A Computational Model of Inclusive Pedagogy: From Understanding to Application

TL;DR

This paper introduces a computational model of co-adaptive teacher-student interactions that incorporates contextual educational insights, demonstrating improved learning outcomes and enabling scalable, inclusive AI in education systems.

Contribution

It presents a novel testable computational T-SI model integrating contextual insights, advancing the understanding and application of co-adaptive educational strategies.

Findings

Co-adaptive strategies outperform unilateral approaches.

The model improves learning outcomes across diverse student groups.

It enables hypothesis generation in controlled educational environments.

Abstract

Human education transcends mere knowledge transfer, it relies on co-adaptation dynamics -- the mutual adjustment of teaching and learning strategies between agents. Despite its centrality, computational models of co-adaptive teacher-student interactions (T-SI) remain underdeveloped. We argue that this gap impedes Educational Science in testing and scaling contextual insights across diverse settings, and limits the potential of Machine Learning systems, which struggle to emulate and adaptively support human learning processes. To address this, we present a computational T-SI model that integrates contextual insights on human education into a testable framework. We use the model to evaluate diverse T-SI strategies in a realistic synthetic classroom setting, simulating student groups with unequal access to sensory information. Results show that strategies incorporating co-adaptation principles (e.g., bidirectional agency) outperform unilateral approaches (i.e., where only the teacher or the student is active), improving the learning outcomes for all learning types. Beyond the testing and scaling of context-dependent educational insights, our model enables hypothesis generation in controlled yet adaptable environments. This work bridges non-computational theories of human education with scalable, inclusive AI in Education systems, providing a foundation for equitable technologies that dynamically adapt to learner needs.