Teams can be depicted in many organizations and is becoming increasingly used for effective and efficient productivity. Teams work as the collection of individuals that have different skills to complement each other rather than compete, their driving force is to achieve a common goal by inculcating strategical approaches of which they are held accountable. This, helps in maximizing general output, however the need of its incorporation in learning. This study presents an overview of Intelligent Tutoring System and its impact generally on learning. A review of previous works done on this area was also inculcated in this study highlighting their importance and relevance. Intelligent Tutoring System (ITS), been a machine intelligence learning paradigm. This research explores and unveils what an intelligent tutoring system is all about and its role in learning. The work also covers how intelligent tutoring system has enhanced learner's skill and performance. In conclusion, the massive effects of intelligent tutoring system on the learning process are acknowledged.

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Driving is a complex and dynamic task requiring drivers not only to make accurate perceptions and cognitions about the information on the driver’s driving skill but also to process this information at a high speed. This paper compared three major image processing/machine learning algorithms viz; Single Shot Multibox Detection (SSD), Convolutional Neural Networks (CNN), and support vector machine (SVM) to find the fastest and most efficient of the three with regards to the dataset from driving events (braking, speeding and safe driving) collected from Nigeria. The results analyzed showed that in an identical testing environment, Support Vector Machine outperformed Single Shot Detection and Convolutional Neural Networks.

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