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Updated: Jan 31, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
A generating technique and knowledge representation of multiple-answer problems for learning with solving knowledge
Noriyuki Matsuda1, Hisashi Ogawa2, Tsukasa Hirashima3
11Faculty of Systems Engineering, Wakayama University, 930 Sakaedani, Wakayama, 640-8510 Japan.
Background:
Erroneous answers in multiple-answer problems not only make the correct answer harder to determine but also indicate why the correct choice is suitable and the erroneous one a mistake when compared to the correct answer. However, it is insufficient to simply create erroneous answers for this purpose: explanations of these answers are also required. Preexisting studies examining functions for generating erroneous answers and their explanations based on this approach are abundant. Nevertheless, a major bottleneck has formed in this research body concerning the related specialized knowledge descriptions that are required for the generation function.
Methods:
This paper focuses on the notion that it is easy for teachers skilled in problem solving to express specific problems in written form and amend incomplete knowledge. Furthermore, it examines a method of constructing knowledge while generating and updating knowledge from specific problems.
Result And Conclusion:
The suitability of the proposed method was verified by examining actual knowledge constructed by the research subjects.
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