An artificial intelligence agent technology based web distance education system

Digital learning has become an everyday experience for a significant part of the population during a pandemic, regardless of their technical and psychological readiness. Both the more and less technologically advanced countries have faced the inevitable need for large-scale deployment of digital learning. This paper presents an assessment of the development of digital learning in Lithuania and the EU countries in 2019, i.e., before the pandemic began. The evaluation is carried out using the Index of Readiness for Digital Lifelong Learning, developed by the Center for European Policy Studies (CEPS) using official indicators and expert evaluation.Despite the growing number of studies aimed at assessing the digital divide in modern society and in education in particular, there is still a lack of empirical material to shed light on the link between the extent of digitalization, its determinants (such as national governance in promoting digitalization), and changes in learning outcomes caused by digitalization. The findings from the CEPS study presented in the paper are the first attempt to move beyond the assessment of the prevalence of learning digitalization in different European countries, taking a holistic view of digitalisation-induced changes in learning outcomes and participation with a special focus on digital learning policy as an important component of digitalisation development.The aim of this article is to assess the digital learning situation in Lithuania using the Index of Readiness for Digital Lifelong Learning, which was developed before the pandemic in 2019. The progress of European Union countries in developing digital learning is reviewed in the paper, based on the results of CEPS (2019) research. The Index of Readiness for Digital Lifelong Learning and the results of Lithuania’s assessment using the methodology developed by CEPS are presented here. The description of the situation in Lithuania is based on the second component of the Index titled “Institutions and policies for digital learning”.In order to qualitatively assess the country’s strategic provisions for digital learning, the method of analysis of the country’s strategic documents was applied. Public expert opinions were included into the analysis of the situation in Lithuania. An interpretation of the comparative analysis of the obtained index values is presented.Standard indicators from the Eurostat, Eurobarometer, OECD, Bertelsmann Stiftung, World Bank, and expert surveys were used to create the combined Index of Readiness for Digital Lifelong Learning. The index is constructed as a weighted average of indicators divided into three categories: learning participation and outcomes, institutions and policies for digital learning, and availability of digital learning. The assessment of the situation in the EU countries, carried out according to the developed methodology, allowed to calculate the value of the Index for each country. Lithuania ranks 11th in the overall EU-27 ranking with an Index value of 0.623.A more detailed analysis allowed us to see that the countries’ ratings can differ significantly according to the different categories of the Index. The Scandinavian countries lead in terms of learning participation and outcomes and, together with the Netherlands and Austria, in terms of availability of digital learning. Southern European countries received relatively high ratings in the Index category “institutions and policies for digital learning”, which reflects their determination to strengthen their position in the digital world. Estonia and the Netherlands also found themselves among the leaders in this category. Lithuania’s relatively high position in the list is also based on positive evaluations of the indicators of the second component of the Index (i.e., policies and institutions), while participation and learning outcomes were assessed modestly. Using the example of Lithuania, the article provides arguments in favor of why the second component of the Index should not be given.

In recent years much research and development effort has been directed towards the broad field of ambient intelligence (AmI), and this trend is set to continue for the foreseeable future. AmI aims at seamlessly integrating services within smart infrastructures to be used at home, at work, in the car, on the move, and generally in most environments inhabited by people. It is a relatively new paradigm rooted in ubiquitous computing, which calls for the integration and convergence of multiple disciplines, such as sensor networks, portable devices, intelligent systems, human-computer and social interactions, as well as many techniques within artificial intelligence, such as planning, contextual reasoning, speech recognition, language translation, learning, adaptability, and temporal and hypothetical reasoning. The term AmI was coined by the European Commission, when in 2001 one of its Programme Advisory Groups launched the AmI challenge (Ducatel et al., 2001), later updated in 2003 (Ducatel et al., 2003). But although the term AmI originated from Europe, the goals of the work have been adopted worldwide, see for example (The Aware Home, 2007), (The Oxygen Project, 2007), and (The Sony Interaction Lab, 2007). The foundations of AmI infrastructures are based on the impressive progress we are witnessing in wireless technologies, sensor networks, display capabilities, processing speeds and mobile services. These developments help provide much useful (row) information for AmI applications. Further progress is needed in taking full advantage of such information in order to provide the degree of intelligence, flexibility and naturalness envisaged. This is where artificial intelligence and multi-agent techniques have important roles to play. In this paper we will review the progress that has been made in intelligent systems, discuss the role of artificial intelligence and agent technologies and focus on the application of AmI for independent living.

