AI can not only outperform people in many planning tasks, but also teach them
how to plan better. All prior work was conducted in fully observable
environments, but the real world is only partially observable. To bridge this
gap, we developed the first metareasoning algorithm for discovering
resource-rational strategies for human planning in partially observable
environments. Moreover, we developed an intelligent tutor teaching the
automatically discovered strategy by giving people feedback on how they plan in
increasingly more difficult problems. We showed that our strategy discovery
method is superior to the state-of-the-art and tested our intelligent tutor in
a preregistered training experiment with 330 participants. The experiment
showed that people's intuitive strategies for planning in partially observable
environments are highly suboptimal, but can be substantially improved by
training with our intelligent tutor. This suggests our human-centred tutoring
approach can successfully boost human planning in complex, partially observable
sequential decision problems.

该研究开发了第一个元推理算法来发现资源合理策略，将其应用于人类在部分可观察环境下的规划中，并通过智能教师教授学习所得的策略，以提高人类在复杂、部分可观察的序列决策问题中的规划能力。

利用人工智能改善在大型部分可观测环境中的人类规划

Leveraging AI to improve human planning in large partially observable environments

Artificial Intelligence (AI) is becoming more and more popular as time
passes, allowing to perform tasks that were difficult to do in the past. From
predictions to customization, AI is being used in many areas, not being
educational environments outside this situation. AI is being used in
educational settings to customize contents or to provide personalized feedback
to the students, among others. In this scenario, AI in programming teaching is
something that still has to be explored, since in this area we usually find
assessment tools that allow grading the students work, but we can not find many
tools aimed towards providing feedback to the students in the process of
creating their program. In this work we present an AI based intelligent tutor
that answers students programming questions. The tool has been tested by
university students at the URJC along a whole course. Even if the tool is still
in its preliminary phase, it helped the students with their questions,
providing accurate answers and examples. The students were able to use the
intelligent tutor easily and they thought that it could be a useful tool to use
in other courses.

本研究提出了基于人工智能的编程教学智能助教，它可以回答学生的编程问题并提供准确的答案和示例，受测试的大学生认为这个工具可以成为其他课程中有用的工具。