As mobile computing becomes central to digital interaction, researchers have turned their attention to adaptive authentication for its real-time, context- and behavior-aware verification capabilities. However, many implementations remain fragmented, inconsistently apply intelligent techniques, and fall short of user expectations. In this Systematization of Knowledge (SoK), we analyze 41 peer-reviewed studies since 2011 that focus on adaptive authentication in mobile environments. Our analysis spans seven dimensions: privacy and security models, interaction modalities, user behavior, risk perception, implementation challenges, usability needs, and machine learning frameworks. Our findings reveal a strong reliance on machine learning ( \(64.3\%\) ), especially for continuous authentication ( \(61.9\%\) ) and unauthorized access prevention ( \(54.8\%\) ). AI-driven approaches such as anomaly detection ( \(57.1\%\) ) and spatio-temporal analysis ( \(52.4\%\) ) increasingly shape the interaction landscape, alongside growing use of sensor-based and location-aware models.