The Productivity Promise: A Critical Study of AI-Driven Monitoring Technologies and Their Impact on Construction Productivity (Ref: ABCE-EBE-KA)

About the Project

Construction organisations are increasingly turning to AI-enabled monitoring systems, such as machine-vision progress tracking, predictive safety and productivity analytics, and automated resource allocation, to enhance project delivery and reduce inefficiencies. These technologies promise real-time insights, faster decision-making, and the ability to optimise workflows at a scale previously impossible. However, empirical evidence on their actual productivity impact remains limited, fragmented, and often driven by vendor claims rather than independent, site-based evaluation.

This PhD project will critically examine how AI-driven monitoring systems influence construction productivity and project delivery outcomes. It will investigate how these tools are integrated into everyday site practices, how project teams interpret and act upon AI-generated insights, and how the presence of monitoring technologies shapes behaviours, communication, and coordination across project stakeholders. The research will also explore both the productivity gains and the potential unintended consequences, such as increased monitoring pressure, data misinterpretation, or workflow disruptions, that may undermine expected benefits.

The ultimate goal is to develop a robust performance evaluation framework that enables construction organisations to assess the true value, limitations, and risks of AI implementations. This framework will support more evidence-informed decisionmaking, helping organisations determine when, where, and how AI-enabled monitoring technologies genuinely contribute to improved productivity and project delivery.

Name of primary supervisor/CDT lead:

Dr Kudirat Ayinla k.ayinla@lboro.ac.uk

Name of secondary supervisor:

Prof Sohail Khan https://www.lboro.ac.uk/departments/abce/staff/m-sohail-khan/

Entry requirements:

Applicants should have, or expect to achieve, at least a 2:1 honours degree (or equivalent) in a relevant discipline such as Construction Management, Engineering, or a related field. A relevant Master’s degree and/or industry or research experience in areas such as digital construction, AI and data analytics, construction technology, or project management is highly desirable.

Candidates should demonstrate strong analytical and critical-thinking skills, an interest in emerging digital technologies, and the ability to engage with both technical and human-centred aspects of construction. Experience with qualitative or mixed-methods research, data analysis, or the application of digital tools in construction will be advantageous. Good communication skills and the ability to collaborate with industry partners and site-based teams are essential.

English language requirements:

Applicants must meet the minimum English language requirements. Further details are available on the International website (http://www.lboro.ac.uk/international/applicants/english/).

Bench fees required:

No

Closing date of advert:

30th June 2026

Start date:

October 2025

Full-time/part-time availability:

Full-time 3 years

Fee band:

2025/26 Band RB (UK £5,006, International £28,600)

How to apply:

All applications must be made online and must include a completed studentship application form (instead of a personal statement) and a two-page research proposal based on the project description outlining how you would approach the project and what methods you would use. Under programme name, please select ‘Architecture, Building and Civil Engineering (Built Environment)’. Please quote advert reference ABCEEBE-KA.

To avoid delays in processing your application, please ensure that you submit the minimum supporting documents including an up-to-date CV, but a personal statement is not required.

Project search terms:

artificial intelligence, built environment, construction management

Email Address ABCE:

abce.pgr@mailbox.lboro.ac.uk