Papers by Tanja Käser
Evaluating Answer Leakage Robustness of LLM Tutors against Adversarial Student Attacks (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are increasingly used in education, yet their default usefulness conflicts with pedagogical principles. |
| Approach: | They propose an adversarial student agent that they fine-tune to jailbreak LLM-based tutors and propose a benchmark to evaluate tutor robustness. |
| Outcome: | The proposed model fine-tunes to jailbreak LLM-based tutors, and shows that they perform well under adversarial student attacks. |
Let Me Teach You: Pedagogical Foundations of Feedback for Language Models (2024.emnlp-main)
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| Challenge: | Natural Language Feedback (NLF) is an increasingly popular mechanism for aligning Large Language Models to human preferences. |
| Approach: | They propose a feedback framework for Large Language Models that outlines various characteristics of the feedback space and a taxonomy based on these variables. |
| Outcome: | The proposed framework provides a general mapping of the feedback space and provides examples for mapping to future research. |
Unraveling Downstream Gender Bias from Large Language Models: A Study on AI Educational Writing Assistance (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are increasingly utilized in educational tasks such as providing writing suggestions to students. |
| Approach: | They conduct a large-scale user study with 231 students writing business case peer reviews in german. |
| Outcome: | The proposed model does not carry bias in the feedback loops of the students . |
Bias at a Second Glance: A Deep Dive into Bias for German Educational Peer-Review Data Modeling (2022.coling-1)
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| Challenge: | Existing studies have highlighted a variety of biases in pre-trained language models . however, these studies focus on fine-grained analysis of educational corpora and text that is not English . |
| Approach: | They analyze bias across text and through multiple architectures on a corpus of 9,165 German peer-reviews collected from university students over five years. |
| Outcome: | The proposed dataset shows that pre-trained language models exhibit conceptual, racial, and gender biases. |
SCRIBE: Structured Chain Reasoning for Interactive Behaviour Explanations using Tool Calling (2025.emnlp-main)
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| Challenge: | Language models can be used to provide personalized feedback in educational settings, but they face privacy concerns, limited computational resources, and the need for pedagogically valid responses. |
| Approach: | They propose a framework for multi-hop, tool-augmented reasoning to generate valid responses to student questions about feedback reports. |
| Outcome: | The proposed framework can generate valid responses to student questions about feedback reports using domain-specific tools and self-reflective inference pipelines. |