2025 ASEE Annual Conference & Exposition

This article outlines the objectives, design, recent findings, and anticipated outcomes of a newly funded research initiative supported by the National Science Foundation (NSF). The project is part of the Research on Innovative Technologies for Enhanced Learning (RITEL) program, which prioritizes pioneering research in emerging teaching and learning technologies tailored to address critical challenges in real-world educational contexts. The primary aim of the project is to create, implement, and assess an AI-driven learning platform in the format of a mobile application called CeLens that acts as an on-demand educator to help construction engineering students learn from their unstructured observations during everyday activities. CeLens seamlessly merges students’ observations during everyday experiences or formal site visits with their formal engineering education. The platform, designed based on the activity learning theory and developed based on human-centered principles, leverages advanced hybrid image-audio processing techniques to accurately and efficiently identify and explain diverse construction elements.
The envisioned AI-enhanced learning system will be designed based on the Activity Learning Theory, which asserts that the human mind is an integral part of environmental interactions and positions activity, whether sensory, mental, or physical, as a precursor to learning. The AI-enhanced platform will be designed based on human-centered principles and will operate using a novel hybrid image-audio processing system that can efficiently and effectively recognize and classify various construction components. In addition to integrating imagery and audio data through this novel hybrid approach, the project will introduce two major technological innovations in audio processing and sound recognition. First, the hybrid use of collected audio and imagery data will improve the overall performance of the system by capturing a more comprehensive range of construction components and operations. Second, by using innovative audio processing and signal source separation algorithms, the need for multiple microphones will be eliminated, enabling the entire system to be encapsulated in a single device (i.e., a student's smartphone) with the ability to sense and analyze audio signals from distances of up to 100 feet. Throughout this project, the proposed AI-enhanced teaching and learning approach will be implemented in multiple undergraduate construction engineering courses to empirically evaluate its effectiveness on students' learning processes and outcomes, as well as the perceptions of both students and educators regarding this innovation as a formal pedagogical method. Although the AI-enhanced learning platform will be developed in the context of construction engineering, the proposed learning method and the intellectual merit of this project can be transferred to other disciplines. This project will also assess the broader applicability of the proposed innovation.