A key factor for success in collaborative learning environments is transactivity. This process, whereby learners engage with and build upon the ideas of others, is crucial for learning outcomes. In today’s digital learning environments, AI-powered speech and text analysis can be used to measure the extent to which learners interact with one another.
In a recent randomized field study involving 252 student teachers, a research team examined how different forms of automated feedback influence the transactivity in online discussions. The study compared basic feedback (BAF), consisting of general information and a performance summary, with highly informative feedback (HIF), consisting of highly personalized, detailed feedback on various aspects of transavtivity.
The results were surprising: Although the participants rated the personalized feedback significantly more positively, it did not lead to any measurable improvements in their actual discussion behavior compared to the standard feedback. The study therefore reveals a noticeable mismatch between the perceived and actual effectiveness of feedback.
These findings show that positive user feedback alone is not a reliable indicator of whether an intervention actually leads to improved learning processes. This discovery is significant for the future developments of AI-supported learning systems.
Menzel-Wuttig, L., Weidlich, J., Mendzheritskaya, J., Winter, M., Drachsler, H. (2027). This is How Students Think About and Act Upon Personalized, Automated Feedback on Transactivity. In: Weidlich, J., et al. Mindful TEL: Learning Technologies Shaped with Intention. EC-TEL 2026. Lecture Notes in Computer Science, vol 16905. Springer, Cham. https://doi.org/10.1007/978-3-032-37979-5_29