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This paper explores the management of emotions and emotional challenges that human trainees face when interacting with a robot or a humanoid trainer.

This study draws on existing academic and grey literature on robot and humanoids-based training with algorithms, bots, and artificial intelligence (AI).

The study highlights the need for personalized feedback, clear communication, and the establishment of trust between the trainee and robotic trainer. The study discusses the strategies to manage emotions like anger, disgust, fear, happiness, sadness, and surprise that are experienced by human trainees.

The research provides an accessible summary of setting realistic expectations for the emotional experience of working with a robotic trainer to help manage expectations and reduce disappointment.

The managers in charge of implementing robotic training programs can provide education and resources to help individuals effectively manage emotions when working with a robotic trainer.

This viewpoint paper investigates the changing role of leadership in a dynamic, technologically driven society, and the vital requirement for leaders to engage in continuous self-upskilling to remain effective. It emphasizes the importance of generative artificial intelligence (GAI) in transforming personalized learning experiences for leaders and allowing them to adapt to an ever-changing world.

A review of current research papers, articles, and case studies is conducted to evaluate the integration of generative AI in leadership self-upskilling. It examines the possibilities and possible benefits of generative AI, and the issues it offers regarding data privacy, algorithmic bias, and learning requirements.

The findings highlight the transformational potential of GAI in self-upskilling for leaders. It demonstrates how GAI can build personalized learning materials, provide real-time feedback, and adapt content to individual learning styles. It identifies notable executives who have effectively embraced GAI for their self-upskilling journeys, resulting in increased productivity and competitiveness.

The paper investigates the application of GAI for self-improvement, addressing challenges such as data privacy and algorithmic bias while suggesting responsible AI use tactics.

This study investigates the relationship between leadership and AI, emphasizing the importance of leaders in self-improvement as well as the possibility of AI-powered self-upskilling to democratize leadership development while also promoting ethical use.

They proposed the necessity of restructuring higher education to provide students and instructors with the skills required for future employment in a world driven by artificial intelligence (AI). The implementation of AI in higher education and its impact on Generation Z students' academic ambitions.

Higher education plays a vital role in cultivating ethical individuals and professionals on a worldwide scale. The implementation of AI in higher education and its impact on Generation Z students' academic ambitions. This study used qualitative methods to examine the viewpoints of students regarding the influence of AI on higher education. For this study, a cohort of 25 students from Pune city was chosen.

The results indicate that there is a need to reform higher education in order to prepare students for future jobs in a society driven by AI. They indicated a lack of extensive understanding on the subject and so sought a clear explanation from an AI during their consultation. Based on the results of this study, it is evident that Generation Z students do not experience fear or worry in relation to emerging technology. On the contrary, they embrace multitasking and actively want to acquire new skills to prepare themselves for the future.

This study has limitations; the data were collected from 25 students, and the insights gathered may not represent the whole population. The geographical restriction was that it was restricted only to Pune. Second, educators are equally important and may have different views; therefore, future studies should collect educators’ views.

They proposed restructuring higher education to provide students and instructors with the skills required for future employment in a world driven by AI. Their proposal introduces novel learning goals that prioritize the development of abilities in both information, learning and education through the use of AI. Participants' narratives are evaluated using advanced techniques such as VOSviewer to give in-depth analysis.

The purpose of this study is to provide an overview of artificial intelligence (AI) in the talent management sphere. The study seeks to contribute to the body of knowledge with respect to human resource management and AI by conducting a literature review on the integration of AI in talent management, synthesising existing approaches and frameworks, as well as emphasising potential benefits.

The study adopts desk research, computational literature review (CLR) and uses topic modelling [with bidirectional encoder representations from transformers (BERTopic)] to throw light on the diffusion of AI in talent management.

The study’s main finding is that the area of AI in talent management is on the verge of gradual development and is in tandem with the growth of AI. We deduced that there is a link between talent management practices (planning, recruitment, compensation and rewards, performance management, employee empowerment, employee engagement and organisational culture) and AI. Though there are some known fears with regards to using the innovation, the benefits outweigh the demerits.

The current study has some limitations. The scope and size of the sample are the primary limitations of this study. No form of qualitative analytics was used in this study; as a result, the information obtained was limited. The study provides a snapshot of AI in talent management and contributes to the lack of literature in the joint fields. Also, the study provides practitioners and experts an overview of where to target investments and resources if need be.

The originality of this study comes from the combination of CLR methods and the use topic modelling with BERTopic which has not been used by previous reviews. In addition, the salient machine learning algorithms are identified in the study, which other studies have not identified.