Objective. Going adaptive is a major challenge for the field of Brain-Computer Interface (BCI). This entails a machine that optimally articulates inference about the user’s intentions and its own actions. Adaptation can operate over several dimensions which calls for a generic and flexible framework. Approach. We appeal to one of the most comprehensive computational approach to brain (adaptive) functions: the Active Inference (AI) framework. It entails an explicit (probabilistic) model of the user that the machine interacts with, here involved in a P300-spelling task. This takes the form of a discrete input-output state-space model establishing the link between the machine’s (i) observations – a P300 or Error Potential for instance, (ii) representations – of the user intentions to spell or pause, and (iii) actions – to flash, spell or switch-off the application. Main results. Using simulations with real EEG data from 18 subjects, results demonstrate the ability of AI to yield a significant increase in bit rate (17%) over state-of-the-art approaches, such as dynamic stopping. Significance. Thanks to its flexibility, this one model enables to implement optimal (dynamic) stopping but also optimal flashing (i.e. active sampling), automated error correction, and switching off when the user does not look at the screen anymore. Importantly, this approach enables the machine to flexibly arbitrate between all these possible actions. We demonstrate AI as a unifying and generic framework to implement a flexible interaction in a given BCI context.