Fostering Deep Belonging Through Culturally-Responsive AI Mentorship Agents: An Identity-Affirming Framework for Educational Support - Li, Zan; Zhu, Yida (Journal of Computer Technology and Applied Mathematics, 2025)
Culturally-responsive AI mentorship agents represent a substantial change in educational technology, addressing critical gaps between personalized learning systems and students' psychological needs for belonging. This research presents a comprehensive framework integrating multi-agent architectures with identity-affirming interaction mechanisms to cultivate deep belonging across diverse student populations. Through a mixed-methods empirical study involving 120 participants from three distinct cultural groups over six months, we demonstrate that culturally-adaptive AI mentors achieve 34.7% higher belonging scores compared to culturally-neutral systems. The framework employs dynamic cultural profiling, identity-safe feedback strategies, and personalized belonging interventions adapted from established psychological research. Statistical analysis reveals significant mediation effects where cultural responsiveness influences academic outcomes through enhanced belonging (β = 0.412, p < 0.001). Implementation across educational contexts shows differential effectiveness patterns, with underrepresented groups experiencing 42.3% greater benefit from culturally-responsive features. This work establishes theoretical foundations and practical guidelines for deploying AI mentorship systems that strengthen rather than diminish human connection in digital learning environments.