Abstract
This paper presents the development and evaluation of an AI mentoring system designed with intersectionality at its core. Using HEXACO-based personality profiling, demographic mapping, and mentoring preference models, the system dynamically generates three distinct mentor personas for each user. We analyse how different user identities—across gender, class, race, and discipline—affect their mentor choices and interactions. By operationalising intersectionality into AI architecture, we challenge universalist AI approaches and offer an adaptive model that acknowledges and responds to users’ lived realities. The paper contributes a framework for inclusive AI design that centres on difference, not abstraction.
Presenters
Timipado ImomotebeghaStudent, PhD Applied Human Computer Interaction, Loughborough University, United Kingdom
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
AI MENTORSHIP, INTERSECTIONALITY, PERSONALITY PROFILING, ADAPTIVE SYSTEMS