Machine ethics has received increasing attention over the past few years because of the need to ensure safe and reliable artificial intelligence (AI). The two dominantly used theories in machine ethics are deontological and utilitarian ethics. Virtue ethics, on the other hand, has often been mentioned as an alternative ethical theory. While this interesting approach has certain advantages over popular ethical theories, little effort has been put into engineering artificial virtuous agents due to challenges in their formalization, codifiability, and the resolution of ethical dilemmas to train virtuous agents. We propose to bridge this gap by using role-playing games riddled with moral dilemmas. There are several such games in existence, such as Papers, Please and Life is Strange, where the main character encounters situations where they must choose the right course of action by giving up something else dear to them. We draw inspiration from such games to show how a systemic role-playing game can be designed to develop virtues within an artificial agent. Using modern day AI techniques, such as affinity-based reinforcement learning and explainable AI, we motivate the implementation of virtuous agents that play such role-playing games, and the examination of their decisions through a virtue ethical lens. The development of such agents and environments is a first step towards practically formalizing and demonstrating the value of virtue ethics in the development of ethical agents.

Abstract: Research links a progressive decrement in episodic memory to deficits in judgment and decision making in aging. In social decision making, decrements in episodic memory contribute to suboptimal decisions among older adults. As artificial agents – such as humanoid robots, artificial intelligence (AI)-powered deepfakes, and chatbots – become increasingly present in human society, they can both benefit (e.g., combat loneliness) and harm (e.g., defraud) older adults. Understanding how older adults make social judgments and decisions involving artificial agents is crucial for research and policy making. Nevertheless, it remains poorly understood how episodic memory deficits influence social judgments and decisions involving artificial agents, and how this relation changes with age. This review will bridge this gap by applying a developmental model to explain how memory influences social judgments and decisions involving artificial agents in late adulthood. Evidence suggests that older adults increasingly rely on gist-based processing, which may explain their greater preference for humanoid robots compared with younger adults. We will also discuss the distinct challenges deepfakes pose for older adults’ trust-related decision making. In closing, we will discuss the implications of age-related memory changes for social judgments and decisions involving disembodied artificial agents, such as chatbots.

AI Applications in Health Sector: Use of Artificial Intelligence in Covid-19 Crisis and Impacts of Medical Robots on Global Economy

In the modern education system, using artificial intelligence and smart learning techniques has become vital for students' academic success. This research examines the direct impact of smart learning, artificial intelligence, and beliefs about the future on academic performance. It further investigates whether the desire for knowledge mediates the relationships between these variables. A structural questionnaire was designed using the existing literature, and data was collected through face-to-face distribution. The respondents have diversified demographic dimensions for which a sample of 317 was empirically tested with the help of MS-Excel and Smart PLS version 4. The results signify the following output: (1) artificial intelligence, desire for knowledge, and smart learning promote the academic performance of the study. (2) Desire for knowledge fully mediates the relationship between smart learning and academic performance and between beliefs about the future and academic performance, respectively. A comprehensive list of policy recommendations is also provided

With the development of science and technology, computer technology is more and more widely used in people's life. As a branch of computer technology, artificial intelligence technology is also gradually developing and expanding, affecting people's life. Nowadays, the processing ability shown by simple computer technology has gradually lagged behind the times. If you want more convenient data processing, artificial intelligence is an excellent solution. Agent technology in artificial intelligence makes artificial intelligence have stronger data processing ability and learning ability than computer technology, and can make decisions quickly according to data information. Artificial intelligence technology can promote automation and intelligence in various industries and improve the production efficiency of enterprises. Starting from the elaboration of artificial intelligence, this paper deeply explores the relationship between artificial intelligence and computer, analyzes the development status of artificial intelligence and computer, and puts forward the technical problems between artificial intelligence and computer, so as to provide reference for the further development of artificial intelligence.

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.

