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Education is increasingly using Intelligent Tutoring Systems (ITS), both for modelling instructional and teaching strategies and for enhancing educational programs. The first part of the paper introduces the basic structure of an ITS as well as common problems being experienced within the ITS community. The second part describes WITNeSS ‐ an original hybrid intelligent system using Fuzzy‐Neural‐GA techniques for optimising the presentation of learning material to a student. The original work in this paper is related to the concept of a “virtual student”. This student model, modelled using fuzzy technologies, will be useful for any ITS, providing it with an optimal learning strategy for fitting the ITS itself to the unique needs of each individual student. In the third part, experiments focus on problems developing a “virtual student” model, which simulates, in a rudimentary way, human learning behaviour. Part four finishes with concluding remarks.

This article proposes an architecture of an intelligent and autonomous recommendation system to be applied to any virtual learning environment, with the objective of efficiently recommending digital resources. The paper presents the architectural details of the intelligent and autonomous dimensions of the recommendation system. The paper describes a hybrid recommendation model that orchestrates and manages the available information and the specific recommendation needs, in order to determine the recommendation algorithms to be used. The hybrid model allows the integration of the approaches based on collaborative filter, content or knowledge. In the architecture, information is extracted from four sources: the context, the students, the course and the digital resources, identifying variables, such as individual learning styles, socioeconomic information, connection characteristics, location, etc. Tests were carried out for the creation of an academic course, in order to analyse the intelligent and autonomous capabilities of the architecture.

In intelligent tutoring systems (ITS), learners were often granted limited authority and are forced to obey the decision of the system which might not satisfy their needs. Failure to grant learners sufficient autonomy could yield unexpected effects that hinder learning, including undermining learners’ motivation, priming learners’ aversion to the algorithm. On the contrary, granting learners overwhelming autonomy could also be harmful as the absence of learning support would also have a negative impact on learning. As such, this study aims to design and implement an intelligent tutoring system that offers learners proper autonomy.

The main learning activity in the system is doing exercises, and by finishing exercises learners could earn virtual coins. Based on item response theory, exercises are administered to learners with proper difficulty. Based on a recommended difficulty parameter predicted by the system, learners could manually modify the difficulty of the exercises, they could earn more credits by finishing more challenging exercises. Meanwhile, a pedagogical agent is embedded. Learners could customize the agent’s personality jointly with the system to create the learning context they prefer.

A intelligent tutoring system with proper learner autonomy (LA) is designed and implemented.

Few previous researches have noticed the potentially important role that LA plays in ITS. Learning might be facilitated using such a design.

In the current global competitive environment, if an organization is to be successful, it has to be a learning organization. Organizations learn from their assertive and adaptive interaction with the environment and from their internal dynamics. Organizational learning needs to be supported as external environments and internal dynamics of organizations become more complex. Discusses different learning processes and the different intelligent systems which can support and enhance organizational learning. Finally, presents a conceptual framework of an integrated intelligent system for supporting organizational learning. Intends to be useful to both organizational theorists/ practitioners and IT managers, who are involved in the development and implementation of learning organizations.

This research aimed to examine the current status of artificial intelligence's (AI's) integration into Chinese adult education, by analyzing the influences that AI has had on current adult education practices in China and by discussing the opportunities and challenges that adult education in China is faced with under the rapid AI development in the past 12 years.

This research employed systematic literature analysis. CNKI (China National Knowledge Infrastructure) Chinese Journals Full-text Database was used to collect scholarly publications on the use of AI in adult education in China that was published in the past decade. Data analysis included the following steps: identifying key words and phrases, detecting underlying meanings, searching for logical connections and relationships, collecting and connecting evidence to the research questions, and drawing logical and credible conclusions.

The findings indicated that AI has been gradually integrated into Chinese adult education through innovations and explorations and AI's influence is broad and profound. More specifically, the following five main themes were identified. The field's understanding of AI technology and AI's influence on adult education has evolved and become more comprehensive; AI challenges traditional Chinese adult education practices by helping to actualize personalized learning and precision education; AI transforms adult learning resource development; AI helps to turn learning environment into an open intelligent learning system; and lastly, AI urges the shift of adult educator's role in adult learning.

This study is not without limitations. Contextualized in China, this study shares the limitations with other single country studies. One such limitation is “cumulation” issue. This study should be replicated in other country contexts to further validate the generalizability of the five main themes identified in this research.

The five themes identified in this study can help understand the promises and challenges that AI brings to the field of adult education in China. These five themes can also serve as an integrated lens through which one can make sense of AI's integration into other countries' adult education practices.

This paper fulfills an identified need of understanding the current status of AI's integration into and influence on the field of adult education in China.

