Designing AI-Driven Learner Modeling Architectures for Regulated Competency-Based Education: A Comparative Analysis of Probabilistic and Symbolic Approaches in the Quebec Collegiate Context

Abstract

Learner modeling is a foundational component of adaptive educational systems, yet the dominant architectures in the field have been conceived without regard for the specific regulatory and curricular constraints of provincial or national education systems. This article addresses that gap by conducting a structured conceptual analysis of two major learner modeling paradigms — symbolic (rule-based) and probabilistic (Bayesian) models — explicitly situated within the regulatory and curricular framework governing Quebec’s collegiate sector. Drawing on a decade of empirical and theoretical work on Bayesian learner modeling (Tadlaoui et al., 2014a, 2014b, 2015a, 2016, 2017, 2018a, 2018b, 2019a, 2019b), a systematic documentary analysis of Quebec’s Act to Modernize Legislative Provisions Respecting the Protection of Personal Information (Law 25), competency-based program specifications (AEC/DEC devis ministériels), and the professional regulatory frameworks of the OACIQ and the RBQ, we identify six dimensions along which the two paradigms diverge in pedagogically and legally consequential ways. On the basis of this analysis, we propose the AIPAQ framework (Pedagogically Aligned AI Architecture for Quebec), a four-layer hybrid architecture that deploys symbolic logic at the regulatory and explainability layers and probabilistic inference at the competency modeling and micro-pedagogical layers. The proposed framework offers both a practical design guide for Quebec collegiate institutions and a generalizable methodology for regulatory-aware AI design in competency-based educational systems.