Work

Integrating Artificial Intelligence into Construction Education: Assessing the Impact on Students’ Perception of Knowledge, Confidence, and Relevance to Career

Year: 2025

Type: conference-paper

Abstract: In technical and professional disciplines such as construction, the importance of creating and implementing positive improvements in the educational curriculum to meet the dynamic and complex needs of the world cannot be overemphasized.There is a rising need for a more highly skilled workforce equipped with programming skills for the analysis of the huge amount of data that can be generated on construction sites, particularly with respect to the prediction of the properties of materials for useful insight generation as well as rapid and informed decision making.In this study, construction students were introduced to artificial intelligence (AI) techniques and how they can be used for predicting the properties of construction materials in a construction course.First, the students were presented with a basic knowledge of AI for predicting the strength of construction materials.A hands-on programming laboratory session was designed to get students started with the implementation of AI knowledge through "learning from practice."Students were made to practice using the provided code templates and make adjustments to see the impact of different AI models on prediction accuracy.Pre-and post-implementation surveys, together with hands-on laboratory assignments, were administered to evaluate students' perception of improvement in AI knowledge, confidence, and relevance to their career.The findings of the study indicate the effectiveness of the learning module incorporated into the course with the students' perception of AI knowledge, learning confidence, and relevance to career increasing by 39%, 22%, and 6%, respectively.These results reflect the students' understanding and appreciation for the importance of data and the exploration of historical material testing data using programming skills and AI techniques to rapidly estimate and better learn how different properties of materials influence their strengths.

DOI: 10.18260/1-2--55059

Authors: Chinedu Okonkwo, Roy Lan, Ibukun Awolusi, Jiannan Cai

Institutions: The University of Texas at San Antonio, The University of Texas at Arlington

Language: English

Topic: Artificial Intelligence in Education

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Funder: University of Texas at San Antonio

OpenAlex ID: W4409945308