Development of an Artificial Intelligence-Based Adaptive Typing Training System to Improve Accuracy and Speed

Abstract

Mastering the skill of fast and accurate ten-finger typing is a crucial competency in the era of digital transformation. However, conventional, static typing training methods are often ineffective because they fail to adapt the training material to each user's specific weaknesses. This research aims to develop a Reinforcement Learning-based adaptive typing training system capable of dynamically personalizing training material to improve user typing speed and accuracy. The research method used was an experiment implementing the Q-Learning algorithm, in which an intelligent agent determines training material based on the user's error profile to maximize performance improvement. Evaluation was conducted on 50 students divided into experimental and control groups. System performance was analyzed through the convergence of the agent's learning curve and a comparison of pre-test and post-test results. The results showed that the Reinforcement Learning agent successfully learned the optimal training strategy and achieved reward stability in the final stage of training. User testing demonstrated that the adaptive system was able to increase Words Per Minute by 30–68% and significantly improve accuracy compared to static methods. Thus, the Reinforcement Learning approach has proven effective in creating a typing training system that is adaptive, efficient, and tailored to individual needs.