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The aim of this chapter is to discuss the key challenges that the academia and the academics of higher education have to face in relation to AI but also to make recommendations on the strategies that the academia can adopt so to optimize the use of AI in an ethical manner. Due to the lack of knowledge in the field of AI, there is limited literature on the field of AI, especially on issues related to academic integrity. For this reason, this chapter suggests several recommendations on how AI can be a foe not an enemy of academia. Those practices include the developing a culture of ethos for the use of AI among the stakeholder of higher education, the use of AI as a personalized tutor, and on grading. However, from a critical perspective, the most important issue of AI is academic integrity. At this case the stakeholders of higher education must take immediate action so to ensure the ethical use of IA in Higher Education. The authors of this chapter suggest making modifications on the way that students are assessed, including having more examinations and online quizzes along with written assignments which will promote critical reflections so to avoid the use of AI in written assignments.

Based on the micro-electro-mechanical system (MEMS) technology, acoustic emission sensors have gained popularity owing to their small size, consistency, affordability and easy integration. This study aims to provide direction for the advancement of MEMS acoustic emission sensors and predict their future potential for structural health detection of microprecision instruments.

This paper summarizes the recent research progress of three MEMS acoustic emission sensors, compares their individual strengths and weaknesses, analyzes their research focus and predicts their development trend in the future.

Piezoresistive, piezoelectric and capacitive MEMS acoustic emission sensors are the three main streams of MEMS acoustic emission sensors, which have their own advantages and disadvantages. The existing research has not been applied in practice, and MEMS acoustic emission sensor still needs further research in the aspects of wide frequency/high sensitivity, good robustness and integration with complementary metal oxide semiconductor. MEMS acoustic emission sensor has great development potential.

In this paper, the existing research achievements of MEMS acoustic emission sensors are described systematically, and the further development direction of MEMS acoustic emission sensors in the future research field is pointed out. It provides an important reference value for the actual weak acoustic emission signal detection in narrow structures.

This paper introduces executive functions (EFs)–i.e. high-level cognitive processes that are elicited in novel and non-routinised situations–into discussions within entrepreneurship education (EE). By reviewing the existing literature, it highlights how EFs are important for the entrepreneur, their role in the entrepreneurial process and implications for improving EE.

We conduct a literature review bridging cognitive psychology, EE and entrepreneurship fields to clarify the role of EFs in the entrepreneurial process. To do so, we define EFs and then propose a model of the entrepreneurial process to frame our review and identify knowledge and gaps in current research.

This review shows why EFs are valuable for EE and calls for more focus on them to better prepare students for entrepreneurship and general life challenges. The findings underscore the importance of EFs in understanding key aspects of the entrepreneurial process. Although EFs are studied in the entrepreneurship and EE fields, they are rarely conceptualised from a cognitive psychology perspective, with research often focusing on isolated EF components instead of examining them as a whole.

This review is the first to highlight the role of EFs in the entrepreneurial process in a structured way. Integrating cognitive psychology insights on EFs can enrich EE for both venture creation and value creation approaches while also supporting the development of more effective programs. This focus on EFs also provides a fresh perspective and a valuable lens for understanding complex phenomena such as cognition, learning and the factors behind success and failure in entrepreneurship.

The insurgence of the COVID-19 pandemic insinuated that family-owned small hotels (F-OSH) should adopt AI capabilities and innovation activities and digitize their operations to survive. This study examines the potential of AI capabilities to digitally transform F-OSHs by leveraging innovation ambidexterity, preparing them for future disasters proactively. Additionally, it sheds light on how the impact of AI capabilities on innovation ambidexterity varies based on strategic fit. In addition, this research explores the influence of digital entrepreneurial intention on fostering innovation ambidexterity, essential for digital transformation in F-OSHs.

The study collected primary data from 318 descendant entrepreneurs designated as chairpersons or managing directors in F-OSH and analyzed the data using the partial least structural equation modeling technique.

This study found a positive association of AI capabilities, and digital entrepreneurial intention with the digital transformation of F-OSHs, while strategic fit does not have an association with innovation ambidexterity. Innovation ambidexterity mediates the relationship between AI capabilities and digital transformation in F-OSHs. Moreover, a strong strategic fit increases the effect of AI capabilities on innovation ambidexterity. Furthermore, a high intention for digital entrepreneurship reduces the impact of innovation ambidexterity on the digital transformation of F-OSHs.

The combination of AI capabilities and innovation ambidexterity has transformed F-OSHs' digital transformation. This proactive approach to dealing with economic recessions such as COVID-19 is also influenced by digital entrepreneurial intention and strategic fit.

Anchored on the dynamic capability theory, this study provides valuable insights and novel empirical evidence by investigating the mediating mechanism of innovation ambidexterity and boundary condition of strategic fit and digital entrepreneurial intention between AI capabilities and digital transformation in F-OSHs.

Many studies have examined pre-diploma students' backgrounds and academic performance with results showing that some did not achieve the expected level of competence. This study aims to examine the relationship between students’ demographic characteristics and their academic achievement at the pre-diploma level using machine learning.

Secondary data analysis was used in this study, which involved collecting information about 1,052 pre-diploma students enrolled at Universiti Teknologi MARA (UiTM) Pahang Branch between 2017 and 2021. The research procedure was divided into two parts: data collecting and pre-processing, and building the machine learning algorithm, pre-training and testing.

Gender, family income, region and achievement in the national secondary school examination (Sijil Pelajaran Malaysia [SPM]) predict academic performance. Female students were 1.2 times more likely to succeed academically. Central region students performed better with a value of 1.26. M40-income students were more likely to excel with an odds ratio of 2.809. Students who excelled in SPM English and Mathematics had a better likelihood of succeeding in higher education.

