This paper presents a new resource for automatically assessing text difficulty in the context of Italian as a second or foreign language learning and teaching. It is called MALT-IT2, and it automatically classifies inputted texts according to the CEFR level they are more likely to belong to. After an introduction to the field of automatic text difficulty assessment, and an overview of previous related work, we describe the rationale of the project, the corpus and computational system it is based on. Experiments were conducted in order to investigate the reliability of the system. The results show that the system is able to obtain a good prediction accuracy, while a further analysis was conducted in order to identify the categories of features which mostly influenced the predictions.