79 research outputs found
"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
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