128 citations · 237 across the 7 of their papers we have counts for
7 papers
AI-Integrated Scientific Inquiry: A Practice-Centered Vision for Science Education
Arne Bewersdorff, Matias Rojas, Xiaoming Zhai
Artificial intelligence (AI) has become part of scientific inquiry. Scientists use AI to observe and measure phenomena, to identify patterns in data, and to build models. As AI mov…
Simulating Validity: Modal Decoupling in MLLM Generated Feedback on Science Drawings
Arne Bewersdorff, Nejla Yuruk, Xiaoming Zhai
In science education, students frequently construct hand-drawn visual models of scientific phenomena. These drawings rely on a visual structure where information is encoded through…
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…
A Systematic Assessment of OpenAI o1-Preview for Higher Order Thinking in Education
Ehsan Latif, Yifan Zhou, Shuchen Guo +24
As artificial intelligence (AI) continues to advance, it demonstrates capabilities comparable to human intelligence, with significant potential to transform education and workforce…
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…
Multimodality of AI for Education: Towards Artificial General Intelligence
Gyeong-Geon Lee, Lehong Shi, Ehsan Latif +10
This paper presents a comprehensive examination of how multimodal artificial intelligence (AI) approaches are paving the way towards the realization of Artificial General Intellige…
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…