Good teachers always tailor their explanations to the learners. Cognitive
scientists model this process under the rationality principle: teachers try to
maximise the learner's utility while minimising teaching costs. To this end,
human teachers seem to build mental models of the learner's internal state, a
capacity known as Theory of Mind (ToM). Inspired by cognitive science, we build
on Bayesian ToM mechanisms to design teacher agents that, like humans, tailor
their teaching strategies to the learners. Our ToM-equipped teachers construct
models of learners' internal states from observations and leverage them to
select demonstrations that maximise the learners' rewards while minimising
teaching costs. Our experiments in simulated environments demonstrate that
learners taught this way are more efficient than those taught in a
learner-agnostic way. This effect gets stronger when the teacher's model of the
learner better aligns with the actual learner's state, either using a more
accurate prior or after accumulating observations of the learner's behaviour.
This work is a first step towards social machines that teach us and each other,
see this https URL

教师应根据学习者的需求调整他们的解释，借鉴了认知科学的思想，我们利用贝叶斯心智理论构建教师代理来优化教学策略，从而提高学习效率。

基于效用的自适应教学策略：贝叶斯心智理论

Utility-based Adaptive Teaching Strategies using Bayesian Theory of Mind

Conversational tutoring systems (CTSs) aim to help students master
educational material with natural language interaction in the form of a dialog.
CTSs have become a key pillar in educational data mining research. A key
challenge in CTSs is to engage the student in the conversation while exposing
them to a diverse set of teaching strategies, akin to a human teacher, thereby,
helping them learn in the process. Different from previous work that generates
responses given the strategies as input, we propose to jointly predict teaching
strategies and generate tutor responses accordingly, which fits a more
realistic application scenario. We benchmark several competitive models on
three dialog tutoring datasets and propose a unified framework that combines
teaching response generation and pedagogical strategy prediction, where a
self-distillation mechanism is adopted to guide the teaching strategy learning
and facilitate tutor response generation. Our experiments and analyses shed
light on how teaching strategies affect dialog tutoring.

本文提出了一种在教学策略预测和生成辅导响应之间进行联合预测的方法，并选择了若干个对话辅导数据集进行基准测试，最终提出了一种统一框架，该框架将教学响应生成和学术策略预测相结合，并采用了自身蒸馏机制来指导教学策略学习和促进辅导响应生成。通过实验和分析，揭示了教学策略对话式辅导的影响。

教学前策略规划：具备自我精华提炼技术的对话式辅导系统

Strategize Before Teaching: A Conversational Tutoring System with Pedagogy Self-Distillation

A major challenge in the field of education is providing review schedules
that present learned items at appropriate intervals to each student so that
memory is retained over time. In recent years, attempts have been made to
formulate item reviews as sequential decision-making problems to realize
adaptive instruction based on the knowledge state of students. It has been
reported previously that reinforcement learning can help realize mathematical
models of students learning strategies to maintain a high memory rate. However,
optimization using reinforcement learning requires a large number of
interactions, and thus it cannot be applied directly to actual students. In
this study, we propose a framework for optimizing teaching strategies by
constructing a virtual model of the student while minimizing the interaction
with the actual teaching target. In addition, we conducted an experiment
considering actual instructions using the mathematical model and confirmed that
the model performance is comparable to that of conventional teaching methods.
Our framework can directly substitute mathematical models used in experiments
with human students, and our results can serve as a buffer between theoretical
instructional optimization and practical applications in e-learning systems.

该研究提出了通过构建学生的虚拟模型来实现教学策略优化的框架，并在实验中使用该数学模型进行了教学，结果表明其性能与传统教学方法相当。该框架可作为理论教学优化和电子学习系统实际应用之间的缓冲。