Author Information
Antonella Chifari, National Research Council of Italy, ItalyMarco Arrigo, National Research Council of Italy, Italy
Giuseppe Chiazzese, National Research Council of Italy, Italy
Abstract
The PBIS-RISE Generator introduces a novel application of Generative Artificial Intelligence (Gen-AI) in the domain of behavioral education, specifically within the Positive Behavioral Interventions and Supports (PBIS) framework. While PBIS promotes the development of locally adapted behavioral expectation matrices to foster prosocial conduct, a persistent challenge lies in evaluating students’ comprehension of these expectations in a standardized yet context-sensitive manner. PBIS-RISE addresses this need by employing a GPT-based model, trained and prompted on PBIS theory, validated behavior matrices, and existing school climate assessments to generate customized questionnaires. These instruments reflect each school’s cultural norms and educational goals while preserving fidelity to the PBIS framework. The generator creates items across six core dimensions: Behavioral Comprehension, Modeling, Generalization, Value Association, Emotional Association, and Meta-Emotional Reflection, thereby aligning with both the cognitive and affective domains of social behavior education. The RISE acronym: Reflecting on Interactions, Standards, and Expectations, captures the tool’s reflective and student-centered design. For educators and school psychologists, PBIS-RISE supports data-driven decision-making, enabling scalable yet individualized behavioral assessments that inform tiered interventions. For teachers, it reduces the burden of developing evaluation tools and enhances consistency in behavior-related assessments. As educational research increasingly explores the integration of AI in formative assessment, PBIS-RISE offers a replicable, theory-grounded, and ethically aligned model to support equitable behavioral education in diverse school settings.
Paper Information
Conference: ACE2025Stream: Design
This paper is part of the ACE2025 Conference Proceedings (View)
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To cite this article:
Chifari A., Arrigo M., & Chiazzese G. (2026) PBIS-RISE: A GPT-Powered Questionnaire Generator for Assessing Student Understanding of Behavioral Expectations ISSN: 2186-5892 – The Asian Conference on Education 2025: Official Conference Proceedings (pp. 1333-1344) https://doi.org/10.22492/issn.2186-5892.2026.103
To link to this article: https://doi.org/10.22492/issn.2186-5892.2026.103
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