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Over the last three decades, service-learning has become a well-known experiential learning pedagogy in both management education and higher education more broadly. This popularity is observed in the increasing number of peer-reviewed publications on service-learning in management and business education journals, and on management education topics within higher education journals focused on civic engagement and community-based teaching and learning. In this field of study, it is known that service-learning can result in positive outcomes for students, faculty, and community members. In particular, for students, positive results are related to mastery of course content and group process skills like teamwork and communication, leadership, and diversity awareness. Despite the rise in scholarship, service-learning instructors still face several challenges in the area of best practice standards, fostering deep and cohesive partnerships, and managing institutional pressures that disincentivize engaged teaching practices. With constantly evolving challenges in management education, continued research is needed to understand a variety of service-learning facets such as platforms (face-to-face, hybrid, and virtual learning), populations (graduate vs. undergraduate populations and adult vs. traditional college-age learners), measurement (how to assess university-community partnerships and faculty instruction), and which institutional policies and procedures can enable and reward community-engaged teaching and learning approach.

Academic integrity is an interdisciplinary concept that provides the foundation for every aspect and all levels of education. The term evokes strong emotions in teachers, researchers, and students—not least because it is usually associated with negative behaviors. When considering academic integrity, the discussion tends to revolve around cheating, plagiarism, dishonesty, fraud, and other academic malpractice and how best to prevent these behaviors. A more productive approach entails a focus on promoting the positive values of honesty, trust, fairness, respect, responsibility, and courage (International Center for Academic Integrity, 2013) as the intrinsically motivated drivers for ethical academic practice. Academic integrity is much more than “a student issue” and requires commitment from all stakeholders in the academic community, including undergraduate and postgraduate students, teachers, established researchers, senior managers, policymakers, support staff, and administrators.

E-learning expands options for teaching and learning using technology. This nomenclature has been solidly in use for the last ten years. The expansive and ever fertile frontier of e-learning—a term used interchangeably with distance and online learning—has become standard fare as an educational delivery solution designed to enhance knowledge and performance. Many educational institutions, corporate enterprises and other entities are utilizing web-based teaching and learning methodologies to deliver education either partially or wholly online using electronic platforms. The learning value chain, including management and delivery, has created multimodal systems, content, and processes to increase accessibility, measurability, and cost effectiveness by infusing advanced learning techniques, such as adaptive learning or communities of practice, among students, employee groups, and lifelong learners. It is interesting to note that e-learning encapsulates internet based courseware and all other asynchronous and synchronous learning, as well as other capabilities for supporting learning experiences. Student success and advancements in technology are now inextricably linked as a result of higher education institutions embracing and offering e-learning options. The absence of direct instructor guidance makes distance learning particularly difficult for some students. Certain students struggle with the lack of guidance inherent in online learning and the requisite need to work independently. In particular, the lack of high touch strategies in e-learning often leads students to drop or fail courses. While some students struggle to remain engaged in technology-enabled learning, technology is often the vehicle for keeping these same students on task. There are a variety of electronic tools designed to augment online learning and keep online learners on task. Podcasts, for example, can be easily downloaded, then played back on a student’s media player or mobile device at a later date. The student is not tied to a computer, which results in a more comprehensive learning experience. In many cases, e-learning has become a very lucrative and desirable marketplace for higher education institutions. The business case for e-learning is a clarion call for tight integration among business, human resources, and knowledge and performance management. Hence, it is incumbent upon educational institutions to instill approaches that focus on the learner, learning, and improved performance, more so than the tools and technology. Of further importance is the need for higher education institutions to provide stratagems for developing and supporting caring online relationships, individualized student environments, collaboration, communication, and e-learning culture. Ultimately, institutions should measure not only improved business and performance, but also improved student online learning aptitudes (more self-motivated, self-directed, and self-assessed learning).

Artificial intelligence (AI), commonly defined as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation,” can be classified into analytical, human-inspired, and humanized AI depending upon its application of cognitive, emotional, and social intelligence. AI’s foundations took place in the 1950s. A sequence of vicissitudes of funding, interest in, and support for AI followed subsequently. In 2015 AlphaGo, Google’s AI-driven system, won against the human grandmaster in the highly complex board game Go. This is considered one of the most significant milestones in the development of AI and marks the starting of a new period, enabling several AI innovations in a variety of sectors and industries. Higher education, the fashion industry, and the arts serve as illustrations of areas wherein ample innovation based on AI occurs. Using these domains, various angles of innovation in AI can be presented and decrypted. AI innovation in higher education, for example, indicates that at some point, AI-powered robots might take over the role of human teachers. For the moment, however, AI in academia is solely used to support human beings, not to replace them. The apparel industry, specifically fast fashion—one of the planet’s biggest polluters—shows how innovation in AI can help the sector move toward sustainability and eco-responsibility through, among other ways, improved forecasting, increased customer satisfaction, and more efficient supply chain management. An analysis of AI-driven novelty in the arts, notably in museums, shows that developing highly innovative, AI-based solutions might be a necessity for the survival of a strongly declining cultural sector. These examples all show the role AI already plays in these sectors and its likely importance in their respective futures. While AI applications imply many improvements for academia, the apparel industry, and the arts, it should come as no surprise that it also has several drawbacks. Enforcing laws and regulations concerning AI is critical in order to avoid its adverse effects. Ethics and the ethical behavior of managers and leaders in various sectors and industries is likewise crucial. Education will play an additional significant role in helping AI positively influence economies and societies worldwide. Finally, international entente (i.e., the cooperation of the world’s biggest economies and nations) must take place to ensure AI’s benefit to humanity and civilization. Therefore, these challenges and areas (i.e., enforcement, ethics, education, and entente) can be summarized as the four summons of AI.