"Teach AI How to Code": Using Large Language Models as Teachable Agents for Programming Education

This work investigates large language models (LLMs) as teachable agents for learning by teaching (LBT). LBT with teachable agents helps learners identify their knowledge gaps and discover new knowledge. However, teachable agents require expensive programming of subject-specific knowledge. While LLMs as teachable agents can reduce the cost, LLMs' over-competence as tutees discourages learners from teaching. We propose a prompting pipeline that restrains LLMs' competence and makes them initiate "why" and "how" questions for effective knowledge-building. We combined these techniques into TeachYou, an LBT environment for algorithm learning, and AlgoBo, an LLM-based tutee chatbot that can simulate misconceptions and unawareness prescribed in its knowledge state. Our technical evaluation confirmed that our prompting pipeline can effectively configure AlgoBo's problem-solving performance. Through a between-subject study with 40 algorithm novices, we also observed that AlgoBo's questions led to knowledge-dense conversations (effect size=0.73). Lastly, we discuss design implications, cost-efficiency, and personalization of LLM-based teachable agents

Magnon-drag thermopower and Nernst coefficient in Fe, Co, and Ni

Magnon-drag is shown to dominate the thermopower of elemental Fe from 2 to 80 K and of elemental Co from 150 to 600 K; it is also shown to contribute to the thermopower of elemental Ni from 50 to 500 K. Two theoretical models are presented for magnon-drag thermopower. One is a hydrodynamic theory based purely on non-relativistic, Galilean, spin-preserving electron-magnon scattering. The second is based on spin-motive forces, where the thermopower results from the electric current pumped by the dynamic magnetization associated with a magnon heat flux. In spite of their very different microscopic origins, the two give similar predictions for pure metals at low temperature, allowing us to semi-quantitatively explain the observed thermopower of elemental Fe and Co without adjustable parameters. We also find that magnon-drag may contribute to the thermopower of Ni. A spin-mixing model is presented that describes the magnon-drag contribution to the Anomalous Nernst Effect in Fe, again enabling a semi-quantitative match to the experimental data without fitting parameters. Our work suggests that particle non-conserving processes may play an important role in other types of drag phenomena, and also gives a predicative theory for improving metals as thermoelectric materials.Comment: main text plus 7 figures; accepted in PRB September 201

열화학 물 분해 반응 기반 그린수소 생산기술

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Thermoelectric energy conversion utilizing spin degree of freedom

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Improving Energy Conversion Efficiency of Spin-Seebeck Devices through Selective Scattering of Phonons

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Thermoelectric Energy Conversion Utilizing Spin Degree of Freedom

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Lowering the Reduction Temperature of Two-step Thermochemical Water Splitting

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