This study proposes the design and application of wearable gloves that can recognize sign language expressions from input images via long short-term memory (LSTM) network models and can learn sign language through finger movement generation and vibration motor feedback. It is difficult for nondisabled people who do not know sign language to express sign language accurately. Therefore, we suggest the use of wearable gloves for sign language education to help nondisabled people learn and accurately express sign language. The wearable glove consists of a direct current motor, a link (finger exoskeleton) that can generate finger movements, and a flexible sensor that recognizes the degree of finger bending. When the coordinates of the hand move in the input image, the sign language motion is fed back through the vibration motor attached to the wrist. The proposed wearable glove can learn 20 Korean sign language words, and the data used for learning are configured to represent the joint coordinates and joint angles of both the hands and body for these 20 sign language words. Prototypes were produced based on the design, and it was confirmed that the angle of each finger could be adjusted. Through experiments, a sign language recognition model was selected, and the validity of the proposed method was confirmed by comparing the generated learning results with the data sequence. Finally, we compared and verified the accuracy and learning loss using a recurrent neural network and confirmed that the test results of the LSTM model showed an accuracy of 85%.