Modularizing Educational LLM-Agency for Fostering Responsible Learning Assistance
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arXiv
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| Format: | Preprint |
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2026
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| author | Gabelmann, Julius Jahn, Felix Baum, Kevin van Rossum, Sophie Wuenscher, Emely Gros, Timo P. Wolf, Verena |
| author_facet | Gabelmann, Julius Jahn, Felix Baum, Kevin van Rossum, Sophie Wuenscher, Emely Gros, Timo P. Wolf, Verena |
| contents | The widespread adoption of AI chatbots in education will drastically change learning, making responsible deployment a critical concern. While large language models (LLMs) might have access to sources discussing insights from educational sciences, they are not particularly inclined to adhere to pedagogical concepts, risking negative effects on the learning process, such as a loss of transfer capabilities, critical thinking, or creativity. In this paper, we introduce an agentic AI chatbot architecture assisting students with exercise solving, specifically designed to contribute to more responsible AI use in education. We base our conceptual development on the identification of several desiderata for responsible LLM-based educational systems, argue for the structural shortcomings inherent in monolithic, out-of-the-box solutions, and instead suggest modularizing the agentic architecture. We propose specific modules for different stages of exercise solving, enabling incorporation of targeted pedagogical advice, guiding students through the learning process in a more controllable, transparent, and overseeable manner. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_30187 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Modularizing Educational LLM-Agency for Fostering Responsible Learning Assistance Gabelmann, Julius Jahn, Felix Baum, Kevin van Rossum, Sophie Wuenscher, Emely Gros, Timo P. Wolf, Verena Artificial Intelligence Computers and Society The widespread adoption of AI chatbots in education will drastically change learning, making responsible deployment a critical concern. While large language models (LLMs) might have access to sources discussing insights from educational sciences, they are not particularly inclined to adhere to pedagogical concepts, risking negative effects on the learning process, such as a loss of transfer capabilities, critical thinking, or creativity. In this paper, we introduce an agentic AI chatbot architecture assisting students with exercise solving, specifically designed to contribute to more responsible AI use in education. We base our conceptual development on the identification of several desiderata for responsible LLM-based educational systems, argue for the structural shortcomings inherent in monolithic, out-of-the-box solutions, and instead suggest modularizing the agentic architecture. We propose specific modules for different stages of exercise solving, enabling incorporation of targeted pedagogical advice, guiding students through the learning process in a more controllable, transparent, and overseeable manner. |
| title | Modularizing Educational LLM-Agency for Fostering Responsible Learning Assistance |
| topic | Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2605.30187 |