128 citations · 214 across the 5 of their papers we have counts for
5 papers
Towards Adaptive Feedback with AI: Comparing the Feedback Quality of LLMs and Teachers on Experimentation Protocols
Kathrin Seßler, Arne Bewersdorff, Claudia Nerdel +1
Effective feedback is essential for fostering students' success in scientific inquiry. With advancements in artificial intelligence, large language models (LLMs) offer new possibil…
Can AI grade your essays? A comparative analysis of large language models and teacher ratings in multidimensional essay scoring
Kathrin Seßler, Maurice Fürstenberg, Babette Bühler +1
The manual assessment and grading of student writing is a time-consuming yet critical task for teachers. Recent developments in generative AI, such as large language models, offer…
Benchmarking Large Language Models for Math Reasoning Tasks
Kathrin Seßler, Yao Rong, Emek Gözlüklü +1
The use of Large Language Models (LLMs) in mathematical reasoning has become a cornerstone of related research, demonstrating the intelligence of these models and enabling potentia…
Taking the Next Step with Generative Artificial Intelligence: The Transformative Role of Multimodal Large Language Models in Science Education
Arne Bewersdorff, Christian Hartmann, Marie Hornberger +6
The integration of Artificial Intelligence (AI), particularly Large Language Model (LLM)-based systems, in education has shown promise in enhancing teaching and learning experience…
Assessing Student Errors in Experimentation Using Artificial Intelligence and Large Language Models: A Comparative Study with Human Raters
Arne Bewersdorff, Kathrin Seßler, Armin Baur +2
Identifying logical errors in complex, incomplete or even contradictory and overall heterogeneous data like students' experimentation protocols is challenging. Recognizing the limi…