
Artificial intelligence (AI) is a demanding and important course for computer science education in the universities and open online courses (MOOCs). It includes various introductory and specialized courses for artificial intelligence like knowledge representation, machine learning, reasoning under uncertainty, natural language processing, robotics, and perception of computer vision, etc. We observed that mostly AI courses focus on the Computer Science (CS)-centric approach and lacks core explanation from their roots including philosophy, neuroscience, psychology, cognitive science, linguistics, economics, social science, etc. In this paper, we propose to engage the interdisciplinary approach along with CS-centric approach for teaching AI that includes the disciplines that have been established to tackle the age-old problem of understanding the science of thinking.
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 9 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
