The main purpose of this paper is to introduce a complete design of a novel system for adaptive intelligent learning system (SAIL), and to innovate on both technical and conceptual fronts by addressing relevant barriers. This research was focused on actualizing a human centered approach through the guidance of experts in the learning and psychological fields. System intelligence was sought through the utilization of automated machine learning technologies to classify learners as well as through information processing at a semantic level. Adaptively was achieved through the constant monitoring of learners' behavior and psychophysiological status, feedback and performance to draw actionable conclusions for both the learners and the system itself. SAIL uses intelligent and interactive technologies in processing individual learner perceptions, preferences and cognitive profiles. In addition, it focuses on a highly configurable architecture to provide a system that can be used in varying training scenarios, so as to produce a tailor-made learning experience, suiting both the needs of the individual learners and the goals of the organizations, thus, improving the professional training process outcomes. Evaluation of both content and the learning system itself were conducted in a sound and statistically relevant way, under the supervision of learning and cognitive experts. SAIL industrial "added value" are quantified and measured, as the industry needs to see a clear connection between competence gaining of employees, business goals, resources and organizational performance. Emphasis is also given on producing fully-reusable system architecture for varying training purposes in the future. [ABSTRACT FROM AUTHOR]