Project Overview

We are rapidly moving to a world where people collaborate with large language model (LLM)-based copilots such as ChatGPT and Microsoft Copilot on many tasks. These LLM copilots can assist people with complex tasks such as communication, idea generation, and problem solving. Organisations are already integrating these AI tools into employee workflows and, meanwhile, job candidates are also using these tools to help find employment (e.g., to write cover letters, prepare interview responses). Establishing and using effective job candidate assessment tools is the foundation of staffing organisations, however, the extant evidence base for best candidate assessment practices, namely, thousands of validation studies, assumes that humans are the sole agents of what they produce, both as candidates and as employees. In a world of human-copilot collaboration, the tenability of that assumption is questionable, and thus our current validation frameworks must be updated for fitness to a future world of human-copilot partnerships. Ignoring this shift risks basing hiring decisions on outdated assumptions and criteria that fail to capture the full spectrum of a candidate’s abilities, including their proficiency in effectively and honestly leveraging AI tools.

Aims

The project aims to unpack how generative artificial intelligence co-pilots will impact both the assessment of candidates seeking paid work and the evaluation of incumbents' job performance.  Through this project, we hope to update our understanding about what the most effective tools are for candidate assessment.

Objectives

In this project, the PhD candidate will conduct a series of experiments that are designed to improve our awareness of how candidates might use LLMs as co-pilots when developing their job application materials (e.g., cover letters, resumes, interview responses, test responses), and the factors that influence decisions that candidates make (e.g., to try to be honest versus trying to cheat).

Significance

Personnel recruitment and selection is undergoing major disruption as generative AI based copilots have for the first time put AI tools directly into the hands of job applicants.  We urgently need to update our evidence base regarding the efficacy of our traditional candidate assessment tools while identifying any potential new sources of unfairness created by this new technology.

Materials and equipment for the research are already purchased and available for use to the student as part of the university’s current infrastructure (e.g., Qualtrics surveys, workspaces). The student will be based at the Future of Work Institute, which has a thriving research-active culture and a large cohort of current PhD students with more incoming students expected in 2025. The student’s supervisory team, comprising two academics from Curtin, is well-positioned to supervise this project because of complementary and overlapping expertise. Both supervisors have a track record of supervising PhD students, mentoring early career academics, and publishing in top-tiered journals. Both also have extensive industry connections and a track record of industry led research.  The main cost to the project will be in participant recruitment (experiments and interviews). Funding for the project will be largely allocated to those activities. This project contributes to Curtin’s People and Partnerships strategic pillars through its focus on enabling employers to design selection systems that grant more opportunities to improve minority representation, and understanding candidate behaviours in an era of generative AI.

An internship may be available for this project. There are opportunities for the candidate to obtain an internship with one of the Future of Work Institute's industry partners.  These include talent assessment development firms such as TestGrid, Criteria Corporation, People Solutions, and HireVue.

• Future Students

• Faculty of Business and Law

• Higher Degree by Research

• Australian Citizen
• Australian Permanent Resident
• New Zealand Citizen
• Permanent Humanitarian Visa
• International Student

• Merit Based

The annual scholarship package, covering both stipend and tuition fees, amounts to approximately $70,000 per year.

In 2024, the RTP stipend scholarship offers $35,000 per annum for a duration of up to three years. Exceptional progress and adherence to timelines may qualify students for a six-month completion scholarship.

A top-up scholarship of a value up to $10,000 per annum may also be available for this position for three years.

Selection for these scholarships involves a competitive process, with shortlisted applicants notified of outcomes by November 2024.

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All applicable HDR courses.

This project would suit individuals who are interested in applying psychological principles and theories to applied settings and high stakes selection decisions. The project will have a strong quantitative focus. We do not expect applicants to already have the skills to analyse the data from the project, however, we believe the project would best suit a candidate with a strong interest in quantitative research methods including experimental design, hypothesis testing, data processing, and analyses. Experience in recruitment or selection is a desirable characteristic but not essential. A background in psychology is desirable.

 

Please send your CV, academic transcripts and brief rationale why you want to join this research project via the HDR Expression of Interest form to the project lead researcher, listed below. 

You must be enrolled in a Higher Degree by Research Course at Curtin University by March 2025.

Project Lead: Professor Patrick Dunlop