With the rapid development of artificial intelligence (AI) technology, database courses, as an important foundational course for computer majors, are facing unprecedented challenges and opportunities. Especially in the context of the digital and intelligent transformation of higher education, database course teaching not only needs to update the content system, but also needs to achieve reconstruction in teaching objectives, teaching methods, and teaching processes. This article takes Guangzhou College of Commerce as an example to analyze the integration relationship between AI and database courses, explore the empowering role of AI in optimizing teaching content, innovating teaching methods, and intelligent management of teaching processes. It proposes to build a database course teaching reform path driven by AI, including "teaching goal design centered on ability cultivation", "teaching content update supported by intelligent technology", "teaching method innovation based on generative tools", and "full process integration mechanism characterized by intelligent monitoring of teaching processes", providing theoretical support and practical reference for the reform of database courses in computer majors in universities.

The rapid advancement of digital technology requires students to develop Digital Literacy and Learning Autonomy to adapt to modern learning dynamics and enhance their Critical Thinking Ability. This study aims to examine the impact of Digital Literacy and Learning Autonomy on students' Critical Thinking Ability, with Artificial Intelligence (AI) as a moderating variable. The research employed a quantitative approach using the Structural Equation Modeling-Partial Least Square (SEM-PLS) method, analyzed through SmartPLS 4.1 software. Data were collected through online questionnaires from 202 respondents selected via simple random sampling from the 2022 class of Economics Education students at the Faculty of Economics and Business, Universitas Negeri Semarang (FEB UNNES). The results indicate that both Digital Literacy and Learning Autonomy have a positive and significant effect on students’ Critical Thinking Ability. Furthermore, AI significantly and strongly moderates the relationship between Digital Literacy and Critical Thinking Ability (path coefficient β = 0.203; p = 0.010), but does not significantly moderate the relationship between Learning Autonomy and Critical Thinking Ability (path coefficient β = 0.051; p = 0.520). These findings suggest that students with higher levels of Digital Literacy are able to leverage AI tools more effectively to enhance their cognitive processes and analytical reasoning. Therefore, Digital Literacy and Learning Autonomy are essential in shaping students' Critical Thinking Ability, while the use of AI serves as a cognitive accelerator, particularly for those already possessing strong digital competence. This study contributes to the development of higher education by emphasizing the importance of integrating Digital Literacy and responsible AI usage in learning environments. Promoting these elements can foster independent learning and strengthen students' Critical Thinking Ability, preparing them to excel academically and professionally in the digital eraThe rapid advancement of digital technology requires students to develop Digital Literacy and Learning Autonomy to adapt to modern learning dynamics and enhance their Critical Thinking Ability. This study aims to examine the impact of Digital Literacy and Learning Autonomy on students' Critical Thinking Ability, with Artificial Intelligence (AI) as a moderating variable. The research employed a quantitative approach using the Structural Equation Modeling-Partial Least Square (SEM-PLS) method, analyzed through SmartPLS 4.1 software. Data were collected through online questionnaires from 202 respondents selected via simple random sampling from the 2022 class of Economics Education students at the Faculty of Economics and Business, Universitas Negeri Semarang (FEB UNNES). The results indicate that both Digital Literacy and Learning Autonomy have a positive and significant effect on students’ Critical Thinking Ability. Furthermore, AI significantly and strongly moderates the relationship between Digital Literacy and Critical Thinking Ability (path coefficient β = 0.203; p = 0.010), but does not significantly moderate the relationship between Learning Autonomy and Critical Thinking Ability (path coefficient β = 0.051; p = 0.520). These findings suggest that students with higher levels of Digital Literacy are able to leverage AI tools more effectively to enhance their cognitive processes and analytical reasoning. Therefore, Digital Literacy and Learning Autonomy are essential in shaping students' Critical Thinking Ability, while the use of AI serves as a cognitive accelerator, particularly for those already possessing strong digital competence. This study contributes to the development of higher education by emphasizing the importance of integrating Digital Literacy and responsible AI usage in learning environments. Promoting these elements can foster independent learning and strengthen students' Critical Thinking Ability, preparing them to excel academically and professionally in the digital era.

