To overcome the effort of manually assessing structural induction proofs provided by students, we aim to provide a scalable, fair, and comprehensible model-driven approach to assess students’ proofs automatically. To this end, we developed a fair assessment schema for structural induction proofs and designed a corresponding metamodel. Employing DSLs for the task and the solutions, the model-driven tool - Automatic Structural Induction Assessor - (ASIA) automatically awards points to a given solution and provides helpful feedback. Additionally, our solution can mitigate typical tricks students might employ to receive unwarranted points. To evaluate our approach in practice, we employed ASIA in two undergraduate computerscience courses (with around 60 students each). We found that the automatic assessment provides a fair assessment, and requires limited manual intervention.