Abstract

In times of disruption, artificial intelligence (AI), particularly the ubiquitous machine learning (ML), is being rapidly integrated into online working environments in academia and industry. Primarily, these transformative technologies can help automate and perform continuous routine tasks using speed, efficiency, and effectiveness to gather and analyze massive amounts of data and information. This research illustrates ways in which ML can benefit learners and organizations—by making learners and workers work smarter and, more importantly, improve their ubiquitous learning environment anytime, anyplace. Humans can learn while working and studying by integrating both reinforcement learning and supervised learning paradigms and by augmenting, supplementing, or complementing ML with workers’ humanness, skills, creativity, emotions, passions, and tacit knowledge. Furthermore, ML can track the humans learning process by learning from its experience—observing and analyzing the knowledge acquisition process, progress, and difficulties. Also, ML algorithms and agents can suggest new learning paths and approaches. In addition to exploring if and how ML can enhance human learning, this article investigates how it can assist learning without impinging on natural human and social learning development. Rather than just substituting or supplementing the human recall, ML can integrate and enhance work performance by minimizing—instead of supplanting—tedious tasks with automation and AI.