One of the main challenges in tapping the full potential of modern educational software is to devise mechanisms to automatically analyze and adaptively support students' problem solving and learning. A number of such approaches have been developed to teach argumentation skills in domains as diverse as science, the Law, and ethics. Yet, imbuing educational software with effective intelligent tutoring functions requires considerable time and effort. We present a highly configurable software framework, CASE ("Configurable Argumentation Support Engine"), designed to reduce effort and development costs considerably when building tutorial agents for graphical argumentation learning systems. CASE detects pedagogically relevant patterns in argument diagrams and provides feedback and hints in response. A wide variety of patterns are supported, including ones sensitive to students' understanding of the domain, problem-solving processes, and collaboration processes. Teachers and researchers can configure the behavior of tutorial agents on three levels: patterns, tutorial actions, and tutorial strategies. The paper discusses design concerns, the architecture, and the configuration mechanisms of CASE. As a proof of concept, four showcases are presented each showing different aspects of CASE and thus demonstrating the flexibility and breadth of applicability of the CASE approach in supporting single-user and collaborative scenarios across different argumentation domains.