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This study focuses on assistant principals, the “forgotten future workforce” of educational leadership. We explored the current landscape of assistant principalship within the context of work performance, including both task and discretionary performance, and the future career aspirations of assistant principals from a cross-national perspective. Specifically, the study aimed to fulfill the following objectives: (a) to identify the factors affecting the task and discretionary performance of assistant principals, (b) to identify the factors affecting three future career aspirations of assistant principals, and (c) to determine whether the influences of these factors differ by national origin. Personal initiative and perceived organizational support (POS) were the independent variables. This study also examined the demographic attributes of the participants and their schools. Two randomly selected samples, which composed of 227 Turkish and 144 American assistant principals were the participants. The data-gathering instrument incorporated the revised versions of the Personal Initiative Scale (Fay & Frese, 2001), the Perceived Organizational Support Scale (Eisenberger, Huntington, Hutchison, & Sowa, 1986), and the School Organizational Citizenship Behavior Scale (DiPaola & Tschannen-Moran, 2001). The findings of the study showed that personal initiative and POS significantly predicted the task performance, discretionary performance, and certain future career aspirations of assistant principals. National origin appeared to be a significantly differentiating factor of the assistant principals' task performances, discretionary performances, and future career aspirations. We drew conclusions and provided suggestions for future research.

Voice command and voice search are becoming increasingly popular in the hospitality and tourism industry, with many hotels and travel companies investing in voice-enabled technology to improve guest experiences and streamline operations. This technology allows travellers to manage their travel plans, request services and get information through natural voice commands on any voice-enabled device. Voice assistants are also multilingual, allowing hotels to customise responses to guests who do not speak the local language. Angie, a multilingual, in-room voice assistant, is an example of this technology. It can fulfil guest requests, answer common questions about the property and create streamlined access to a wide range of hotel amenities, such as ordering room service or requesting extra towels. Hotels can control questions and responses to assist stretched staff and provide upsell and advertising revenue through digital promotions or recommended onsite amenities or discounts. In addition, voice command technology can be used to book travel and find things to do at a destination. Google Assistant can help with travel plans like booking a hotel, checking flight status and finding things to do at a destination. In conclusion, voice command and voice search technology are transforming the hospitality and tourism industry by improving guest experiences, reducing operational costs and increasing revenue.

Training, particularly in the form of comprehensive professional development, continues to be a need for paraeducators (also known as teacher assistants). Training needs begin with an initial set of knowledge and skills and is built based upon the paraeducator’s role with individual students and the educational settings. Standards or guidance documents are available from a few individual states within the United States, higher education systems, and professional organizations that serve individuals with exceptional needs and agencies. An international professional organization, Council for Exceptional Children [CEC] (2011), identified a common skill set that reinforces standards for defining curricula when providing training to paraeducators. Key to their ongoing professional development is the on-the-job coaching by the education professional (teacher), to support the application of skills into the inclusive setting. Various forms of professional development are available including online trainings in addition to face-to-face.

The COVID-19 pandemic stressed the health care sector's longstanding pain points, including the poor quality of frontline work and the staffing challenges that result from it. This has renewed interest in technology-centered approaches to achieving not only the “Triple Aim” of reducing costs while raising access and quality but also the “Quadruple Aim” of doing so without further squeezing wages and abrading job quality for frontline workers.

How can we leverage technology toward the achievement of the Quadruple Aim? I view this as a “grand challenge” for health care managers and policymakers. Those looking for guidance will find that most analyses of the workforce impact of technological change consider broad classes of technology such as computers or robots outside of any particular industry context. Further, they typically predict changes in work or labor market outcomes will come about at some ill-defined point in the medium to long run. This decontextualization and detemporization proves markedly problematic in the health care sector: the nonmarket, institutional factors driving technology adoption and implementation loom especially large in frontline care delivery, and managers and policymakers understandably must consider a well-defined, near-term, i.e., 5–10-year, time horizon.

This study is predicated on interviews with hospital and home health agency administrators, union representatives, health care information technology (IT) experts and consultants, and technology developers. I detail the near-term drivers and anticipated workforce impact of technological changes in frontline care delivery. With my emergent prescriptions for managers and policymakers, I hope to guide sectoral actors in using technology to address the “grand challenge” inherent to achieving the Quadruple Aim.

