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This chapter describes how conversational computer agents have been used in collaborative problem-solving environments. These agent-based systems are designed to (a) assess the students’ knowledge, skills, actions, and various other psychological states on the basis of the students’ actions and the conversational interactions, (b) generate discourse moves that are sensitive to the psychological states and the problem states, and (c) advance a solution to the problem. We describe how this was accomplished in the Programme for International Student Assessment (PISA) for Collaborative Problem Solving (CPS) in 2015. In the PISA CPS 2015 assessment, a single human test taker (15-year-old student) interacts with one, two, or three agents that stage a series of assessment episodes. This chapter proposes that this PISA framework could be extended to accommodate more open-ended natural language interaction for those languages that have developed technologies for automated computational linguistics and discourse. Two examples support this suggestion, with associated relevant empirical support. First, there is AutoTutor, an agent that collaboratively helps the student answer difficult questions and solve problems. Second, there is CPS in the context of a multi-party simulation called Land Science in which the system tracks progress and knowledge states of small groups of 3–4 students. Human mentors or computer agents prompt them to perform actions and exchange open-ended chat in a collaborative learning and problem-solving environment.

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.

It has often been said that a great part of the strength of Aslib lies in the fact that it brings together those whose experience has been gained in many widely differing fields but who have a common interest in the means by which information may be collected and disseminated to the greatest advantage. Lists of its members have, therefore, a more than ordinary value since they present, in miniature, a cross‐section of institutions and individuals who share this special interest.

Aarhus Kommunes Biblioteker (Teknisk Bibliotek), Ingerslevs Plads 7, Aarhus, Denmark. Representative: V. NEDERGAARD PEDERSEN (Librarian).

This chapter describes how the anticipation of connected content relegates cognitive spacing, which opens the possibility for schema acquisition. Information organization does not simply involve putting new data into folders, but instead cognitively preparing for knowledge development.

Understanding information input and output is central to providing meaningful instructional opportunities. This chapter describes the three phrases of cognitive spacing: ready, set, and go.

Information organization does not simply involve putting new data into folders, but instead cognitively preparing for knowledge development. This is accomplished by ongoing reorganizations where new information, known information, and assumed information are evaluated against current stimuli. The subsequent shifts in understanding are the fundamental crux to instilling lifelong learning within students.

The importance of spacing theory in literacy development is significant to skill development and content acquisition.

1. Documentation, library and book organizations (0) American Documentation Institute

In this special marketing abstracts issue of Marketing Intelligence & Planning a variety of topics of interest to the marketer are highlighted from the international selection of journals which feature on the Anbar coverage list. The value of a forum such as this is that quality material can be brought to the attention of the reader to which they would not otherwise be exposed. For example, would you normally take Datamation, Industrial Engineering, or The Ohio CPA?

Social question and answer (SQA) site is one of the factors that boosted up and popularized the vision of social web. It enables community members to post highly valued answers to globally asked questions and information seekers to grab intellectual information in a contextual, concise, and meaningful format at the cost of investing a few minutes. The purpose of this paper is to present a common architecture, history, and a comprehensive review of such sites.

A critical and analytical investigation of the state-of-the-art SQA sites and relevant literature has been carried out with the intention to explore the noticeable features of such sites.

By studying relevant literature, and analysing a number of existing systems, a number of research challenges are identified and a generic architecture of SQA sites is contributed.

The review contributes a comprehensive knowledge about SQA systems and aims to be helpful to new researchers who want to get a broad picture of SQA systems on a single platform. The domain is in its infancy and requires tremendous efforts from the research community to explore its salient aspects with respect to the human world.

The study inspects SQA sites on a large scale and makes an original contribution by presenting a comprehensive review, future research challenges, and a generic architecture of SQA sites.