Purpose This study aims to provide a systematic review of the existing literature on the applications of deep learning (DL) in hospitality, tourism and travel as well as an agenda for future research. Design/methodology/approach Covering a five-year time span (2017–2021), this study systematically reviews journal articles archived in four academic databases: Emerald Insight, Springer, Wiley Online Library and ScienceDirect. All 159 articles reviewed were characterised using six attributes: publisher, year of publication, country studied, type of value created, application area and future suggestions (and/or limitations). Findings Five application areas and six challenge areas are identified, which characterise the application of DL in hospitality, tourism and travel. In addition, it is observed that DL is mainly used to develop novel models that are creating business value by forecasting (or projecting) some parameter(s) and promoting better offerings to tourists. Research limitations/implications Although a few prior papers have provided a literature review of artificial intelligence in tourism and hospitality, none have drilled-down to the specific area of DL applications within the context of hospitality, tourism and travel. Originality/value To the best of the authors’ knowledge, this paper represents the first theoretical review of academic research on DL applications in hospitality, tourism and travel. An integrated framework is proposed to expose future research trajectories wherein scholars can contribute significant value. The exploration of the DL literature has significant implications for industry and practice, given that this, as far as the authors know, is the first systematic review of existing literature in this research area.

The ambitious and wide-ranging paper on Academic Health Science Systems ['AHSS'] [1] proposed a new model for health innovation and stimulated considerable interest. The paper made three main assumptions about AHSS: i) university-based centres should play linchpin roles in health and social care innovation; ii) medical innovation cannot be achieved without links to industry; iii) innovation occurs at the scientific end of a discovery-care continuum. But the paper had a pregnant coda for the NHS, and GM devolution in particular: the authors explicitly linked their view of the need for the integration of university-based research and health care delivery to population level approaches, suggesting that vertically integrated AHSSs should ultimately transform into integrated care organisations. When Manchester's experiment in the devolution of health and social care as a place-based approach to health and social care began in 2015, Health Innovation Manchester was created as an AHSS to support innovation in the Partnership. Five years after the start of devolution, this short paper, which is based on a longer study of Health Innovation Manchester's development [2], provides an overdue reflection on the proposition advanced just over a decade ago [1].

This chapter provides a sense of what artificial intelligence is, its benefits, and integration to higher education. Seeing through the lens of the literature, this chapter will also explore the emergence of artificial intelligence and its attendant use for learning and teaching in higher education institutions. It begins with an overview of artificial intelligence and proceeds to discuss practical applications of emerging technologies and artificial intelligence on the manner in which students learn as well as how higher education institutions teach and develop. The chapter concludes with a discussion on the challenges of artificial intelligence on higher education.

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