Biosensor technology for adaptive intelligent education systems to enhance personalized English learning

Computer-based educational systems are becoming increasingly important in all levels and types of education. Intelligent Educational Systems (IESs) are computer-based educational systems encompassing Artificial Intelligence schemes to increase the effectiveness of the learning process. In this paper, we survey patents regarding IESs. As IESs we consider either Intelligent Tutoring Systems (ITSs) or Adaptive Educational Hypermedia Systems (AEHSs) incorporating intelligent techniques. We first present indicative functions implemented in the three basic components of an IES: the domain knowledge, the user (or student) modeling unit and the pedagogical module. Afterwards, we focus on representative patents concerning IESs and present the corresponding work. At the end, we outline current and future developments regarding IESs. Keywords: Intelligent tutoring systems, adaptive educational hypermedia systems, intelligent educational systems, artificial intelligence in education, intelligent E-learning systems, intelligent computer-based training systems, artificial intelligence in Elearning

The aim of the work was to demonstrate the possibility of using artificial intelligence technologies and instrumental psychodiagnostics tools to ensure the effective operation of cognitive learning mechanisms. The presented literary review shows the main directions of modern scientific thought in relation to the use of artificial intelligence in education. These are adaptive learning systems, whose main task is to adapt educational content within the framework of personalization of learning, and educational chatbots (virtual assistants, tutors), which assess the student’s progress, monitor what and when the student studied, collect students’ opinions about the teacher, predict educational problems and help to overcome them, predict academic results. The presented analysis of the scientific elaboration of the problem of using artificial intelligence for the tasks of ensuring the cognitive effectiveness of training showed the lack of such elaboration. The possibilities of integrating instrumental psychodiagnostics methods, such as laser Doppler vibrometry, bioradiolocation, eytracking, thermography, recognition and analysis of audio and video recordings, into intelligent educational systems have not been studied at all. The idea that the educational place of the student of the future should be provided with at least several instrumental psychodiagnostic channels has been expressed. At the same time, the artificial intelligence system must coordinate the information received for these various channels. The article presents the results of an analytical foresight study that demonstrates the possibilities of building a class of the future, intensively using the idea of cognitive-effective education, artificial intelligence technology, and modern technical means. This study analyzes the cognitive factors that negatively affect the effectiveness of learning and the possibilities of overcoming them by means of artificial intelligence and instrumental psychodiagnostics, as well as the possible impact of digitalization on cognitive learning processes. The factors are analyzed: of nonpreservation of learning knowledge in long-term memory, of non-homogenity of academic knowledge, of non-formation of the necessary information and cognitive basis of knowledge for a discipline or for further training in general, of insufficient academic attention, of lack of cognitive training, of the formation of recognition in the absence of the possibility of free actualization of knowledge, of the inefficiency of certain methods of digital education, of non-formation of knowledge explication and actualization in a dialogue, of different cognitive readiness of students in the class, of gadget-stress, of misunderstanding of reading (functional illiteracy), and others. The conclusion is made about the possibility and reality of an integrated implementation of artificial intelligence systems and of modern technical means in the educational process to reduce the negative cognitive effects of modern learning.

There has been much discussion among academics on how pupils may be taught online while yet maintaining a high degree of learning efficiency, in part because of the worldwide COVID-19 pandemic in the previous two years. Students may have trouble focusing due to a lack of teacher–student interaction, yet online learning has some advantages that are unavailable in traditional classrooms. The architecture of online courses for students is integrated into a system called the Adaptive and Intelligent Education System (AIES). In AIESs, reinforcement learning is often used in conjunction with the development of teaching strategies, and this reinforcement-learning-based system is known as RLATES. As a prerequisite to conducting research in this field, this paper undertakes the consolidation and analysis of existing research, design approaches, and model categories for adaptive and intelligent educational systems, with the hope of serving as a reference for scholars in the same field to help them gain access to the relevant information quickly and easily.

