Visualizing Confidence in Entrepreneurship via PBL Using Causal AI: Results of Practical Education Program at Hiroshima University Published
Hootfolio Co., Ltd., in collaboration with NEC, conducted a PBL program using the causal AI 'causal analysis®' at Hiroshima University to teach data-driven decision making (EBPM).
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- 📰 Published: April 2, 2026 at 19:18
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Hootfolio Co., Ltd., in collaboration with NEC Corporation (NEC), implemented an educational program utilizing the causal AI 'causal analysis®' at Hiroshima University. This initiative was conducted as a PBL (Project Based Learning) focused on learning decision-making based on causality rather than mere correlation. Students took on the role of educational designers at the Center for Educational Development, challenging themselves to derive educational measures from actual survey data to boost confidence in entrepreneurship.
### Background: The Importance of EBPM and Causal Thinking in Education
In recent years, the concept of Evidence-Based Policy Making (EBPM) has spread in the field of education, increasing the importance of decision-making based on data rather than experience or impressions. In particular, an analysis that causally grasps 'what produces the results' is considered an essential perspective in improving the quality of educational measures.
The causal AI 'causal analysis®' provided by Hootfolio Co., Ltd. is a no-code solution that can visualize the cause-and-effect relationships hidden in data without requiring specialized knowledge. In this program, students utilized this technology to analyze educational issues using actual survey data, experiencing the process of formulating measures based on scientific evidence.
### Implementation Overview: PBL on the Theme of Entrepreneurship
- Format: In-class PBL (group work, with final presentation)
- Target: Graduate students in the Educational Data Science Program, Hiroshima University
- Theme: As educational designers at the Center for Educational Development, considering education to improve confidence in entrepreneurship
- Objectives:
- Learning scientific decision-making based on causality
- Experiencing the educational problem-solving process using data
- Practically understanding the perspective of EBPM
- Program Content:
1. Basic understanding of causal analysis (learning the difference between correlation and causality, and use cases in education)
2. Causal analysis of student survey data (exploring factors of confidence regarding entrepreneurship)
3. Planning and presentation of educational measures (designing specific measures based on analysis results)
### Background: The Importance of EBPM and Causal Thinking in Education
In recent years, the concept of Evidence-Based Policy Making (EBPM) has spread in the field of education, increasing the importance of decision-making based on data rather than experience or impressions. In particular, an analysis that causally grasps 'what produces the results' is considered an essential perspective in improving the quality of educational measures.
The causal AI 'causal analysis®' provided by Hootfolio Co., Ltd. is a no-code solution that can visualize the cause-and-effect relationships hidden in data without requiring specialized knowledge. In this program, students utilized this technology to analyze educational issues using actual survey data, experiencing the process of formulating measures based on scientific evidence.
### Implementation Overview: PBL on the Theme of Entrepreneurship
- Format: In-class PBL (group work, with final presentation)
- Target: Graduate students in the Educational Data Science Program, Hiroshima University
- Theme: As educational designers at the Center for Educational Development, considering education to improve confidence in entrepreneurship
- Objectives:
- Learning scientific decision-making based on causality
- Experiencing the educational problem-solving process using data
- Practically understanding the perspective of EBPM
- Program Content:
1. Basic understanding of causal analysis (learning the difference between correlation and causality, and use cases in education)
2. Causal analysis of student survey data (exploring factors of confidence regarding entrepreneurship)
3. Planning and presentation of educational measures (designing specific measures based on analysis results)