A Computational Model of Inclusive Pedagogy: From Understanding to Application

2025-05-02

AI Summary

Current educational science lacks a computationally tractable teacher-student co-adaptation interaction (T-SI) model, hindering context-sensitive theoretical validation and AI-driven educational systems’ capacity to dynamically adapt to human learning processes. Method: We propose the first formal, executable T-SI model—grounded in inclusive pedagogy and “bidirectional agency”—and implement it within a synthetic classroom environment featuring heterogeneous sensory input. Our approach integrates agent-based modeling (ABM), multi-type learner representations, and a strategy-comparison evaluation framework. Results: Empirical evaluation demonstrates that our co-adaptive strategies significantly improve learning outcomes across diverse learner profiles, confirming both generalizability and fairness. The model further enables hypothesis generation and cross-contextual validation, establishing a foundational framework for scalable, interpretable, and equitable AI in Education (AIEd) systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Learning Human Values and Preferences
• Multiagent Systems: Adversarial Agents

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Modeling co-adaptive teacher-student interactions computationally
• Bridging educational theories with scalable AI systems
• Improving learning outcomes through bidirectional adaptation strategies

Innovation

• Computational model for co-adaptive teacher-student interactions
• Simulates diverse strategies in synthetic classroom settings
• Bridges human education theories with scalable AI systems