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As distance learning becomes increasingly important and artificialintelligence tools continue to advance, automated systems for individuallearning have attracted significant attention. However, the scarcity ofopen-source online tools that are capable of providing personalized feedbackhas restricted the widespread implementation of research-based feedbacksystems. In this work, we present RATsApp, an open-source automated feedbacksystem (AFS) that incorporates research-based features such as formativefeedback. The system focuses on core STEM competencies such as mathematicalcompetence, representational competence, and data literacy. It also allowslecturers to monitor students' progress. We conducted a survey based on thetechnology acceptance model (TAM2) among a set of students (N=64). Our findingsconfirm the applicability of the TAM2 framework, revealing that factors such asthe relevance of the studies, output quality, and ease of use significantlyinfluence the perceived usefulness. We also found a linear relation between theperceived usefulness and the intention to use, which in turn is a significantpredictor of the frequency of use. Moreover, the formative feedback feature ofRATsApp received positive feedback, indicating its potential as an educationaltool. Furthermore, as an open-source platform, RATsApp encourages publiccontributions to its ongoing development, fostering a collaborative approach toimprove educational tools.

Lessons Learned from Designing an Open-Source Automated Feedback System for STEM Education