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This conceptual chapter aims to understand the role of artificial intelligence (AI) in value co-creation phenomena in a healthcare service ecosystem, through a literature review and the definition of a conceptual framework. AI, as an operant resource, can stimulate a completely patient-centered, adaptive and resilient healthcare system, and governance models in healthcare based on data-driven decision-making (DDDM), ensuring faster choices, more timely diagnosis and more personalized treatment paths. However, the full implementation of AI in healthcare is inhibited by some frictions, mainly related to the risk that the AI black box may generate an inadequate automatic decision, also due to the quality of data used, often partial and unstructured given the reluctance to share them by patients concerned by privacy threats. The co-design (multi-part and multi-level) of a predictive decision model based on the functional transparency of the AI algorithm would allow for augmented decision as result of an effective human–machine interaction. Healthcare actors could thus make decisions using the information detected by the software (based on clear cause-and-effect correlations and modifiable variables in case of mistakes), integrated with their professional knowledge. This would also help to strengthen the patient’s perception of the decision’s reliability and accuracy and the safety of the tool (factors that can affect his/her trust). AI may be considered as a driver for value co-creation in healthcare, thanks to transparency. It would allow the promotion of collaborative behaviors involving actors by generating new institutions and new resource integration practices among them.

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.

Diabetes is a chronic disease and the major types of diabetes are type 1 and type 2. On aging, people with diabetes tend to have long-term problems in hypertension, coronary artery disease, obesity, and nerves. Given the increasing number of complications in recent years, by 2040, 624 million people will have diabetes worldwide and l in 8 adults will have diabetes in the future. Machine learning (ML) is evolving rapidly, many aspects of medical learning use ML. In this study, tension-type headaches (TTH) were associated with diabetes using SPSS, Pearson correlation, and ANOVA tests. Data were collected from Delhi NCR Hospital. It contains 30 diabetic subjects. The purpose of this study was to correlate diabetes analysis from TTH and other diseases using the latest technologies to analyze the Internet of Things and Big Data and Stress Correlation (TTH) on human health. The authors used Pearson correlation to correlate study variables and see if there was any effect between them. There was an important relationship between the percent variable, the total number of individuals, the number of individuals, and the minimum variable. The age (field) of the number of individuals to one of the total number of individuals showed a strong correlation (1.000) with a significant value of p (1.000). Overall, cases of TTH increased with age in men and do not follow the pattern of change in diabetes with age, but in cases of TTH, patterns of headaches such as diabetes increase to age 60 and then tend to decrease.

Purpose: To analyse the acceleration of artificial intelligence (AI) operations and robotic process automation (RPA) by comparing its market size and revenue worldwide during the pandemic and, measuring the impact of AI investment levels on jobs human resource functions, and analysing the role of AI in future work.

Design/Methodology: The archival data analysis technique is used to fetch data from sources like the Centre for Monitoring Indian Economy (CMIE), Statista, Deloitte, Mc Kinsey, Strata, Tractica, and IDC. Descriptive analysis with supporting literature has been contextually used for each objective which further establishes practical and theoretical implications of AI, intelligent process automation (IPA), and RPA in different industries during Covid-19 pandemic. This study analysed active scholarly articles from the Scopus database and presented results and findings.

Findings: The findings of the study state that emerging technologies such as AI, IPA, and RPA have a strong potential impact on market size, revenue, number of jobs, and investments levels during the pandemic. The global investment in AI is projected to witness an upsurge from 2018 to 2027, which significantly impacts the human workforce in various industries. The results of the study state that AI/RPA seems to be a crucial technological intervention, especially in times of the pandemic.

Originality/Value: This study contributes to the body of knowledge by constructing a base for understanding the pace of AI/RPA/IPA intervention and its significant impact on organisation process, structure, and people in different sectors. The timeline and forecast of this study intend to make industry consultants future to prepare to align themselves in an era of digital disruption.

Studying Chinese higher education internationalization policy-making requires paying attention to the political ontology of China's top-designed policy-making system before proceeding to methodological approaches. The ontology is two-fold: a fixed reality grounded in the structure and agency of the one-party state, and an emergent reality that derives from the pervasive practice of using policy documents to govern. A two-pronged epistemology is proposed to uncover these realities: interpretative and poststructural problematization. Interpretative problematization helps discern how policy-makers frame a problem–solution discourse in policy documents to achieve predetermined strategic objectives. Contrastingly, poststructural problematization views policy documents as prescriptive texts that offer rules on how to behave. The potential methodologies drawn from the tradition of critical policy sociology can be employed to study these two problematizations, thereby unpacking the fixed and emergent realities.

Along with the “reform and open door” policy launched in the late 1970s, China has experienced an annual average GDP growth rate of 9.8% between 1978 and 2002 (Hu, 2003, October 19). China's economy system has also gone through a fundamental transition from a central planning system to a socialist free market economy. To cope with the booming economy and radical social changes, the higher education system of China has been undergoing a process of expansion with marketization (World Bank, 1997).