Adaptive Scaffolding for Cognitive Engagement in an Intelligent Tutoring System

2026-02-07

AI Summary

This study addresses the challenge of adaptively eliciting optimal levels of cognitive engagement in intelligent tutoring systems. Building upon the ICAP framework, it pioneers the integration of this theoretical model with adaptive instructional strategies by dynamically selecting between guided examples (active mode) and erroneous examples (constructive mode) to provide personalized scaffolding. The research implements two real-time adaptation algorithms—Bayesian Knowledge Tracing (BKT) and Deep Reinforcement Learning (DRL)—to tailor cognitive engagement modes to individual learners. An empirical evaluation with 113 students demonstrates differential efficacy: BKT significantly helps low-prior-knowledge students catch up with their higher-knowledge peers, whereas DRL more effectively enhances post-test performance among high-prior-knowledge students, revealing distinct responsiveness to adaptive strategies based on initial knowledge levels.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Intelligent Robots: Cognitive Robotics
• Machine Learning: Active Learning

Application Category

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

Abstract

The ICAP framework defines four cognitive engagement levels: Passive, Active, Constructive, and Interactive, where increased cognitive engagement can yield improved learning. However, personalizing learning activities that elicit the optimal level of cognitive engagement remains a key challenge in intelligent tutoring systems (ITS). In this work, we develop and evaluate a system that adaptively scaffolds cognitive engagement by dynamically selecting worked examples in two different ICAP modes: (active) Guided examples and (constructive) Buggy examples. We compare Bayesian Knowledge Tracing (BKT) and Deep Reinforcement Learning (DRL) as adaptive methods against a non-adaptive baseline method for selecting example type in a logic ITS. Our experiment with 113 students demonstrates that both adaptive policies significantly improved student performance on test problems. BKT yielded the largest improvement in posttest scores for low prior knowledge students, helping them catch up with their high prior knowledge peers, whereas DRL yielded significantly higher posttest scores among high prior knowledge students. This paper contributes new insights into the complex interactions of cognitive engagement and adaptivity and their results on learning outcomes.

Problem

• cognitive engagement
• intelligent tutoring systems
• personalized learning
• adaptive scaffolding
• ICAP framework

Innovation

• adaptive scaffolding
• cognitive engagement
• ICAP framework
• Bayesian Knowledge Tracing
• Deep Reinforcement Learning