Presentation Information
[WS-D-01]Visualizing the Diversity and Transformations of Students: BEVI-ChatGPT
*Hajime Nishitani1, *Yoshinobu Ohnishi2, *Mitsuyuki Ichimura5, *Hiroyuki Takagi3, *Mina Mizumatsu4, *Eri Nakamura2, *Meng Yun6, *Yuko Kobayakawa4 (1. Hiroshima University / Soka University, 2. Chiba University, 3. Tamagawa University, 4. Toyo University, 5. Yokohama National University, 6. Niigata University)
Keywords:
Visualization of Diversity,AI Feedback (BEVI × ChatGPT) –,Student-Faculty Comparison,DEI (Diversity,Equity,Inclusion),Data-Driven Reflection,Application to Educational Practice,Structured and Objective Evaluation
Background and Objectives
In recent years, international education programs—such as study abroad and COIL (Collaborative Online International Learning)—have been increasingly expected not only to demonstrate implementation outcomes (Outputs) but also to provide evidence of student development and transformation (Outcomes) through empirical assessment and measurement.
A significant shift is seen in the 2024 Inter-University Exchange Project guidelines, which explicitly require institutions to “assess and measure” student learning outcomes. This represents a notable departure from previous evaluation methods, which typically relied on post-program satisfaction surveys.
Launched in 2021, the Internationalization Promotion Forum project by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) aims to facilitate cross-institutional sharing of effective practices in international education. As part of this initiative, the adoption of BEVI (Beliefs, Events, and Values Inventory) has been actively promoted as an objective assessment tool. As of today, more than 50 universities in Japan have implemented BEVI, enabling not only internal satisfaction surveys but also standardized measurement and the comparison and sharing of results across institutions.
Workshop Significance
This workshop aims to provide an overview of BEVI as an objective psychometric tool, introduce its theoretical foundations, and explore practical applications in educational settings. Through shared dialogue, participants will deepen their understanding of how BEVI data can inform and enhance teaching and learning.
Participants will be asked to complete the BEVI survey in advance and will also experience personalized feedback through BEVI-AI. Unlike generic AI systems, BEVI-AI generates responses based on individual psychological assessment scores, offering a unique and highly tailored reflection of each participant’s values and assumptions. This allows faculty and staff to gain insights into their own worldviews and to explore how those perspectives intersect or differ from those of students and colleagues—ultimately supporting more intentional and inclusive educational practices.
We also welcome comparative discussion with other forms of objective data or assessments where applicable.
Tentative Workshop Agenda
Plenary Session: Data-Informed Introduction
Overview of value orientations related to DEI (Diversity, Equity, and Inclusion) based on aggregated BEVI data
Visual presentation of trends and differences within and between discussion groups using tools such as the Within Group Report and Between Group Report
Comparison between individual BEVI scores and group-level data to visualize the diversity of perspectives even among faculty and staff
Breakout Session: Deepening Understanding and Practical Application
Participants engage in group discussions comparing their own scores with group reports, identifying both differences and shared assumptions
(Facilitated by instructors experienced in applying BEVI in their teaching)
Comparison with sample student scores (individual and group) to critically examine gaps between educators and learners
Interactive discussion incorporating real-time feedback from BEVI-AI to explore how personal tendencies influence educational interactions
Plenary Session: Synthesis and Q&A
Reflection on how insights from the day’s analyses can inform classroom and program-level practices using BEVI and BEVI-AI
Discussion of common challenges and concerns during implementation, including lessons learned from early adopter institutions
Clarification of how participants might envision integrating BEVI-based tools and approaches into their own institutional contexts
In recent years, international education programs—such as study abroad and COIL (Collaborative Online International Learning)—have been increasingly expected not only to demonstrate implementation outcomes (Outputs) but also to provide evidence of student development and transformation (Outcomes) through empirical assessment and measurement.
A significant shift is seen in the 2024 Inter-University Exchange Project guidelines, which explicitly require institutions to “assess and measure” student learning outcomes. This represents a notable departure from previous evaluation methods, which typically relied on post-program satisfaction surveys.
Launched in 2021, the Internationalization Promotion Forum project by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) aims to facilitate cross-institutional sharing of effective practices in international education. As part of this initiative, the adoption of BEVI (Beliefs, Events, and Values Inventory) has been actively promoted as an objective assessment tool. As of today, more than 50 universities in Japan have implemented BEVI, enabling not only internal satisfaction surveys but also standardized measurement and the comparison and sharing of results across institutions.
Workshop Significance
This workshop aims to provide an overview of BEVI as an objective psychometric tool, introduce its theoretical foundations, and explore practical applications in educational settings. Through shared dialogue, participants will deepen their understanding of how BEVI data can inform and enhance teaching and learning.
Participants will be asked to complete the BEVI survey in advance and will also experience personalized feedback through BEVI-AI. Unlike generic AI systems, BEVI-AI generates responses based on individual psychological assessment scores, offering a unique and highly tailored reflection of each participant’s values and assumptions. This allows faculty and staff to gain insights into their own worldviews and to explore how those perspectives intersect or differ from those of students and colleagues—ultimately supporting more intentional and inclusive educational practices.
We also welcome comparative discussion with other forms of objective data or assessments where applicable.
Tentative Workshop Agenda
Plenary Session: Data-Informed Introduction
Overview of value orientations related to DEI (Diversity, Equity, and Inclusion) based on aggregated BEVI data
Visual presentation of trends and differences within and between discussion groups using tools such as the Within Group Report and Between Group Report
Comparison between individual BEVI scores and group-level data to visualize the diversity of perspectives even among faculty and staff
Breakout Session: Deepening Understanding and Practical Application
Participants engage in group discussions comparing their own scores with group reports, identifying both differences and shared assumptions
(Facilitated by instructors experienced in applying BEVI in their teaching)
Comparison with sample student scores (individual and group) to critically examine gaps between educators and learners
Interactive discussion incorporating real-time feedback from BEVI-AI to explore how personal tendencies influence educational interactions
Plenary Session: Synthesis and Q&A
Reflection on how insights from the day’s analyses can inform classroom and program-level practices using BEVI and BEVI-AI
Discussion of common challenges and concerns during implementation, including lessons learned from early adopter institutions
Clarification of how participants might envision integrating BEVI-based tools and approaches into their own institutional contexts