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Background: Artificial intelligence (AI) is a booming sector that has profoundly influenced every walk of life, and the education sector is no exception. In education, AI has helped to develop novel teaching and learning solutions that are currently being tested in various contexts. Businesses and governments across the globe have been pouring money into a wide array of implementations, and dozens of EdTech start-ups are being funded to capitalise on this technological force. The penetration of AI in classroom teaching is also a profound matter of discussion. These have garnered massive amounts of student big data and have a significant impact on the life of both students and educators alike.

Purpose: The prime focus of this chapter is to extensively review and analyse the vast literature available on the utilities of AI in health care, learning, and development. The specific objective of thematic exploration of the literature is to explicate the principal facets and recent advances in the development and employment of AI in the latter. This chapter also aims to explore how the EdTech and healthcare–education sectors would witness a paradigm shift with the advent and incorporation of AI.

Design/Methodology/Approach: To provide context and evidence, relevant publications were identified on ScienceDirect, PubMed, and Google Scholar using keywords like AI, education, learning, health care, and development. In addition, the latest articles were also thoroughly reviewed to underscore recent advances in the same field.

Results: The implementation of AI in the learning, development, and healthcare sector is rising steeply, with a projected expansion of about 50% by 2022. These algorithms and user interfaces economically facilitate efficient delivery of the latter.

Conclusions: The EdTech and healthcare sector has great potential for a spectrum of AI-based interventions, providing access to learning opportunities and personalised experiences. These interventions are often economic in the long run compared to conventional modalities. However, several ethical and regulatory concerns should be addressed before the complete adoption of AI in these sectors.

Originality/Value: The value in exploring this topic is to present a view on the potential of employing AI in health care, medical education, and learning and development. It also intends to open a discussion of its potential benefits and a remedy to its shortcomings.

Almost every detail of our lives – where we go, what we do, and with whom – is captured as digital data. Technological advancements in cloud computing, artificial intelligence, and data analytics offer the education sector new ways not only to improve policy and processes but also to personalize learning and teaching practice. However, these changes raise fundamental questions around who owns the data, how it might be used, and the consequences of use. The application of Big Data in education can be directed toward a wide range of stakeholders, such as educators, students, policy-makers, institutions, or researchers. It may also have different objectives, such as monitoring, student support, prediction, assessment, feedback, and personalization. This chapter presents the nuances and recent research trends spurred by technological advancements that have influenced the education sector and highlights the need to look beyond the technical boundaries using a socio-semiotic lens. With the explosion of available information and digital technologies pervading cultural, social, political as well as economic spaces, being a lifelong learner is pivotal for success. However, technology on its own is not sufficient to drive this change. For technology to be successful, it should complement individual learning cultures and education systems. This chapter is broadly divided into two main sections. In the first section, we contemplate a vision for the future, which is deemed possible based on ongoing digital and computing advancements. The second section elaborates the technological, pedagogical, cultural, and political requirements to attain that vision.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.

Using Y2K compliance as a starting point, NCR established a standard IT infrastructure that's consistent for all its offices worldwide.

Pink ring is a ubiquitous problem arising during the manufacture of multilayer PCBs, being the manifestation of local delamination at the inner‐layer oxide interfaces around drilled holes and subsequent dissolution of the oxide during plating processes. Except in extreme cases, there is no evidence that the occurrence of pink ring identifies any in‐service reliability problem, but it is nevertheless a clear process indicator and is strictly monitored in statistical process control. The UK Printed Circuit Industry has carried out a collaborative research programme aimed at providing an understanding and a quantitative analysis of the pink ring condition. The research has advanced on two fronts: (i) an investigation into the micro‐mechanisms of the delamination and stress relief around drilled holes and subsequent rôles of the desmear and plating chemicals, and (ii) a statistical analysis of boards manufactured in a variety of ways, analysing the quantitative measurements of pink ring in terms of, for example, panel source, drill supplier, drill quality, drilling backing material, drilling chip rate, stack position, and panel entry/exit side.

To the Editor. SIR,—I feel that I must write a few lines to congratulate you and Dr. Pugsley on his article on “Structural Research in Aeronautics” published in your June issue.

The presence of digital learning space is widely seen as there is an active engagement between educators and learners. However, the challenge raised mainly amidst the pandemic age, which is potentially leading to the interference on the active engagement in education process. The necessary act to have a critical response from the student’s feedback towards the online learning services should be taken into consideration in ensuring the continuance of teacher education in enabling to grab the potential chance to advance the assessment of strategic approach in online learning. This paper aims to examine the digital access during the pandemic age through elaborating the extensive value of better learning service or adaptation for the online learning achievement amidst the pandemic age.

This study is conducted with a qualitative approach through the particular method of data collection, namely, structured interview. This qualitative approach was selected to enable obtaining the richness of information and related data. The insightful feedback will be coming from 27 higher education learners.

The finding revealed that better design of achievement pathway on the digital access could be enhanced in supporting the online learning performance through the online services. The main point refers to look into detail about digital online infrastructure insufficiency for online access support and improvements on digital online infrastructure for accessibility of learning service. The main occupations are clearly pointed in the following phase. Those are empowering digital access for learning service support and enhancing digital-adaptation for online learning achievement.

This study is supposed to contribute in assisting the value contribution with an extensive point to continue the digital access during pandemic age through the adaptation empowerment of higher learner’s online learning services.

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.

After 40 years, IJPDLM received its first impact factor from Web of Science in 2010. This anniversary editorial provides a retrospective bibliometric assessment of IJPDLM over its initial five years as a Web of Science journal (2011-2015). First, IJPDLM’s citation metrics are compared to those for the Web of Science journal subject category of Management. Next, IJPDLM’s most cited articles, best papers and special issues together with the international diversity of the journal’s author base from 2011 to 2015 are reviewed. The analysis also presents the journals that cite IJPDLM most frequently, as well as the journals most frequently cited in IJPDLM. Finally, IJPDLM is compared to peer journals in the logistics and SCM field on various scholarly metrics including impact factor, five-year impact factor, h5-index, number of citations received and self-citation rate. The paper aims to discuss these issues.

Retrospective bibliometric analysis of IJPDLM from 2011 to 2015.

Boosted by the journal’s admission to Web of Science in 2010, IJPDLM has made steady progress toward fulfilling the mission of providing its constituents with timeliness, inclusiveness and impact.

The comparison of IJPDLM’s scholarly metrics with those of peer journals and journals in the Web of Science Management category will be of interest and value to logistics and SCM researchers.

The retrospective overview and celebration of IJPDLM’s progress over the last five years and future directions will be of interest to the journal’s stakeholders and prospective authors.