Traditional online learning environments generally use a one-size-fits-all approach, neglecting individual learner needs, goals, and cognitive abilities. Adaptive Educational Hypermedia (AEH) systems emerged to address this by personalizing learning content based on user profiles. The Adaptive Learning System is a computerized system developed to enhance digital interactive educational content teaching and learning. The system’s Adaptive algorithm automatically detects learners’ strengths and weaknesses through the adaptation process and provides personalized learning materials, feedback, and guidance during their learning. Adaptive content allows learners to learn the material at their own pace while providing additional support, evaluation, and resources tailored to each learner. In traditional educational systems, every student is given access to the same learning content regardless of their individual learning needs. Higher education institutions face various challenges when testing and implementing adaptive learning systems for their domain of knowledge. Issues related to technical challenges include processing real-time data, difficulties consolidating learning concepts and solutions into existing learning management systems, and the complexity, ease of use, and usability of adaptive learning systems. However, despite the advancement and technical sophistication in adaptive algorithms, most AEH systems still face significant usability challenges. Learners often struggle with complex interfaces, limited system responsiveness, and poor adaptation to diverse demographics and subject backgrounds. Consequently, the current adaptive systems are underutilized in higher education, with limited empirical evidence on their effectiveness in enhancing student engagement, engagement, quality of experience (QoE), and academic performance. While foundational research work (Brusilovsky, 2001; De Bra et al., 2013) and recent studies (Liu & Lim, 2020; colMOOC project) have shown the importance of adaptive educational hypermedia, yet ongoing issues persist regarding usability, learner control (Paramythis & Loidl-Reisinger, 2004), and empirical validation across varied higher education settings (Latham et al., 2004; Educause, 2018). Systematic investigations into user-controllable personalization and its effects on inclusivity and academic achievement remain limited (Xie et al., 2019; Lundqvist; Warburton, 2019). Furthermore, Li et al. (2021) highlighted that existing systems lack comprehensive cognitive modelling and fail to adequately adapt to individual learner dispositions, which restricts their intelligence and adaptability. This complexity in adaptive systems continues to pose challenges for education overall, as emphasized by Fengying Li, Yifeng He, and Qingshui Xue (2021). Addressing these issues, Dubiel et al. (2022) propose a framework for adaptive user interfaces that integrates the interaction environment to enhance system responsiveness. Therefore, there remains a significant gap in the literature regarding the lack of empirical evaluation of user-controlled personalization, particularly in real-world, multi-disciplinary higher education contexts. Existing research has focused mainly on adaptive algorithmic and technical development, but has not given sufficient attention to practical usability (the lack of effective, usable AEH systems, learner diversity, and actual learner-centered outcomes). Moreover, there is a lack of comprehensive frameworks for evaluating the usability of adaptive educational hypermedia (AEH), and there is limited research on how these adaptive systems can foster inclusivity and fairness among diverse learner populations. Therefore, there is an urgent need to address the usability and complexity problems in AEH systems and to suggest the creation of usable, effective, personalised, learner-centric, and user-controllable AEH systems for higher education environments. This research study addresses these gaps by introducing a personalized, user-controllable Adaptive Educational Hypermedia (AEH) framework specially designed for a Higher Education setting. It evaluates multiple versions of adaptive learning systems across multiple disciplines (English, Malay, Computer Science) revealing significant enhancements in both student achievement and satisfaction. The study not only evaluates multiple versions of adaptive systems but also offers practical recommendations for software developers and educators seeking to implement effective, more intuitive, and learner-centric adaptive systems within real higher educational environments. By exploring the relationships between learner demographics, usability, and academic outcomes, the study promotes inclusivity and equity in digital education. More specifically, it introduces a novel user-controllable personalization feature that empowers learners to customize the learning content to improve their adaptive learning experience, addressing a major limitation found in existing systems. The findings demonstrate significant improvements in student performance and satisfaction with the system’s interface, thereby enhancing subject retention, confirming that adaptive learning systems can effectively improve the academic experience in higher education environments.

Adaptation and personalization have been extensively studied in CSCL research community aiming to design intelligent systems that adaptively support eLearning processes and collaboration. Yet, with the fast development in Internet technologies, especially with the emergence of new data technologies and the mobile technologies, new opportunities and perspectives are opened for advanced adaptive and personalized systems. Adaptation and personalization are posing new research and development challenges to nowadays CSCL systems. In particular, adaptation should be focused in a multi-dimensional way (cognitive, technological, context-aware and personal). Moreover, it should address the particularities of both individual learners and group collaboration. As a consequence, the aim of this book is twofold. On the one hand, it discusses the latest advances and findings in the area of intelligent adaptive and personalized learning systems. On the other hand it analyzes the new implementation perspectives for intelligent adaptive learning and collaborative systems that are brought by the advances in scripting languages, IMS LD, educational modeling languages and learning activity management systems. Given the variety of learning needs as well as the existence of different technological solutions, the book exemplifies the methodologies and best practices through several case studies and adaptive real-world collaborative learning scenarios, which show the advancement in the field of analysis, design and implementation of intelligent adaptive and personalized systems.

