Formative Writing Assessment: Automated Feedback Using Artificial Intelligence

FORMAT – Formative Writing Assessment: Automated Feedback Using Artificial Intelligence

The BMBF-funded junior research group FORMAT investigates how artificial intelligence can be used in the classroom to enhance students' writing performance.

Project data

Research linesResearch Line Domain-specific learning in kindergarten and school
DepartmentsEducational Research and Educational Psychology
FundingBundesministerium für Bildung und Forschung (6/1/20215/31/2026)
Current statusrunning
IPN participantsDr. Jennifer Meyer (Project lead), Lucas Wilhelm Liebenow
External partners

IPN Leibniz-Institut für die Pädagogik der Naturwissenschaften und Mathematik (Lead), Universität Hildesheim, Abteilung Angewandte Erziehungswissenschaft

The BMBF-funded junior research group FORMAT is a cooperation project between the IPN Kiel and the University of Hildesheim. In the project, we aim to investigate how the automated evaluation of texts using artificial intelligence can be applied in the classroom to promote students’ learning and performance. Based on the automated assessment, we will provide students with automated feedback on their writing. We will focus on how these technologies can be useful in German and English as a foreign language classrooms.

The project aims to help more students receive feedback on their written performance, to support teachers in assessing complex performances of students in less time, and to explore design principles of adaptive feedback. We will focus on fostering learning outcomes and motivational aspects. Furthermore, we investigate the contextual conditions of feedback effectiveness in order to provide students with different learning prerequisites with the best possible learning opportunities.

To address these research questions, we can capitalize on annotated text corpora from previous projects, including texts from lower secondary and upper secondary school students. In cooperation with the Center of Advanced Technology for Assisted Learning and Predictive Analytics (CATALPA) at FernUniversität in Hagen, machine learning algorithms will be trained on these text corpora, each containing more than 1000 texts, allowing us to provide automated feedback to the students. The project duration is five years (2022-2026).

Project leader: Dr. Jennifer Meyer

Group members: Lucas Liebenow

Cooperation partner: University of Hildesheim, Project leader: Prof. Dr. Johanna Fleckenstein