- The LEAF System
What is the LEAF System?
- BookRoll
BookRoll: Digital Teaching Material Distribution System
- LogPalette
LogPalette: Learning Record Analysis Tool
- Jp
What is LogPalette?
The AI Recommendation System recommends the best and the most optimized questions for each learner to work on the assignment satisfactorily.
AI systems already exist that recommend exercises and teaching materials for overcoming challenges.
However, it is impossible to motivate learners and enhance the learning effect unless they are convinced why they are studying a particular question or material. Our project aims to support learning by an AI that learned how to explain things from human beings and to clarify the learner’s thought process by verbalizing the learner’s thought process. In addition, the AI visualizes the learner’s solution process, where the AI learns penstroke data and text data from self-explanatory learning with a handwritten pen, correct/ incorrect answer data for questions, and various learning data. By doing so, we are researching and developing EXAIT (Educational Explainable AI Tools: EXAIT), an explanation generation engine that allows learners to solve problems with more conviction.
If learners are not satisfied with the data analyzed by AI, it is difficult to motivate them to take the initiative.
The “Explainable AI” will derive more effective analytical data for learning and teaching so that working on the assignments makes more sense to the learners and teachers can understand students’ stumbling difficulties and provide appropriate guidance.
When the AI analyzes the learning history and recommends questions to be solved, it is better to explain the reason for the recommendation, which will make sense to the learner and increase his/her motivation.
Learning with EXAIT is conducted in the following steps: (1) analyze, (2) recommend, (3) explain, (4) solve, (5) explain. By repeating this cycle, learners can expect to learn continuously with conviction and improve their understanding.
A knowledge map allows visualization of knowledge acquisition. It shows the connections between knowledge as a network with mode and links and allows for the recommendation of questions based on the structure of that knowledge.
