Role of Instructional Guidance in Generative AI-Assisted Learning

Investigates how instructional guidance shapes student-AI interaction in construction engineering education. Introduces a five-step prompting framework grounded in Generative Learning Theory (GLT) to guide learner interaction during review activities. Three conditions tested in a controlled experiment: slide-based learning, unprompted AI-supported learning, and prompted AI-supported learning.

Key findings: Performance differences concentrated on tasks requiring explanation and reasoning (higher-order cognitive outcomes). The prompted condition achieved significantly higher open-ended scores (≈2–3 points on 18-point scale, p<0.01). No significant differences in multiple-choice recall across conditions. Unprompted AI use did not outperform traditional slide-based review.

Demonstrates that the effectiveness of AI-supported learning depends critically on how interaction is structured — a simple prompting framework grounded in learning science can significantly improve higher-order cognitive outcomes. Provides a basis for integrating learning science principles into GenAI systems for education.

Connected Concepts

Higher Ed

Scaffolding

Active Learning

Personalized Learning

Connected Articles

Flowcode AI Creative Coding — Flowcode: An AI-Powered Programming Environment for Scaffolding Iteration in Creative Computing Education

LLM Tts Dialogue Lesson Generation — A Semi-Automated System for Generating Dialogue-Based TTS Lessons Using Large Language Models: An Exploratory Study of Educational Potential

Learnmate2 LLM Adaptive Learning — LearnMate^2: Design and Evaluation of an LLM-powered Personalized and Adaptive Support System for Online Learning

Q Learning Lab RL Teaching — Q-Learning Lab: Teaching Reinforcement Learning Through Learner-Generated Trace Analysis

Courseblueprint Adaptive Video Generation — CourseBlueprint: A Structured Pipeline for Adaptive Pedagogical Video Generation Grounded in Course Corpora

LLM Misconception Difficulty Easy Trap — The Easy Trap: Why LLMs Underestimate Misconception-Driven Difficulty

Citation

Hou, X., Xiao, B., Liu, H., & Mueller, S. (2026). The Role of Instructional Guidance in Generative AI-Assisted Learning: Empirical Evidence from Construction Engineering Education. arXiv:2606.05509.