With Industry 4.0 and the extensive rise of smart technologies, we are seeing remarkable transformations in work practices and workplaces. Scholars report the phenomenal progress of smart technologies. At the same time, we can hear the rhetoric emphasising their potential threats. This study focusses on how and where intelligent machines are leveraged in the workplace, how humans co-working with intelligent machines are affected and what they believe can be done to mitigate the risks of the increased use of intelligent machines.

We conducted in-depth interviews with 15 respondents working in various leadership capacities associated with intelligent machines and technologies. Using NVivo, we coded and churned out the themes from the qualitative data collected.

This study shows how intelligent machines are leveraged across different industries, ranging from chatbots, intelligent sensors, cognitive systems and computer vision to the replica of the entire human being. They are used end-to-end in the value chain, increasing productivity, complementing human workers’ skillsets and augmenting decisions made by human workers. Human workers experience a blend of positive and negative emotions whilst co-working with intelligent machines, which influences their job satisfaction level. Organisations adopt several anticipatory strategies, like transforming into a learning organisation, identifying futuristic technologies and upskilling their human workers, regularly conducting social learning events and designing accelerated career paths to embrace intelligent technologies.

This study seeks to understand the emotional and practical implications of the use of intelligent machines by humans and how both entities can integrate and complement each other. These insights can help organisations and employees understand what future workplaces and practices will look like and how to remain relevant in this transformation.

In this paper a conception of the model for learning scenario determination is presented. We define the learning scenario as a sequence of the hypermedia pages, representing particular knowledge units, and tests related to them. The scenario determination is a dynamic process that begins when a new student takes up a course. The opening scenario for this student is chosen as the consensus of the final scenarios of the students, who have already finished this course, and who belong to a class of the learners similar to the new one. We have elaborated the consensus‐based procedure for the scenario determination. Since this procedure operates on a set of similar learners, we have developed the conceptions of learner’s profile and students’ classification. The learner’s profile is proposed to include the attributes describing students’ personal data (as name, birthday etc.), their cognitive and learning styles as well as their usage data (represented by the learning scenarios). The students’ classification is based on a set of the basic attributes that seem to influence the learning effects. Their significance is verified during the learning process. We have also elaborated the procedure of reducing undistinguishable values of the attribute and removing useless attributes from the set of basic attributes. A learning procedure proposed, describes generally the situations when the scenario is modified, and the methods used for its modification.

This paper aims to present a comprehensive overview of the patterns and trends of publications on artificial intelligence (AI) in personalised learning. It addresses the need to investigate the intellectual structure and development of this area in view of the growing amount of related research and practices.

A bibliometric analysis was conducted to cover publications on AI in personalised learning published from 2000 to 2022, including a total of 1,005 publications collected from the Web of Science and Scopus. The patterns and trends in terms of sources of publications, intellectual structure and major topics were analysed.

Research on AI in personalised learning has been widely published in various sources. The intellectual bases of related work were mostly on studies on the application of AI technologies in education and personalised learning. The relevant research covered mainly AI technologies and techniques, as well as the design and development of AI systems to support personalised learning. The emerging topics have addressed areas such as big data, learning analytics and deep learning.

This study depicted the research hotspots of personalisation in learning with the support of AI and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and the need for future work on diverse means to support personalised learning with AI, the pedagogical issues, as well as teachers’ roles and teaching strategies.

This research aims to investigate how a generic web‐based ITS can be created which will adapt the training content in real time, to the needs of the individual trainee across any domain.

After examining the various alternatives SCORM was adopted in this project because it provided an infrastructure that makes it possible to deliver personalised learning dynamically using re‐usable learning objects.

The results show that a system which presents a student with content that is supplementary to an authored course should be accompanied by a tool to help the trainee's navigation. For such a tool, key functionality would be: first, to identify learning objects that would take the student towards the ultimate learning goals; second, to suggest a pathway through the authored course structure and additional learning objects to the student; and finally, to present the student with different choices of pathway, such as fastest, most comprehensive and most popular routes.

This investigation has taken another approach of adapting the course by displaying an adapted set of learning objects to the trainee, instead of using a linear course structure.

The purpose of this paper is to illustrate the advantages of using intelligent agents to facilitate the location and customization of appropriate e‐learning resources and to foster collaboration in e‐learning environments.

This paper proposes an e‐learning environment that can be used to provide customized learning. It utilizes a set of interacting agents that can personalize instruction based on an individual's prior knowledge as well as their cognitive and learning needs. The e‐learning agents monitor the e‐learning environment and improve learning and collaboration based on learners' prior knowledge, social characteristics and learning style.

E‐learning agents should allow the discovery of new learning objects more easily, allow learners to customize materials presented to improve learning outcomes, and improve collaboration in the e‐learning environment.

Little prior research has been done on the use of agents in e‐learning environments. This paper proposes a set of e‐learning agents that, if implemented in online education or training environments, should provide tangible benefits to organizations.