and Applications
## 4 Intelligent Educational Environments--Overview and Key Concepts
### Intelligent Tutoring Systems  
Intelligent tutoring systems (ITSs) were developed to support the direct application of AI to create personalized and adaptive learning. ITS lays the ground for intelligent education. They aim to facilitate teaching by modeling all the learning and tutoring processes (including tutoring knowledge) and emulating human tutor tasks, including knowledge presentation, problem statement interpretation, problem-solving process, and assessing the validity of the problem-solving process [39; 40]. ITSs are computer-based educational systems that use various information about each student and knowledge models and utilize AI techniques to adapt the learning experience to the student's needs [41]. Usually, ITSs provide personalized tutoring, learning, or feedback to learners, often in the form of a one-on-one tutor. Much research discusses concepts related to ITS. Here is a summary of key issues and features of intelligent tutoring systems derived after a comprehensive analysis of scientific sources [39; 41; 42; 43; 44]. * Domain Model: It represents the learning content (usually in a machine-processable way) and provides the structure and knowledge needed to present lessons, problems, and feedback. * Expert Model: The best possible model of expert knowledge in the domain from a scientific point of view that provides the system with correct answers, solutions, and reasoning strategies. It helps evaluate the students' answers, i.e., the actual knowledge obtained, and identify gaps in their knowledge. * Student Model: It represents the learner's current knowledge, skills, psychological properties, preferences, and learning progress. It tracks individual performance and the evolution of preferences, learning styles, and behaviors. * Tutoring Strategy/Pedagogical Module: It manages the overall teaching strategy and adaptation to the learner's needs. It defines how the system presents and sequences the content, how feedback is delivered, and how it is used for personalization. * Assessment and Evaluation Module: This module assesses students' understanding of the material over time and evaluates their progress. It uses assessment tests, quizzes, or tasks to reveal whether and to what level the learner has achieved the learning objectives. Diagnostic tests, formative assessments, or summative assessments can also be included. User Interface: It interacts directly with users (students, teachers, or domain experts). It presents learning content, allows students to input responses, provides feedback, and ensures professional manipulation of knowledge models. * Inference Engine: It interprets the data from the user input, the student model, and the domain model to make decisions about the next best actions (e.g., presenting problems, offering hints, adjusting difficulty). It uses logical reasoning or machine learning techniques. * Feedback System: It provides immediate and constructive feedback based on the student's actions and answers. Feedback can be corrective, encouraging, or suggestive (offering improvement strategies). It may also suggest hints and explanations or prompt the learner to think critically. Thus, ITSs usually integrate several of the abovementioned components, enabling intelligent education. However, in practice, a variety of experimental ITSs were developed, implementing only partially some of the discussed components because of the complexity and difficulties in realizing one universal ITS. Intelligent tutoring systems are relatively old for supporting personalized tutoring and learning, but their architecture is well-developed, very flexible, and adaptable to the requirements of modern intelligent education. Recently, significant research has been conducted on integrating ITSs with technologies based on modern large language models. Both the architecture of LLMs and ITS are used in Hypergraph-Based Intelligent Tutoring Systems and Conversation-based personalized tutoring systems.