Intelligent Educational Systems (IESs) need large amounts of educational content that is typically not provided by the creators of these systems. In this paper we discuss a new approach for authoring practical IESs where core authoring is done by professional design teams, while the educational content is mainly developed by teachers who use the system in their classes. The major bottleneck of this approach is the lack of intelligent authoring support tools that allow regular teachers to author intelligent content that an IES needs in order to perform its functions. As a contribution to solving this problem, we present our recent work on authoring support for an adaptive vocabulary acquisition system, ELDIT. The paper describes the ELDIT system, the needs and challenges of language content authoring by teachers, and the two authoring support components that we have developed for two essential kinds of language learning content: illustrative examples and educational texts.

Towards a Design of an Intelligent Educational System

With the popularization of computers and various mobile intelligent terminals, intelligent teaching systems based on learners are becoming more and more popular among learners. The above phenomenon has greatly affected and changed the current teaching quality diagnosis methods and models. However, the author found through investigation that the current intelligent teaching quality diagnosis still has different degrees of deficiencies in the design and implementation. In response to the above problems, this paper proposes a teaching quality intelligent diagnosis model based on the combination of wireless sensor networks and fuzzy comprehensive evaluation algorithms. First of all, this article is based on the wireless sensor network to link various levels of intelligent teaching systems, and constructs the information transmission structure of the teaching intelligent diagnosis system. Secondly, this article uses fuzzy comprehensive evaluation and convolutional neural network algorithms to evaluate and excavate intelligent teaching information. Finally, the model successfully passed the simulation test and simulation application, which can provide intelligent diagnosis of teaching quality for modern intelligent teaching system.

The ongoing COVID-19 pandemic has accelerated the adoption of e-learning, remote learning, and hybrid models in education. These models have become essential in meeting the demands of smart cities and addressing the limitations of traditional distance learning. However, to truly achieve academic success, education must be adaptive and tailored to the individual needs of students. This study presents a novel concept for intelligent educational systems that integrate Explainable Artificial Intelligence (XAI) and Internet of Behavior (IoB) technologies. The integration of these technologies aims to revolutionize intelligent educational systems by providing a more personalized and effective learning experience. By collecting and analyzing student behavior data, the system can provide real-time feedback and adjust to meet the needs of each individual student. The results of this study demonstrate the significant impact of IoB technology on student performance. The integration of IoB led to a substantial increase in student response from 40% to 79%. These findings highlight the potential for IoB to enhance learner assistance and improve system modifications to better meet the expectations of students for increased performance. The proposed concept of integrating XAI and IoB technologies in intelligent educational systems can pave the way for a more personalized and effective learning experience in the future.

The objective was to study changes in EEG time-domain Kolmogorov complexity under different mental fatigue state and to evaluate mental fatigue using Lempel-Ziv complexity analysis of spontaneous EEG in healthy human subjects. EEG data for healthy subjects were acquired using a net of 2 electrodes (Fp1 and Fp2) at PM 4:00, AM 12:00 and AM 3:00 in the 24 hours sleep-deprived mental fatigue experiments. It was presented that initial results for eight subjects examined in three different mental fa-tigue state with 2-channel EEG time-domain Lempel-Ziv complexity computations. It was found that the value of mean Lempel-Ziv com-plexity corresponding to a special mental state fluctuates within the special range and the value of C(n) increases with mental fatigue increasing for the total frequency spectrum. The result in-dicates that the value of C(n) is strongly cor-relative with the mental fatigue state. These re-sults suggest that it may be possible to nonin-vasively differentiate different mental fatigue level according to the value of C(n) for particular mental state from scalp spontaneous EEG data. This method may be useful in further research and efforts to evaluate mental fatigue level ob-jectively. It may also provide a basis for the study of effects of mental fatigue on central neural system.

