Technology-Supported Feedback in Learning Spaces: An Evidence-Based Overview for University Teaching

In an era of digitation, automated and AI-generated feedback is gaining significance. To optimally design such computer-based feedback, it is essential to draw on current psychological research findings. This evidence-based article examines the design of effective feedback for digital learning environments in higher education contexts. Through a systematic review, it focuses on the prerequisites for the effectiveness of computer-based (automated and AI-generated) feedback. The article discusses the factors influencing feedback effectiveness in the digital space based on a current theoretical framework (MISCA; Panadero & Lipnevich, 2022). It explores the role of the feedback message, its implementation, the student, the context of the feedback situation, and the agent (provider/source). The evidence suggests that educators should not only take informed decisions regarding the content of the feedback message but also consider possible affective-motivational and metacognitive impacts alongside the cognitive aims of the feedback to foster student learning. Educators should aim to create optimal contextual conditions for beneficial learning opportunities through feedback. Moreover, learners should be addressed as proactive participants with the aim for them to actively engage in the feedback process. Especially when using AI, it is crucial to ensure that critical, unbiased reflection on feedback messages is a key factor for the successful implementation of feedback in digital learning environments. For the higher education context, this article provides empirically-informed guidance for the systematic planning of feedback situations.