CBPM Conference 2026 · Vol. 2026, Issue 1

Student Attendance and Academic Performance in a GenAI-Enabled Quantity Surveying Learning Environment

Anushka Rathnayake, Nipuni Sumanarathna, Aravinda Adhikari

Keywords: Student Attendance; Academic Performance; Generative Artificial Intelligence (GenAI); Quantity Surveying Education; Measurement Education.

Abstract

The increasing availability of Generative Artificial Intelligence (GenAI) tools is reshaping teaching, learning, and assessment practices in higher education. While GenAI offers opportunities to support independent learning and accessibility, concerns remain regarding student engagement, attendance, assessment authenticity, and competency development within applied disciplines. These concerns are particularly relevant in Quantity Surveying (QS) education, where measurement modules require students to develop technical competence, analytical reasoning, and professional judgement through guided practical activities. This study investigates the relationship between student attendance and academic performance within a Level 4 QS Measurements module delivered at a UK higher education institution. A quantitative research approach was adopted using secondary data analysis of anonymised attendance records and assessment outcomes from 60 undergraduate students. Descriptive statistical analysis was conducted to examine patterns between attendance, coursework performance, and overall module achievement. Findings suggest that students attending seven to ten sessions generally achieved higher average grades and pass rates than students attending fewer sessions. The analysis also identified variation between CW01 and CW02 performance, suggesting that different assessment components may evidence different aspects of students’ technical understanding within technology-enabled learning environments. This study contributes empirical evidence to the limited literature on attendance-performance relationships in QS education. The findings suggest that attendance remains an important component of academic performance in Quantity Surveying measurement education and highlight the need for authentic assessment approaches that support technical competency development in AI-enabled learning environments.