Towards more intelligent teaching systems

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

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.

The integration of biosensor technologies, like electroencephalography (EEG), has extended the limits of adaptive, intelligent education systems, offering real-time, personalized learning knowledge. This study explores the use of EEG to track and assess students’ cognitive states, allowing for the improvement of an active, adaptive English learning system that tailors content according to every student’s participation and improvement. Students’ cognitive states serve as the foundation for personalized education responses that motivate and enhance their participation. EEG data are gathered during English language testing to assess the correlation between learners’ cognitive states and their performance. Noise reduction is one of the preprocessing stages that ensures clear and pertinent data for analysis. Power spectral density (PSD) for feature extraction approaches is used to identify key cognitive patterns. Based on real-time EEG data, the personalized education feedback system constantly modified the course material, enhancing motivation and learning results. This research proposed a novel Dynamic Osprey Optimized Intelligent Gradient Boosting Machines (DOO-IGBM) to assess and improve the efficiency of an adaptive intelligent education system. The findings suggest that EEG-based adaptive systems make it possible to significantly progress English learning by offering personalized education paths based on brain activity to other conventional algorithms with 98.5% accuracy, 97.7% precision, 98% recall, and 98.6% F1-score. These outcomes provide precious insights and data to support the future development of adaptive, intelligent education systems for language learning.

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.

In existing network teaching systems, deficiencies of the resource sharing and management are analyzed. An intelligent network teaching system based on knowledge grid is constructed to provide a uniform platform of intelligent information for teaching. The global sharing of knowledge resource will be realized by this platform. A course domain knowledge representation method based on the topic map in grid environment was put forward. Meanwhile, the domain knowledge model was built. Aiming at the problem about the knowledge resource deficiency in the present answering systems, an intelligent answering subsystem based on knowledge grid was studied and designed. By expanding the answering resource base to the Internet, this subsystem will fully mine the Internet knowledge resources by use of knowledge mining technology to complete the answering task. Details are provided about the architecture's design and main techniques of the system in this paper, including the constructing of domain knowledge structure model, intelligent answering, personalized knowledge service and so on.

In recent years, information technology marked by computer vision communication, blended learning, intelligent teaching systems (ITS), and virtual reality has gradually penetrated into the education field, and collaborative innovation has realized the gradual transformation of teaching to intelligence. The purpose of this article is to study the teaching system exploration and algorithm design of “virtual reality (VR) + visual communication design (VCD)” collaborative innovation. This article analyzes the application value of “VR + VCD” collaborative innovation in teaching. By comparing it with traditional teaching, it summarizes the advantages of “VR + VCD” technology application teaching, and proposes a VR-based Co-evolutionary teaching and learning optimization algorithm. The survey data shows that 21% of the students in the experimental class will be very active in learning after class, 60% of the students will be willing to spend time studying after class, 20% of the students in the control class will be very active in learning after class, and 54% of the students will be willing to study after class. This shows that the application of “VR + VCD” technology in the classroom can effectively increase students’ interest in learning and increase their confidence in learning.KeywordsVirtual realityVisual communication designTeaching systemCollaborative innovation

Towards a Design of an Intelligent Educational System

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.

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.

Paper mainly introduces a kind of design method of building an on-line intelligent assistant teaching system based on dynamic web language program tool. This system uses ASP language as developing-designing tools, and adopts software of Dreamwaver MX, etc in B/S structure, which completing web platform system building. In this paper, we introduce design method and realization process of system parts of sub-system function module design in course <;VB Program Design>, as well as provide some sub-system web pages and corresponding source file in detail. System design study, communication and on line test modules, as well as design homework uploading sub-system, etc, which can be replant into other course and can improve students' study interest and study initiative. In addition, system has background management module by technologies of database SQL and ADO, etc, which can realize on-line manage whole system materials easily.

Internet plus education has hitherto unknown the opportunities and challenges of the development of modern occupation education. Based on the super star "one level and three ends" intelligent teaching system, modern information technology is deeply integrated into the reform and practice of mixed teaching mode of computer specialty in higher vocational colleges. Taking the teaching of C language programming as an example, reconstruct multiple curriculum objectives, reform the curriculum teaching content system, create MOOC courses with higher vocational characteristics, build a "MOOC + SPOC + flipped classroom" teaching mode, carry out "3 + 2" mixed teaching, and build a three-dimensional curriculum ideological and political system of "country, society and individual", Create a diversified assessment method combining process and summative assessment, organically integrating online and offline classes and three teaching stages before, during and after class. Teaching practice shows that this model is conducive to stimulate higher vocational students’ learning interest, cultivate higher vocational students’ autonomous learning ability, promote higher vocational students’ in-depth learning, improve higher vocational students’ learning ability, and achieve good teaching results.

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.

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.

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.

Design of intelligent English translation teaching system combined with bidirectional encoder representation