This study builds on Stoll and Beller’s framework for understanding moral reasoning by creating the AI Behavioral Quotient (AIBQ)—a tool that analyzes how students develop morally through their reflective writing. The AIBQ measures moral reasoning through three different lenses: how well students structure their thoughts (Analytic Linguistic Coherence), how broadly they explore ideas (Semantic Diversity Index), and how clearly they express their values (Ethical Value Expression). The framework was applied to longitudinal reflective journals and subsequent research papers produced by undergraduate Movement Sciences students (n = 120). Analyses indicate increases in semantic diversity and stronger alignment of ethical language with established moral-value categories across writing iterations, consistent with reflective growth over time. Rather than replacing human judgment, the model introduces a structured reflective interval that supports AI-assisted feedback prior to ethical decision-making. By combining transparent computational analysis with faculty-guided interpretation, AIBQ positions AI as a reflective partner that supports fairness, deliberation, and ethical continuity within human–AI learning systems.
- From the Playing Field to the Neural Field: Embedding the AI Moral Code in Augmented Cognition and Cybersecurity Ethics
- Randy J. Hinrichs - University of IdahoSharon K. Stoll - University of Idaho, Center for ETHICS
- Dylan D. Schmorrow (Editor)Cali M. Fidopiastis (Editor)
- Augmenting Cognition in the AI-Accelerated Era, pp.17-32
- Lecture Notes in Computer Science
- Springer Nature Switzerland; Cham
- 996976110601851
- © 2026 The Author(s), under exclusive license to Springer Nature Switzerland AG
- Movement Sciences; Center for ETHICS
- English
- Book chapter