  As distance learning becomes increasingly important and artificial
intelligence tools continue to advance, automated systems for individual
learning have attracted significant attention. However, the scarcity of
open-source online tools that are capable of providing personalized feedback
has restricted the widespread implementation of research-based feedback
systems. In this work, we present RATsApp, an open-source automated feedback
system (AFS) that incorporates research-based features such as formative
feedback. The system focuses on core STEM competencies such as mathematical
competence, representational competence, and data literacy. It also allows
lecturers to monitor students' progress. We conducted a survey based on the
technology acceptance model (TAM2) among a set of students (N=64). Our findings
confirm the applicability of the TAM2 framework, revealing that factors such as
the relevance of the studies, output quality, and ease of use significantly
influence the perceived usefulness. We also found a linear relation between the
perceived usefulness and the intention to use, which in turn is a significant
predictor of the frequency of use. Moreover, the formative feedback feature of
RATsApp received positive feedback, indicating its potential as an educational
tool. Furthermore, as an open-source platform, RATsApp encourages public
contributions to its ongoing development, fostering a collaborative approach to
improve educational tools.
