Peter Wulff

Assistant Professor

Heidelberg University of Education

Germany

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Peter Wulff is an Assistant Professor at Heidelberg University of Education in Germany. His research focuses on the integration of artificial intelligence in STEM education, particularly in the context of teacher training and educational assessment. Recent articles reflect his interests in climate change education, problem-solving in physics, and the application of natural language processing in the natural sciences.

Articles (16)

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2025

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2025

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2025

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2025

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2025

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2025

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2024

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2024

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2023

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2023

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2023

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2023

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2023

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2023

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2022

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2021

Collaborators (1)

Manfred Stede

Professor

University of Potsdam

GERMANY

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Peter Wulff

Research Interests

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Articles (16)

Künstliche Intelligenz in den Naturwissenschaftsdidaktiken – gekommen, um zu bleiben: Potenziale, Desiderata, Herausforderungen

AI in STEM Teacher Education: Inquiry into Capabilities of an Emerging Technology

Evaluating GPT- and reasoning-based large language models on Physics Olympiad problems: Surpassing human performance and implications for educational assessment

Climate change education: Bibliometric analysis of the Status-Quo and future research paths

AI in STEM Teacher Education: Inquiry into Capabilities of an Emerging Technology

Exploring the sequential structure of students’ physics problem-solving approaches using process mining and sequence analysis

Are science competitions meeting their intentions? a case study on affective and cognitive predictors of success in the Physics Olympiad

Physics language and language use in physics—What do we know and how AI might enhance language-related research and instruction

Exploring new depths: Applying machine learning for the analysis of student argumentation in chemistry

Integrating artificial intelligence-based methods into qualitative research in physics education research: A case for computational grounded theory

Utilizing a Pretrained Language Model (BERT) to Classify Preservice Physics Teachers’ Written Reflections

Network analysis of terms in the natural sciences insights from Wikipedia through natural language processing and network analysis

Correction to: Utilizing a Pretrained Language Model (BERT) to Classify Preservice Physics Teachers’ Written Refections

Enhancing writing analytics in science education research with machine learning and natural language processing—Formative assessment of science and non-science preservice teachers’ written reflections

Bridging the Gap Between Qualitative and Quantitative Assessment in Science Education Research with Machine Learning — A Case for Pretrained Language Models-Based Clustering

Computer-Based Classification of Preservice Physics Teachers’ Written Reflections

Collaborators (1)

Manfred Stede

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