About the Lab

Operationalizable Theories of Human Learning
for Transferable Problem-Solving on Intelligent Tutoring Systems

Problem-Solving Intelligence Lab (PSI Lab / Koike Lab) is part of the Department of Electrical, Electronics and Information Engineering, Faculty of Engineering, Kanagawa University. We explore “How can the ‘compound interest’ of problem-solving—where insights gained from solving one problem prove effective on the next—be made to arise?” through interdisciplinary research that bridges Engineering (AI, Knowledge Engineering, Learning Analytics) and Science (Cognitive Science, Educational Psychology).

The Joy of “Learning”, not just “Education”

Our focus is “Learning” (the learner’s experience), rather than “Education” (the teacher’s logic). How do people understand new concepts, navigate trial and error, and acquire skills? We aim to scientifically elucidate this process and enrich it with technology.

Learning is not just about increasing knowledge. We design “accumulative learning” where the “ways of thinking” and “ways of reflecting” gained through one problem-solving experience live on in the next.

Exploring the “Intersection” of Science and Engineering

Let’s compare humans to “birds” and learning to “flying”. There have been two main approaches:

• Engineering (AI / Knowledge Engineering / Learning Analytics): Building “machines that fly (airplanes)”. Achieving the goal of flight by any means, regardless of whether it mimics a bird.
• Science (Cognitive Science / Educational Psychology): Unraveling “how birds fly”. Faithfully understanding the mechanism of structure and movement.

Our research is neither pure science nor pure engineering. It is an interdisciplinary approach that integrates insights from both to create new value. Understanding “how the bird flies (Cognition)” and designing “tools that help the bird fly higher and further (Technology)”.

We don’t just build convenient apps. We model cognitive science findings from an engineering perspective, and use engineering approaches to deepen our understanding of human cognition. We aim to build foundational theories and frameworks that can be applied across many situations, rather than just developing isolated tools.

Taking this a step further, constructing theories of learning as working systems is our methodology. If a theory is correct, the system works correctly; if it doesn’t work, we can identify where the theory went wrong. This “understanding by building” approach is the foundation of our research.

We carry out this kind of research in the field known as “Intelligent Tutoring Systems (ITS)”.

Lab Stance

Our core values in research:

1. Formalization & Constructive Understanding

Turning “vague understanding” into “explicit explanation” (Formalization). And deepening understanding by not just theorizing, but building working systems (Constructive Understanding). The gap between “knowing” and “doing” is bridged by concrete output.

2. “Compound Interest” of Problem-Solving

We aim for generalizable “wisdom”—knowledge and thinking patterns that are not one-offs but transferable to future situations. We strive for research where one result or tool helps someone in a completely different field.

3. Seeing “Information” in Failure

In learning, mistakes are not to be avoided. They contain vital information: “What did you understand, and where did you get lost?”. The same goes for research. We welcome those who find unexpected results interesting and can derive the next hypothesis from them.

4. Interdisciplinary Crossing

AI, Knowledge Engineering, Learning Analytics, Cognitive Science, Educational Psychology—and anything else that’s needed. We don’t stick to one field; we use and combine whatever tools are necessary. Let’s gather members with different perspectives, play at the boundaries, and create new ideas together.

For specific research projects, please visit the Research page.