CLIN02 - Knowing when it is good enough: how sonography students learn to judge their own scanning

Overview

A sonographer builds the diagnostic image. They sweep a probe across a moving, three-dimensional organ and produce a record. Doing that well takes two kinds of skill. One is the physical craft: hand control, optimisation, working through an organ systematically. The other is evaluative judgement, the ability to tell whether your own work is good while you are still doing it.

Two separate judgements sit inside that. Did the scan interrogate the region thoroughly enough to find pathology if it was there? And do the saved images fairly represent what the scan actually did? These can come apart. A tidy set of static images can come from a scan that missed something.

Research on evaluative judgement has almost always used finished work: essays, reports, artefacts judged after the fact. It has rarely looked at the live act of producing the work, and we have found no study examining it in sonography, where the work is embodied, perceptual, and gone the moment it ends.

This project asks how novice sonography students judge their own scanning as they do it. It runs in the QUIQ Lab, records the full video of each scan alongside the still images the student chooses to keep, and compares the student's own judgement against a panel of experienced sonographers using the same rating instrument. Scanning is limited to one organ to keep the study finishable within the scheme.

By separating judgement of the image from judgement of the scan, the project can ask a question the field has not been able to pose: is recognising good work a single capability, or does it depend on what is being judged? The answer bears on how we teach and assess judgement in sonography, and on assessment more broadly at a point where generative AI can produce a plausible artefact without the judgement behind it.

Research engagement

Prior to data collection, the student will engage with a focused literature base (evaluative judgement). During data collection, you will work with an ultrasound video-capture and data-management workflow in the QUIQ Lab (Kelvin Grove campus), apply a structured rating instrument to ultrasound images and video, and be introduced to mixed-effects analysis in R. The analysis is taught and supervised as you go.

Research activities

You will work alongside the supervisory team, an expert panel of sonographers, sonography students as participants, and a small pool of volunteers who agree to be scanned. Specifically, you will:

• help set up and test the video-capture workflow in the lab
• assemble the expert panel's reference ratings for both the still images and the scan videos, and calculate how consistently the experts agree with each other
• support the pilot scanning sessions
• use R to measure how closely novice judgements of their own images and sweeps align with expert judgement

Ethics approval will be in place before the project begins, so you work inside an approved protocol on de-identified data throughout.

Research skills

Designing and running a study within ethical constraints; applying a structured rating instrument reliably; mixed-effects (multilevel) modelling in R; data de-identification and management; and research communication through co-authorship on the resulting paper. You will also finish with a working understanding of evaluative judgement and of ultrasound image quality, both of which transfer to any imaging or assessment field.

Outcomes

The project has three aims.

1. Measure novice judgement in action. How closely do novice sonographers' judgements of their own work, both the images they keep and the live sweep that produced them, match those of experienced sonographers? This is the process-level evidence the field currently lacks.
2. Test whether the two judgements come apart. A student might judge their images well and their scanning poorly, or the reverse. If that happens, evaluative judgement is specific to what is being judged, which changes how it should be taught.
3. Turn the findings toward design. Evidence about where novice judgement diverges from expert judgement, particularly on the process, gives us something to build learning and assessment activities from, instead of leaving judgement to accumulate with clinical hours. The method should transfer to other examinations and modalities.

This is a feasibility study, scoped to the scheme's timeframe. You will give the required VRES oral presentation and contribute to a co-authored manuscript. The project is positioned to seed an Honours or HDR study, so there is a route to continue if you want one.

Skills and experience

An undergraduate in a relevant health or medical-science discipline (medical imaging, biomedical science, or similar) with a strong academic record consistent with the scheme's expectations, and an interest in higher degree research.

Careful, methodical work with data matters more here than prior experience with it. Some exposure to R or statistics is an advantage, though it is not required and the analysis will be taught.

You do not need to be enrolled in sonography. Sonography students are the participants in this study; the scholar sits outside that group. An interest in clinical education, assessment, or medical imaging will make the experience more rewarding.

Start date

2 November, 2026

End date

19 February, 2027

Location

QUIQ  Lab  (Kelvin Grove Campus)

Additional information

Resources available to the student: structured supervision from the team; access to the QUIQ lab and its ultrasound equipment; the video‑capture workflow; secure QUT data storage; R and library/EndNote; an expert sonographer panel for the reference ratings; and supervised access to the sonography cohort and volunteer models for recruitment. The project is bounded to a single organ to keep the workload within the scheme’s hours.

Keywords

• sonography
• evaluative judgement
• health professions education
• ultrasound
• Artificial Intelligence
• GenAI

Contact

Chris Edwards

3138 1924

c8.edwards@qut.edu.au