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This chapter describes five disciplinary domains of research or lenses that contribute to the design of a team tutor. We focus on four significant challenges in developing Intelligent Team Tutoring Systems (ITTSs), and explore how the five lenses can offer guidance for these challenges. The four challenges arise in the design of team member interactions, performance metrics and skill development, feedback, and tutor authoring. The five lenses or research domains that we apply to these four challenges are Tutor Engineering, Learning Sciences, Science of Teams, Data Analyst, and Human–Computer Interaction. This matrix of applications from each perspective offers a framework to guide designers in creating ITTSs.

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.

This chapter examines some of the challenges and emerging strategies for authoring, distributing, managing, and evaluating Intelligent Tutoring Systems (ITSs) to support computer-based adaptive instruction for teams of learners. Several concepts related to team tutoring are defined along with team processes, and fundamental tutoring concepts are provided including a description of the learning effect model (LEM), an exemplar describing interaction between learners and ITSs with the goal of realizing optimal tutor decisions. The challenges noted herein are closely related to the LEM and range from acquisition of learner data, synthesis of individual learner and team state models based on available data, and tutor decisions which center on optimizing strategies (recommendations) and tactics (actions) given the state of the learner, the team, and the conditions under which they are being instructed, the environment. Finally, we end this chapter with recommendations on how to use this book to understand and design effective ITSs for teams.

This chapter introduces the concept of an intelligent organization in the context of the twenty-first-century knowledge economy. An intelligent organization is one in which individuals behave intelligently, work is grounded in intelligent methods and choices, and rich stocks of intelligence in the form of knowledge capital to support intelligence work and choices. Intelligence is defined as both a thing and attribute and behavior and way of working. The chapter also highlights examples of intelligent behaviors and or organizational pathologies. The chapter also highlights the importance of becoming aware of intelligent and unintelligent choices.

This chapter explains how organizations can build intelligence capabilities into their everyday working environments. The definition of capacity building builds upon the organizational management and the strategic workforce development literatures. This chapter also derives essential guidance from another series focused on critical capabilities and capacity building. The authors highlight the role of a strong intelligence culture and learning in building intelligence capacity. Capacity building is achieved through short- and long-term efforts. This chapter also highlights the importance of balancing capacity building across everyday business operations and specialized intelligence functions.

This chapter examines firm strengths and weaknesses from the standpoint of intangible assets. These are compared within and across industry sectors in order to better understand who might be a potential collaborator (or competitor) in different contexts. Establishing the conceptual basis of a range of intangibles, including data, explicit knowledge, tacit knowledge, and intelligence, the chapter moves to metrics for assessing industry averages and individual firm capabilities. Finally, several sectors in healthcare are examined, specifically identifying what kinds of collaborators would best fit with a technology-driven start-up like Theranos.

Public librarians throughout North America now support physical activity. One sees this function in the emergence and diffusion of new programs and services, such as librarians checking out exercise equipment, as well as in librarians actually sponsoring exercise classes. This chapter focuses on understanding this type of work. The first part looks at five different frameworks – the library as place, community-led librarianship, whole person librarianship, community health, and recreation and leisure – that each in different ways enable one to understand how supporting physical activity could become part of the work of public librarians. Focus then shifts to understanding empirically how public librarians in the US and Canada enact and understand this work. Research shows that this role has become more integral and expected in youth services than in adult services. Library staff themselves are more likely to lead movement-based programs for youth than for adults. The discussion then shifts to the implications of this trend in terms of evidence-based practice and multidisciplinary discussions on how and why to increase physical activity throughout society. The conclusion suggests additional work needed to understand this and other poorly understood functions of public librarians.