Flow is a mental state achieved by a person performing a task when there is a balance between skill and challenges, thereby evoking focused attention, full involvement, and enjoyment. For education applications, achieving a flow state may accelerate learning, retention and improve learning outcomes. Most current methods for assessing the extent of flow state use a self-reported questionnaire only when participants finish a learning activity. Alternatively, using objective real-time measurement such as electrophysiological techniques to assess the flow state, especially during a learning period, affords the possibility of timely adjustment of challenges to keep up with learners’ growing abilities. Such an adaptive approach may provide a more improved way to enter and sustain flow in learning activities. Previous studies also indicate that gamification is a proven way to induce the flow state, and virtual reality (VR) activities provide learners more immersive experience. The purpose of this preliminary study is to investigate an adaptive approach for assessing flow state during VR learning activities with electrophysiological measurement. This study used a NeuroSky EEG headset with a supervised machine learning algorithm to identify the features of attention, meditation, and eye blinks with flow state while performing VR learning activities. To evaluate the effects of the proposed approach, data collection was through an experiment with 10 UW students by assessing their flow state while playing VR educational games. The first version model trained by the three types of indicators is capable of determining if a learner performing VR learning activities enters the flow state. In turn, the result of this preliminary study serves as a metric for the follow-on research addressing the development of adaptive learning strategies to help each learner enter into a flow state and improve learning outcomes with the sustained flow state.