Computerized adaptive testing (CAT) and multistage testing (MST) have become two of the most popular modes in large-scale computer-based sequential testing. Though most designs of CAT and MST exhibit strength and weakness in recent large-scale implementations, there is no simple answer to the question of which design is better because different modes may fit different practical situations. This article proposes a hybrid adaptive framework to combine both CAT and MST, inspired by an analysis of the history of CAT and MST. The proposed procedure is a design which transitions from a group sequential design to a fully sequential design. This allows for the robustness of MST in early stages, but also shares the advantages of CAT in later stages with fine tuning of the ability estimator once its neighborhood has been identified. Simulation results showed that hybrid designs following our proposed principles provided comparable or even better estimation accuracy and efficiency than standard CAT and MST designs, especially for examinees at the two ends of the ability range.