Teaching and learning physical skills often require one-on-one interaction, making it difficult to scale up, as there are not enough human teachers. Robots offer an attractive alternative. This paper presents TeachingBot, an adaptive robotic system that teaches handwriting to human learners through physical interaction. Robot teaching poses two major challenges: (i) adapting to the individual handwriting style of the learner and (ii) maintaining an engaging learning experience. For the first challenge, TeachingBot uses a probabilistic model to capture the learner's writing style from their writing samples. Drawing on the insight that effective teaching balances standardization with individuality, the system generates a personalized teaching trajectory that aligns with the learner's natural writing. For the second challenge, TeachingBot employs variable impedance control to guide the learner, dynamically adjusting the strength of physical guidance based on the learner's performance. Human-subject experiments with 15 participants demonstrate the effectiveness of TeachingBot, showing clear improvement in learners' handwriting and engagement over baseline methods.