Artificial Intelligence (AI) is a system that is developed and continues to innovate deeply fields of study that are created either on machines or computers that have the same intelligence or even more like humans. Artificial Intelligence (AI) technology is often used, not only one or two Artificial Intelligence (AI) platforms have been created by humans for makes human work easier. However, Artificial Intelligence (AI) has various types where each platform has different uses and functions, the author this time wants describes the platforms that students often use for survival universities according to a survey that the author has carried out. Research methods used to find out this phenomenon is a qualitative descriptive research method with techniques collect questionnaire data by asking questions and can be answered directly by respondents according to their respective answers. Artificial Intelligence (AI) has potential play an important role in supporting students with learning disabilities and helping them reach their maximum potential. For the emotional plane students, the use of Artificial Intelligence (AI) technology can help build feelings confident in learning outcomes, because human artificial intelligence technology can help learning in a pleasant and comfortable environment, thereby increasing the sense of self-confidence and reduce anxiety in learning. UINSA student use this intelligence tool to make work easier and save money their time. This human artificial intelligence technology also does not completely have it benefits for students, if we as students are aware of technological advances with the suitability of his actions regarding Max Weber's rational actions, for achieveing the actor’s dreams. AbstrakArtificial Intelligence (AI) merupakan sistem yang dikembangkan dan terus berinovasi dalam bidang studi yang yang dibuat baik pada mesin ataupun komputer yang memiliki kecerdasan sama atau bahkan lebih seperti manusia. Teknologi Artificial Intelligence (AI) sering digunakan, tidak hanya satu atau dua saja platform Artificial Intelligence (AI) yang diciptakan manusia untuk memudahkan pekerjaan manusia. Namun, Artificial Intelligence (AI) memiliki berbagai jenis yang setiap platform memiliki tata guna dan fungsi yang berbeda, penulis kali ini ingin menjabarkan platform-platform yang sering digunakan mahasiswa dalam keberlangsungan hidup universitas menurut survey yang sudah penulis laksanakan. Metode penelitian yang digunakan untuk mengetahui fenomena ini adalah metode penelitian deskriptif kualitatif dengan teknik pengumpulan data kuesioner dengan memberikan pertanyaan dan dapat dijawab langsung oleh responden sesuai dengan jawaban mereka masing-masing. Artificial Intelligence (AI) berpotensi memainkan peran penting dalam mendukung mahasiswa dengan ketidakmampuan belajar dan membantu mereka mencapai potensi maksimal mereka. Untuk bidang emosional mahasiswa, penggunaan teknologi Artificial Intelligence (AI) dapat membantu membangun rasa percaya diri dalam hasil belajar, karena teknologi kecerdasan buatan manusia dapat membantu pembelajaran dalam lingkungan yang menyenangkan dan nyaman, sehingga meningkatkan rasa percaya diri dan mengurangi kecemasan dalam belajar. Mahasiswa UINSA mempergunakan alat bantu kecerdasan ini untuk mempermudah pengerjaan serta menghemat waktu mereka.Upaya yang mereka lakukan telah dijelaskan oleh Max Weber sebagai bentuk dari tindakan rasional yang berorientasi, kepada cita-cita para pelaku. Kata kunci: Artificial Intellegence, Pembelajaran, tindakan rasional

Artificial intelligence usage in the communication field is of strategic importance. However, most communication practitioners and academicians have not noticed the gains in momentum substantially artificial intelligence. Through communication journals, this study aims to examine how artificial intelligence is used, its effects, and how important it is in communication. This study was designed as a content analysis and bibliometric analysis study. The sample of this study is AI-based articles in Web of Science Q1 indices communication journals. It selected and examined the first sixteen journals in communication keyword search based on SCImago Journal & Country Rank. This study revealed artificial intelligence usage in the communication industry and field, in addition to AI's impact and significance and AI-based technologies in communication. It was found that the most used method for artificial intelligence-based articles in communication journals was the semi-structured interview method. It is seen that common keywords in AI-based articles and communication journals are announced as human-machine communication, machine learning, artificial agent, artificial intelligence, bias, and social media concepts. In this study, a positive correlation between communication science and artificial intelligence technologies was observed. This study contributes to new studies on artificial intelligence, communication, the impacts of artificial intelligence on communication, new communication technologies, generative AI, human-machine communication, and other related areas.

