Smart tutoring systems (STSs) are intelligent computer tutors and typically have an expert model, student model, instructional module, and intelligent interface. Such systems are based on the artificial intelligence (AI) methodologies, theories and reasoning techniques. Hypotheses derived from these theories can inform curriculum, pedagogy, and potential roles for computers in education. STSs can adjust its tutorial to the student’s knowledge, experience, strengths, and weaknesses. It may even be able to carry on a natural language dialogue. In addition, automatic generation of exercises and tests is an important feature of STS. This paper presents a study of the technical issues of the recent techniques and intelligent authoring tools used in the designing of the STSs, namely; case-based reasoning (CBR) and ontological engineering. Moreover, the paper discusses our proposed model for STS based on CBR methodology. It is aiming to find out the efficient techniques and smart trends around producing STSs, the motivation behind AI in education research, emerging standards and the possible research gaps. The obtained results can help researchers, knowledge engineers and digital learning practitioners in software domain who want to be aware of new trends about developing robust smart tutoring and learning systems.