Learning fine psychomotor skills such as handwriting is a tedious endeavour which requires close supervision of the teacher to master. However, the increasing number of students in classes means less time a teacher can allocate for each student. This adversely affects the development of handwriting in students. Sensor-based technologies can help address this problem, as they are capable of providing feedback to the student whilst the teacher is not present during the student's writing. While there are multiple sensor-based applications to date for handwriting practice, such applications provide feedback in only for simple tracing over practice tasks. In this paper, we present a conceptual methodology using AI and sensors, for providing feedback in non-tracking tasks that do not have a single correct solution and allow larger variations.@en

It takes considerable time, experience, and direct assistance from teachers to become a skilled writer. Handwriting fluency is one of the predictors of writing quality among students. However, students do not receive enough teacher supervision as a beginner to develop handwriting fluency in a proper manner. The ``Calligraphy tutor'' presented in this paper, is an application developed to assist teachers to help students learn proper handwriting fluency skills. Calligraphy tutor is designed to support deliberate practice of handwriting, in which teachers play the central role. To reduce workload of teachers, Calligraphy tutor automates repetitive actions such as providing mundane real-time feedback, while also collecting performance data from students, allowing students to practice without the presence of a teacher. The collected performance data is used by teachers to further personalise students' training.@en