Work

A Unified AI Architecture for Self-Regulated Learning: Cognitive Modeling, Meta-Learning, and Continual Adaptation

Year: 2025

Type: article

Abstract: The growing need for intelligent educational systems calls for architectures supporting adaptive instruction, while enabling more permanent, long-term personalization and cognitive alignment in the long run. While we have seen progress in adaptive learning technologies at the intersection of Self-Regulated Learning (SRL), Continual Learning (CL), and Meta-Learning, these are generally employed in isolation to provide piecemeal solutions. In this paper, we propose CAMEL, a unified architecture for (1) cognitive modelling based on SRL, (2) continual learning functionalities, and (3) meta-learning to provide adaptive, personalized, and cognitively consistent learning environments. CAMEL includes the following components: (1) A Cognitive State Estimator that estimates learner motivation, attention, and persistence from behavioral traces, (2) A Meta-Learning Engine that allows it rapid adaptation through Model-Agnostic Meta-Learning (MAML), (3) A Continual Learning Memory that preserves knowledge across sessions using Elastic Weight Consolidation (EWC) and Replay, (4) A Pedagogical Decision Engine that makes real-time efficient adjustments of instructional strategies, and (5) A closed-loop that continuously reconciles misalignments between pedagogical actions and predicted cognitive states. Experiments conducted on the xAPI-Edu-Data dataset evaluate the system’s few-shot adaptation capability, knowledge retention, cognitive-state prediction accuracy, and knowledge, as well as cognitive responsiveness to the impending questions. It offers competitive performance in learner-state prediction and long-term performance compared to the baselines, and the improvements are consistent across the different baselines. This paper lays the groundwork for next-generation adaptive and cognition-driven AI-based learning systems.

Source: Algorithms

DOI: 10.3390/a19010026

Authors: Ridouane Oubagine, Loubna Laaouina, Adil Jeghal, Hamid Tairi

Institution: Sidi Mohamed Ben Abdellah University

Language: English

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $1,930