Over 3 million intermittently employed and socially disadvantaged workers receive low wages and limited benefits in diverse long-term care settings and employment arrangements as they try to become a positively valued unified occupation: “direct care workers.” Before this occurs, these workers must overcome negative definitions imposed by three powerful institutions: professional guilds, employers, and states. Care workers’ legitimacy is challenged as nursing labels them “unlicensed, assistive personnel,” defining them in terms of their task relationship to nurses rather than their social relationship to clients. Care workers’ identity is obscured as corporate rationalization nullifies their unique contributions with task unbundling, part-time work, short staffing, and turnover undermining bonding with colleagues and clients. State regulation impedes care workers’ integration, segmenting similar workers under different regulatory regimes, defining workers negatively rather than by their educational attainments and competencies. Overcoming this triple negation will require not just cultural change, but also real structural changes, and can occur only through concerted actions involving coalitions. Labor market intermediaries, public authorities, labor unions, workforce investment boards, philanthropic organizations, and government interagency groups are among those supporting direct care workers’ advancement by strategically coordinating licensing, purchasing, and developing the workforce. Recent federal policy changes and health reform legislation have enhanced recognition of this occupation and are providing new resources for its development.

In the recent past, mobile technologies that track the movement of people, freight and vehicles have evolved rapidly. The major categories of such technologies are reviewed and a number of attributes for classification are proposed. The willingness of people to engage in such technologically based surveys and the reported biases in the make-up of the sample obtained are reviewed. Lessons are drawn about the nature of the samples that can be achieved and the representativeness of such samples is discussed. Data processing is addressed, particularly in terms of the processing requirements for logged data, where additional travel characteristics required for travel analysis may need to be imputed. Another issue explored is the reliability of data entered by respondents in interactive devices and concerns that may arise in processing data collected in real time for prompting or interrogating respondents. Differences, in relation to the data user, between data from mobile devices and data from conventional self-report surveys are discussed. Potentials that may exist for changes in modelling from using such data are explored. Conclusions are drawn about the usefulness and limitations of mobile technologies to collect and process data. The extent to which such mobile technologies may be used in future, either to supplement or replace conventional methods of data collection, is discussed along with the readiness of the technology for today and the advances that may be expected in the short and medium term from this form of technology.

In this chapter, we address the question if and how modern technology can be used to design questionnaires, diaries, web sites, and experiments to improve the validity of reliability of active data collection instruments. In particular, it discusses the history of computer-assisted activity diary data, reenactment sessions, stated preference methods, and interactive computer experiments with a special focus on the design of these instruments in terms of respondent support and user interfaces. Empirical evidence and experience suggests that although fascinating instruments may increase respondent motivation and involvement and therefore improve the reliability of the measurements, there is also the danger that respondents' answers are influenced by features of the electronic instrument that are not essential, reducing validity and reliability.

This paper examines job satisfaction and participation in decision making in three home health aide facilities with different organizational structures (worker-owned for-profit, for-profit with no participation or ownership by workers, and nonprofit).

More than 600 surveys were completed by home health aides across the three facilities. The author also engaged in participant observation during training sessions and other meetings and conducted a small number of interviews with caregivers and agency management.

Home health aides at the worker-owned, participative decision making organization were significantly more satisfied with their jobs than those at the other agencies. Results for the other agencies were not significantly distinguishable from one another.

This study involved respondents from one of each type of business. A study across several of each type of organization would allow more focus on the effects of the structural characteristics of the organizations.

In the United States, the work that home health aides perform provides a valuable service to society. On behalf of caregivers and those for whom they provide care, conditions of the work need improvement. If participative democratic workplaces provide better outcomes, they should receive more attention from lawmakers, the business community, and researchers.

This research highlights the working conditions of the people (primarily women) who perform this work. The poor compensation received is a reminder of inequality in opportunity for some workers and of the value placed on this type of caring labor.

This research is unique in its focus on work environment and outcomes in home health care across nonprofit, for-profit, and worker-owned for-profit organizations. The findings of different job satisfaction outcomes from the others in the worker-owned organization and similar outcomes in the nonprofit and conventional for-profit organizations are also unique.

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.