Introduction: the article deals with the issues concerning the protection of the rights to digital content created with the use of artificial intelligence technology and neural networks. This topic is becoming increasingly important with the development of the technologies and the expansion of their application in various areas of life. The problems of protecting the rights and legitimate interests of developers have come to the fore in intellectual property law. With the help of intelligent systems, there can be created not only legally protectable content but also other data, relations about which are also subject to protection. In this regard, of particular importance are the issues concerning the standardization of requirements for procedures and means of storing big data used in the development, testing and operation of artificial intelligence systems, as well as the use of blockchain technology. Purpose: based on an analysis of Russian and foreign scientific sources, to form an idea of the areas of legal regulation and the prospects for the application of artificial intelligence technology from a legal perspective. Methods: empirical methods of comparison, description, interpretation; theoretical methods of formal and dialectical logic; special scientific methods (legal-dogmatic and the method of interpretation of legal norms). Results: analysis of the practice of using artificial intelligence systems has shown that today intelligent algorithms include a variety of technologies that are based on or related to intelligent systems, but not always fall under the concept of classical artificial intelligence. Strictly speaking, classic artificial intelligence is only one of the intelligent system technologies. The results created by autonomous artificial intelligence have features of works. At the same time, there are some issues of a public law nature that require resolution: obtaining consent to data processing from the subjects of this data, determining the legal personality of these persons, establishing legal liability in connection with the unfair use of data obtained for decision-making. Standardization in the sphere and application of blockchain technology could help in resolving these issues. Conclusions: in connection with the identified and constantly changing composition of high technologies that fall under the definition of artificial intelligence, there arise various issues, which can be divided into some groups. A number of issues of legal regulation in this area have already been resolved and are no longer of relevance for advanced legal science (legal personality of artificial intelligence technology); some issues can be resolved using existing legal mechanisms (analysis of personal data and other information in course of applying computational intelligence technology for decision-making); some other issues require new approaches from legal science (development of a sui generis legal regime for the results of artificial intelligence technology, provided that the original result is obtained).

The objective of this paper is to study the key technology of the mobile communication network optimization system based on artificial intelligence technology. Specific Content. This paper designs the artificial intelligence agent‐ (IA‐) type mobile communication network optimization tool iOS2CMCN, analyzes the relevant intelligent technology introduced into the system, and analyzes the feasibility and practicability of iOS2CMCN through application examples. The results show that the optimization of mobile communication networks is one of the important links in the construction of communication networks and ensuring the quality of network service. In the form of an artificial intelligence agent, iOS2CMCN absorbs the experience and knowledge of a large number of network optimization engineers and experts and realizes the intelligence and automation of mobile communication network optimization. It is proven that the fuzzy technology is introduced in theory, the practical problem is reasonably modeled, and the rule and case reasoning are used to simulate the thinking mode of a human being when solving the problem. It reduces the dependence of network optimization on humans, improves the efficiency of network optimization, and provides a new idea for practical mobile communication network optimization.

The rapid development of artificial intelligence (AI) technologies has demonstrated their affordances and limitations in revolutionizing pedagogical strategies in higher education. Given the lack of guidelines, policies, and resources to assist instructors in efficiently and ethically integrating AI into teaching and learning practices, this systematic review aimed to investigate AI integration competencies and challenges in higher education from the intelligent Technological Pedagogical Content Knowledge (TPACK) perspective. We first applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to identify 23 studies published between 2019 and 2023 that met the inclusion and exclusion criteria. After conducting open coding and thematic analysis, the research findings showed four AI integration competencies and strategies, including 1) AI literacy and readiness as intelligent technological knowledge, 2) AI-supported innovative pedagogy to supplement instructor social presence and transform the instructor-student relationship, 3) AI as intelligent learning partners to increase student engagement in self-regulated learning and higher-order thinking skills, and 4) AI-driven learning experience design and delivery. AI integration limitations and challenges contained: 1) the short-term novelty effect, 2) digital divides, 3) technical deficiencies, and 4) ethical concerns. Based on the implications for future practices, a diagram was developed to illustrate the systematic considerations to support the sustainability of AI-assisted teaching and learning in higher education.