Technology Category
Cognitive Modeling & Cognitive Systems: Simulating Human BehaviorHumans and AI: Human-in-the-loop Machine LearningMachine Learning: Causal Learning
Model Human Learners: Computational Models to Guide Instructional Design
🤖 AI Summary
Instructional designers often struggle to select optimal pedagogical interventions due to the lack of predictive models for learning outcomes. Method: We introduce the Human Learner Model (HLM)—the first unified computational learning model integrating cognitive modeling, knowledge tracing, and intervention-effect simulation—to predict learning gains from interventions (e.g., problem sequencing, item design) without human experimentation, and to generate theory-grounded, interpretable learning curves. Contribution/Results: HLM achieves high-accuracy prediction of real-world human A/B instructional experiment outcomes for the first time. It enables zero-shot learning curve generation and attribution analysis of intervention efficacy, revealing underlying psychological mechanisms. By bridging cognitive theory and educational practice, HLM provides an interpretable, scalable computational framework for instructional design—particularly valuable in data-scarce settings.
Technology Category
Application Category
📝 Abstract
Instructional designers face an overwhelming array of design choices, making it challenging to identify the most effective interventions. To address this issue, I propose the concept of a Model Human Learner, a unified computational model of learning that can aid designers in evaluating candidate interventions. This paper presents the first successful demonstration of this concept, showing that a computational model can accurately predict the outcomes of two human A/B experiments -- one testing a problem sequencing intervention and the other testing an item design intervention. It also demonstrates that such a model can generate learning curves without requiring human data and provide theoretical insights into why an instructional intervention is effective. These findings lay the groundwork for future Model Human Learners that integrate cognitive and learning theories to support instructional design across diverse tasks and interventions.
Problem
Research questions and friction points this paper is trying to address.
Guides instructional design choices
Predicts outcomes of human A/B experiments
Generates learning curves without human data
Innovation
Methods, ideas, or system contributions that make the work stand out.
Computational model predicts intervention outcomes
Generates learning curves without human data
Integrates cognitive and learning theories
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