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Although game‐based learning has been increasingly promoted in education, there is a need to adapt game content to individual needs for personalized learning. Procedural content generation (PCG) offers a solution for difficulty in developing game contents automatically by algorithmic means as it can generate individually customizable game contents applicable to various objectives. In this paper, we advanced a data‐driven PCG approach benefiting from a genetic algorithm and support vector machines to automatically generate educational‐game contents tailored to individuals' abilities. In contrast to other content generation approaches, the proposed method is not dependent on designer's intuition in applying game contents to fit a player's abilities. We assessed this data‐driven PCG approach at length and showed its effectiveness by conducting an empirical study of children who played an educational language‐learning game to cultivate early English‐reading skills. To affirm the efficacy of our proposed method, we evaluated the data‐driven approach against a heuristic‐based approach. Our results clearly demonstrated two things. First, users realized greater performance gains from playing contents tailored to their abilities compared with playing uncustomized game contents. Second, this data‐driven approach was more effective in generating contents closely matching a specific player‐performance target than the heuristic‐based approach. Lay Description: What is already known about this topic Educational games are mostly designed with a fixed task‐progression difficulty. Given the current broad diversity of player background, preferences, and motivations, it is typically difficult to achieve a dynamic difficulty adaptation with any single and fixed progression.Procedural content generation provides a solution for this difficulty by developing game contents automatically through algorithmic means with or without the involvement of a human designer. What this paper adds A data‐driven approach is employed to refine the fitness function for content evaluation and the prediction of players' capabilities.Unlike previous approaches that have predominantly steered the generation process through designer‐defined goals or heuristics, the proposed approach does not depend on the designer intuition in aligning contents with fitness. Implications for practice and/or policy The proposed framework can be applied to other educational games.The proposed approach offers users a different experience with every new game to enhance the game replayability. [ABSTRACT FROM AUTHOR]