This research was limited to pre-diploma students from UiTM Pahang Branch. For better generalizability of the results, future research should include pre-diploma students from other UiTM branches that offer this programme.

This study is expected to offer insights for policymakers, particularly, the Ministry of Higher Education, in developing a comprehensive policy to improve the tertiary education system by focusing on the fourth Sustainable Development Goal.

These pre-diploma students were found to originate mainly from low- or middle-income families; hence, the programme may help them acquire better jobs and improve their standard of living. Most students enrolling on the pre-diploma performed below excellent at the secondary school level and were therefore given the opportunity to continue studying at a higher level.

This predictive model contributes to guidelines on the minimum requirements for pre-diploma students to gain admission into higher education institutions by ensuring the efficient distribution of resources and equal access to higher education among all communities.

Given the increasing attention on ChatGPT in academia due to its advanced features and capabilities, this study aims to examine the links among Artificial intelligence (AI), knowledge sharing, ethics, academia and libraries in educational institutions. Moreover, this study also aims to provide a literature base while discussing recent trends in AI and ChatGPT technologies, highlighting their specific uses in institutions.

The paper involves a structured interview format where a human interviewer poses questions “Qs” in ChatGPT, related to knowledge sharing, ethics, academia and libraries. Moreover a literature base is also provide to discussed recent trends in AI and ChatGPT technologies, highlighting their specific uses in institutions.

The study find out that AI and ChatGPT technologies in educational institutions affect knowledge sharing, ethical consideration, academia and libraries. This study also highlights literature directions for the trends and proper use of the AI and ChatGPT among institutions, such as improving student-learning engagement.

This research contributes to the prior literature by offering an in-depth review of current uses and applications of AI and ChatGPT in educational institutions. It not only highlights key trends and innovations but also provides insights and guidelines for future research. This study also provides insights and guidelines for future research. Furthermore, the article emphasizes the potential impact of AI and ChatGPT on the future of education and technology.

The theoretical background bases on the big data analytics-artificial intelligence (BDA-AI) technologies and supply chain ambidexterity (SCAX) in the firms to assess their sustainability endeavors such as green supply chain management (GSCM) to improve their green communication and corporate image.

Around 220 participants in the manufacturing firms are participants' industry expertise, diverse roles, and representation as key stakeholders.

The results show BDA-AI and SCAX affected on GSCM and found the significant relationships with green communication and corporate image. Green communication was discovered to impact corporate image significantly.

Prior studies are neglected to address the relationship among the AI, powered by rapid computational and BDA breakthroughs, redefines cognitive tasks, achieving feats previously deemed impossible-making implicit judgments, simulating emotions, and driving operations. This study selects manufacturing firms as respondents due to their forefront of BDA-AI and supply chain ambidexterity adoption to benefit the operational efficiency and competitiveness. The firms intricate supply chains, diverse stakeholders, and strategic emphasis on corporate image make it an ideal context to examine the nuanced impact of these technologies.

This study aims to measure the integrated impact of big data analytics and artificial intelligence (BDA-AI) adoption by using the ChatGPT generative AI online platform as a BDA-AI tool on the operational and environmental performance.

This study considers Taiwanese professionals who engage with ChatGPT; the sample consists of 388 online users.

This study’s main finding is that the considered antecedents – including technological, organizational and environmental contexts, tangible resources and workforce skills – are significantly associated with BDA-AI adoption. Notably, BDA-AI adoption exhibits a significant relationship with operational performance, environmental performance and environmental process integration. Moreover, environmental process integration is significantly correlated with environmental performance. Lastly, operational performance is significantly correlated with environmental performance.

This study contributes to the heavily lacking but developing literature on the antecedents and consequences of BDA-AI adoption. Its theoretical foundation consists of the technological-organizational-environmental model, Roger’s diffusion of innovation theory and resource-based view theory.

The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.

A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.

Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.

This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.

The emergence of artificial intelligence (AI) which operates through technology and digital workspace has proven to transform organisations in recent times. However, there has been key concern over its efficiency among the workforce on how it may replace human intelligence in the contemporary work environment. This study aims to investigate the drawbacks otherwise known as the dark side of AI and its effect on employee quality of work−life and organisational performance through the lens of employee capacity development in reducing its shortcomings.

This study used a descriptive research design using a cross-sectional survey approach to administer the research instrument to 1,847 customer service officers of banks, customer agents of telecoms, customer care of retail organisations in Nigeria business environment across various units were respondents of this study, however, 862 participants were finally used. A simple random strategy was used to survey the study participants, and existing scales were adopted to form a new research instrument. A partial least square (PLS) based structural equation model (SEM) was adapted to analyse the collected data from the respondents.

The outcome of the study indicated that AI lacks creativity and has a negative impact on both employee quality of work−life and overall organisational performance. The outcome of the study demonstrated the drawbacks and the dark sides of AI as lack of emotional intelligence, lack of in-depth contextual knowledge, over-reliance on data quality and lack of ethical and moral decision analysis are the possible dark side of AI which adversely affect quality of work−life and overall performance of the organisations. The study concluded that it is difficult to replace human intelligence because of AI’s drawbacks and dark side. AI cannot function effectively beyond what is programmed in the system.

This study has offered a novel trajectory against the efficiency and possible benefits of AI that people are familiar with. It has changed the understanding of the researchers, policymakers and organisations that AI cannot replace human intelligence in the workplace without improvement on those established AI dark sides.