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In this chapter, a general introduction on artificial intelligence (AI) is given as well as an overview of the advances of AI in different engineering disciplines, including its effectiveness in driving the United Nations Sustainable Development Goals (UN SDGs). This chapter begins with some fundamental definitions and concepts on AI and machine learning (ML) followed by a classification of the different categories of ML algorithms. After that, a general overview of the impact which different engineering disciplines such as Civil, Chemical, Mechanical, Electrical and Telecommunications Engineering have on the UN SDGs is given. The application of AI and ML to enhance the processes in these different engineering disciplines is also briefly explained. This chapter concludes with a brief description of the UN SDGs and how AI can positively impact the attainment of these goals by the target year of 2030.

Many studies document the importance of learner-centered active teaching to improve college students' critical engagement with challenging problems presented by our information-rich twenty-first-century environment. Others indicate that students from less privileged backgrounds often struggle even in well-designed classrooms. What is lacking is a mechanism for understanding these divergent outcomes and designing courses that better meet the needs of the diverse students in the college classroom. In this chapter, an argument is presented for understanding college student learning and curriculum design through the lenses of epistemological development and behaviors of learning. The consensus model presents descriptions of four epistemological stages, creating a framework to help classroom practitioners and administrators better understand the abilities of their students. The foundational assumption is that using appropriate curricular components will support student engagement and epistemological and self-regulation growth. To support this assumption, the model is accompanied by research-supported activities and strategies that benefit learners at different developmental stages and with different degrees of self-regulation. Moreover, intentional and reflective teaching has the potential to improve faculty understanding about the nature of learning and acceptance of learner-centered pedagogies, which will also have positive consequences for students. The end result will be a more inclusive learning environment with improved outcomes for a wider range of students.

Faced with the outbreak of the pandemic, Chinese government quickly postponed the opening of schools and advocated “classes suspended but learning continues” project through online learning. In order to understand the teaching effect of online learning and explore a possible transformation of the application of educational information technology in the future, questionnaires have been used in this study to collect data of students, principals, and teachers across China. Most students and principals are satisfied with online learning, while teachers suggest that it is considerably difficult to teach online. Meanwhile, students, schools, and teachers are facing problems, including insufficient adaptation to online learning methods, poor learning quality, imperfect information infrastructure, insufficient learning resources, and so on. Based on the results, the authors propose to explore systematic solutions to guarantee a fair and high-quality development of online teaching. For one thing, the whole education system must ensure the fairness of learning conditions, encourage all-round development, and promote school–home cooperation. For another thing, it is necessary to clarify the connotation of online learning, guarantee the construction of information infrastructure, provide training in information technology and classroom management capabilities, and improve the evaluation system of online learning activities.

This chapter introduces electroencephalography (EEG), a measure of neurophysiological activity, as a critical method for investigating individual and team decision-making and cognition. EEG is a useful tool for expanding the theoretical and research horizons in organizational cognitive neuroscience, with a lower financial cost and higher portability than other neuroimaging methods (e.g., functional magnetic resonance imaging). This chapter briefly reviews past work that has applied cognitive neuroscience methods to investigate cognitive processes and outcomes. The focus is on describing contemporary EEG measures that reflect individual cognition and compare them to complementary measures in the field of psychology and management. The authors discuss how neurobiological measures of cognition relate to and may predict both individual cognitive performance and team cognitive performance (decision-making). This chapter aims to assist scholars in the field of managerial and organizational cognition in understanding the complementarity between psychological and neurophysiological methods, and how they may be combined to develop new hypotheses in the intersection of these research fields.

Scholars have been conducting serious research on the human, organizational, and cultural aspects of mergers and acquisitions (M&A) for 30 years. Yet, over this period, there have only been modest improvements in the M&A success rate. In this chapter, the author examines corporate combinations, describes how human factors contribute to their failure or success, and identifies key research questions whose answers can help to improve the M&A success rate in both financial and human terms. The author proposes research questions for the key phases of a deal, including buying a company and putting companies together. And, reflecting an emerging trend among some frequent acquirers to build an internal competence in M&A execution, the author also proposes research questions for how to accelerate the process of learning from past combinations to better manage future ones.

Two different types of technology-enabled stories that can help expand the notion of narratives are discussed in this chapter. The narratives found in digital storytelling and video games offer new possibilities and advantages for language learners and instructors. They are multimodal, immersive, and authentic; they offer significant motivational benefits and allow for agentive, situated, and participatory learning. Both forms, DST and video games, exemplify new modes of relating meaningful narratives. Media creation and sharing as well as gaming are familiar domains for today's learners. Thus, if these authentic practices are part of the learner's everyday experiences, it makes sense to utilize their potential for educational purposes. As the review of some applications in this chapter indicates, there is an area of convergence that is of particular interest for language learning purposes and may lead us to contemplate a redefinition of these narrative forms. In addition to more traditional narratives, these new and emergent forms can and should be represented in language learning curricula.

Using data from a mid-west public university, this study examines the determinants of students’ repeating the first college-level accounting course. More than 600 students are included in the study. The results show that three factors (cumulative college grade point average, intention to major in accounting, and students’ motivation and determination) are significant in explaining students’ repeating of the course. The study provides evidence that the repeating students are more likely to be the students with prior high school accounting education. The study identifies that repeating is not due to a student failing the course but rather that repeating is more likely to be an individual decision when the student is not satisfied with the grade he/she gets, either because he/she self-screens out of business school, or decides to repeat the course to stay in their business major. Finally, the study shows that there is little evidence of grade improvement when a student repeats the first college-level accounting course.