Enhancing Team-Based Learning of Construction Scheduling and Cost Control through a Scenario-Based Digital Twin and LLM Support

Construction management education aims to develop students’ ability to make informed managerial decisions by integrating scheduling, progress monitoring, and cost control under dynamic project conditions. In practice, these competencies are tightly interrelated: progress deviations directly influence cost performance, resource allocation, and recovery strategies. However, current teaching approaches often rely on static examples, isolated calculations, and lecture-driven delivery, which limit students’ capacity to reason holistically, understand temporal cause–effect relationships, and justify management decisions in complex and evolving construction scenarios.This project proposes a reusable teaching framework that integrates a lightweight, scenario-based Digital Twin (DT) with Large Language Model (LLM)-assisted learning support to enhance team-based, scenario-driven learning of progress-oriented scheduling and cost-aware project control. The proposed framework is explicitly designed for teaching purposes rather than operational project management, with the objective of strengthening students’ professional reasoning and decision justification skills. The Digital Twin serves as a simplified, episodic representation of construction site scenarios, capturing task sequences, planned versus actual progress, and associated cost implications at key project stages. These snapshots provide a structured and authentic learning context for analysing progress deviations and exploring their cost consequences.Within this framework, the LLM functions as an interactive reasoning scaffold that supports, rather than replaces, student thinking. Through guided prompts and structured dialogue, the LLM assists student teams in interpreting Digital Twin scenarios, diagnosing progress-related issues, exploring alternative recovery strategies (e.g., re-sequencing, acceleration, or resource adjustment), and articulating how such decisions affect cost performance over time. Students are required to critically evaluate the LLM’s explanations, challenge assumptions, and refine their decisions, thereby fostering reflective thinking, reasoning transparency, and professional judgment.The framework is embedded into team-based, scenario-driven learning activities, where students collaboratively assume managerial roles and engage in structured decision-making tasks. Each team analyses shared Digital Twin scenarios, proposes progress control strategies, evaluates cost impacts, and defends their decisions through peer discussion and instructor-facilitated reflection. This learning design promotes active engagement, collaborative problem-solving, and communication skills essential for construction management and surveying professionals. While the framework is designed to be reusable across different construction management topics, this project deliberately focuses on scheduling, progress monitoring, and cost feedback to ensure depth of learning and rigorous evaluation.The proposed framework will be implemented and evaluated through selected learning activities in CA3401 Construction Management and Economics and CA3214 Construction Economics, providing authentic teaching contexts for demonstration and validation. The project is expected to enhance student learning by improving: (i) the ability to interpret and monitor project progress using scheduling information; (ii) understanding of the dynamic relationship between progress and cost performance; (iii) capability to compare and justify alternative control strategies under uncertainty; and (iv) engagement through team-based, scenario-driven learning. Project effectiveness will be evaluated through rubric-based assessment of student work, comparative analysis across cohorts, and structured student feedback.Overall, the project contributes a practical and reusable teaching framework that supports integrated reasoning, collaborative decision-making, and scenario-based learning, helping prepare next-generation civil and surveying students for data-informed construction management practice.