Adaptive and intelligent instructional systems are used to deal with the issue of learning personalisation in contexts where human instructors are not immediately available, so their role is transferred entirely or in part onto the computer. Even though such systems are mostly developed for well-defined domains that have a rather straightforward acquisition order, such as mathematics or computer programming, they found their application in ill-defined domains as well. Natural language learning is one such domain, and developing adaptive instructional systems for this specific purpose is notoriously complex and challenging due to the nature of language systems. The paper at hand examines the theoretical background of adaptivity and intelligence in instructional systems, and discusses their application in learning and teaching of natural languages. Moreover, the paper reviews adaptive and intelligent language learning systems in existence and identifies their characteristics in a systematic manner. The discussion section offers a more detailed view of selected systems for language learning that exhibit interesting and innovative implementation solutions. The paper concludes by suggesting possible developmental directions and future work in the field.

Intelligent tutoring systems (ITS) are able to provide a personalized approach to learning by assuming the role of a real teacher/expert who adapts and steers the learning process according to the specific needs of each learner. This is done by taking into account a number of learner features and deciding on the best action to follow at any point in the learning process. Within the field of computer assisted language learning (CALL), the “computer-as-a-tutor” modality has been widely accepted for some time now, although it has long been overshadowed by the “computer-as-a-tool” modality. This was mainly due to the observable lack of interactivity of language learning systems and the inability of technology to show “intelligence”. However, the development of artificial intelligence in general, and natural language processing, user modeling and ITSs in particular, gave impetus for the development of the field referred to as intelligent CALL (ICALL). The paper at hand outlines and briefly discusses the issues surrounding the development and use of ITSs for language learning, taking also into account the broader context of (I)CALL, and gives an overview of such systems already in use.

Intelligent Tutoring Systems (ITSs) are inherently adaptive e-learning systems usually created for teaching well-defined domains (e.g., mathematics). Their objective is to guide the student towards a predefined goal such as completing a lesson, task, or mastering a skill. Defining goals and guiding students is more complex in ill-defined domains where the expert defines the model of the knowledge domain or the students have freedom to follow their own path through it. In this paper we present an overview of our systems architecture that integrates the ITS with data mining tools and performs a number of educational data mining processes to increase the adaptivity and, consequently, the efficiency of the ITS.

Since the creation of Web-based learning systems researchers have tried to make them adaptive to various characteristics of learners in order to increase the quality of learning. The application of data mining (DM) methods on learning system logs is often used as a basis of intelligent tutoring systems (ITS) that have the ability for automatic adaptation of some aspect of the learning process. Such a system was developed at our institution in the previous years and has been used in a number of courses. To improve our system we added an integration application to create a continuous feedback loop with a DM tool. Our goals, from the student's perspective, are to improve the quality of knowledge acquired by students as well as shorten the learning time by offering recommendations based on mined patterns of learning paths through the knowledge domain. From the perspective of teachers, our goal is to create a rich data visualization system (in the form of a Web application using standard Web technologies to create visualizations) to give them new insight into students' behaviours. In this paper we present the structure of our system and the research design that will be used to verify its results.

The integration of artificial intelligence (AI) into online education has transformed the digital learning space, offering new ways to enhance learner satisfaction and engagement. This systematic literature review, covering a five-year span from 2020 to 2025, explores how AI technologies, such as chatbots, intelligent tutoring systems (ITS), sentiment analysis, gaze tracking and predictive analytics, support learner engagement across cognitive, emotional, behavioral, and social dimensions. Drawing from 30 peer-reviewed studies, the current review addresses three central research questions: (1) What aspects of AI positively influence learner satisfaction and engagement in online courses within higher education institutions; (2) What potential challenges from using these technologies may arise; and (3) What research approaches are most commonly used to assess AI’s impact in such learning contexts? The findings highlight that adaptive learning, real-time feedback, and emotion-aware systems contribute positively to personalized learning and motivation. However, concerns persist around data privacy, algorithmic bias, over-reliance on automation, and system usability. Experimental and quasi-experimental designs, as well as machine learning, mixed methods, and survey-based approaches are found to dominate in reviewed studies. Based on these insights, this work offers a foundation for future AI-enhanced learning management systems designed primarily to enhance learner engagement across cognitive, emotional, behavioral, and social domains.

Over the last decade, language learning with digital technology in general, and mobile technology in particular, has received significant attention in both research and practice. This is mostly attributed to the learning-enabling aspects of mobile-as...