A modern Intelligent Tutoring System (ITS) should be sentient of learner's cognitive and affective states, as learner's performance could be affected by motivational and emotional factors. It is important to design a low-cost and unobtrusive computational method for ITS to determine learner's states automatically. The automated learner's data sensing is useful in the development of personalized e-learning that can adapt learning content according to individual's emotion. Although many past findings relate stressor such as task demand to learner's emotion, however there is a lack of existing studies that examine the impact of stressor on learners' cognitive states and their mouse and keystroke dynamics. This exploratory research, conducted on 160 undergraduate students, found that the impacts of direct learning instruction and external stress stimuli, such as timer display, on learner's cognitive and affective states are significant. The correlations between direct instruction, external stimuli, learners' cognitive and affective states, as well as their mouse and keystroke behaviours are also significant.

In instructional design, decisions about which instructional methods to use are made well in advance of their implementation. While expert teachers select methods while instruction is underway and are well placed to adapt instruction to changing conditions, designers must try to predict ahead of time how instructional conditions will change. This is difficult to do because design procedures are non-deterministic and instructional theory is incomplete. In intelligent instructional systems, it is possible to shift certain aspects of decision-making back to the implementation phase of instruction. This enables systems to adapt to changing conditions and to present information in a variety of formats. The designer becomes less concerned with selecting methods and puts more effort into writing rules that allow systems to select methods once instruction has begun.

In this paper two observations are the starting points for a proposal as to how learning, rather than computing considerations, can be made the main focus in the development of Intelligent Educational Systems (IESs). These observations are: a) the predominant use of computers in education is for the running of general tool software; and b) the symbolic approach to Artificial Intelligence is being questioned from a situated cognition perspective.Learning interactions are seen as consisting of a Task Level, and a higher‐level Discussion Level comprising planning and evaluation of, and reflection about, Task Level activity. It is proposed that IESs should be developed by adding a module to support Discussion Level interaction with the learner. The Mayday project is described, in which expert human teachers are being studied as they support Discussion Level with a learner working at computer‐based lexical activities. It is argued that such studies of human learning is a necessary step towards the development of the proposed IESs.

Since its debut in the field of education nearly three decades ago, Artificial Intelligence (AI) has been considered as a powerful tool to facilitate new paradigms for instructional design and innovative educational practice in the form of intelligent tutoring systems, chatbots, teaching robots and adaptive learning systems among others. Recent technological advances in the adjacent areas of natural language processing, machine learning and computer graphics focusing primarily on design features that can improve their human-like qualities of naturalness and believability as human interlocutors have also amplified new application opportunities for Intelligent Virtual Agents (IVAs) or Animated Pedagogical Agents (APAs) within the area of Intelligent Computer-Assisted Language Learning (ICALL). Although AI-powered IVAs hold the potential to enhance the learning process in nearly any knowledge domain and personalize automation in teaching by embodying different roles in the learning environment, strikingly few studies have empirically attempted to assess IVAs impact on L2 learners’ academic achievement when learning English as a Foreign (EFL) so far. This study addresses this issue via a systematic review of relevant interventionist IVA studies that were conducted in EFL settings and published within the 2015-2020 timeframe examining IVAs key affordances, major barriers in their adoption for language learning purposes, and the CALL research trends currently prevalent on the topic. Pedagogical implications for the effective implementation of IVA technology in L2 contexts are discussed and future research avenues in the area are highlighted.

This study investigates the long-term impact of adaptive digital learning ecosystems on students' self-regulated learning (SRL) behaviors and academic persistence. Adaptive learning systems personalize the learning experience by adjusting content and feedback to meet individual students' needs, preferences, and performance. These systems enhance engagement, motivation, and learning outcomes through real-time adjustments and continuous feedback. The research aims to explore how adaptive learning systems influence SRL and academic persistence in university courses over time. Using a longitudinal quantitative design, the study tracks SRL behaviors and academic persistence at multiple points during the semester. Results show significant improvements in SRL behaviors such as goal setting, planning, self-monitoring, and reflection among students engaged with adaptive learning environments. These students exhibited greater autonomy, improved metacognitive awareness, and higher motivation. Additionally, students in adaptive systems demonstrated greater academic persistence, as indicated by more time spent on tasks, higher assignment completion rates, and sustained engagement. The findings suggest that adaptive learning platforms promote SRL and academic persistence by offering personalized, responsive learning experiences. Unlike static, non-adaptive environments, adaptive systems provide dynamic support, enhancing students' ability to regulate their learning and remain engaged despite challenges. The study concludes that adaptive learning systems are vital for long-term academic success, though further research is needed to assess the sustainability of these effects in various educational settings and among diverse student populations.