The Federal Aviation Administration (FAA) Academy is challenged with limited availability of Air Traffic Control (ATC) training technologies for their trainees. Due to the number of trainees and cost of operation of ATC labs, trainees can only practice for limited amounts of time, hindering their learning experience. Embry-Riddle Aeronautical University is tasked with building a lightweight and web-based en route ATC simulator that mimics the FAA en route automation environment, allowing trainees to practice ATC scenarios at their own pace. The proposed simulation technology, namely ATC Scenario Training Technology (ASTT) mimics En Route Automation and Modernization (ERAM) functionalities and provides a web-based practice platform to en route trainees, as well as hints on their performance, without requiring any external human or system actors when running a scenario. This research tackled the issue of how to algorithmically fulfil the role of the pilot, the adjacent sector controllers, and the ATC instructor in a browser-based ATC training environment. Several criteria were identified as requirements for such a system to be implemented on the ASTT server to play the role of the above-mentioned actors. In this paper, we present the web-based ATC training environment by detailing the tool features and its unique feedback capabilities serving as an automated supervisory training platform. Front-end as well as back-end mechanism of ASTT technology will be discussed, highlighting tool's design and operation details.