A Computational Model of Inclusive Pedagogy: From Understanding to Application
This work addresses the problem of scaling educational insights and improving AI in Education systems for researchers and developers, though it appears incremental as it builds on existing theories with a new computational framework.
The paper tackled the underdevelopment of computational models for co-adaptive teacher-student interactions by presenting a model that integrates contextual insights into a testable framework, showing that strategies with co-adaptation principles outperform unilateral approaches in improving learning outcomes for all student types in a synthetic classroom setting.
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.