New Paper in BJET: Empowering reflective writing with large language models

This paper introduces Reflectium, an AI-powered reflective writing assistant that leverages large language models to support students through worked and modelling examples. The study highlights how integrating adaptive, personalized feedback via generative AI can significantly enhance learning outcomes, interaction behavior, and user experience in reflective writing tasks. Read it here!

Authors

• Seyed Parsa Neshaei
• Paola Mejia-Domenzain
• Richard Lee Davis
• Tanja Käser

Full Abstract
Reflective writing is known as a useful method in learning sciences to improve the metacognitive skills of students. However, students struggle to structure their reflections properly, limiting the possible learning gains. Previous works in educational technologies literature have explored the paradigms of learning from worked and modelling examples, but (a) their application to the domain of reflective writing is rare, (b) such methods might not scale properly to large-scale classrooms, and (c) they do not necessarily take the learning needs of each student into account. In this work, we suggest two approaches of integrating AI-enabled support in digital systems designed around learning from worked and modelling examples paradigms, to provide personalized learning and feedback to students using large language models (LLMs). We evaluate Reflectium, our reflective writing assistant, show benefits of integrating AI support into the learning from examples modalities and compare the perception of the users and their interaction behaviour when using each version of our tool. Our work sheds light on the applicability of generative LLMs to different types of providing support using the learning from examples paradigm, in the domain of reflective writing.