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Trial registered on ANZCTR
Registration number
ACTRN12625000523404
Ethics application status
Approved
Date submitted
2/05/2025
Date registered
26/05/2025
Date last updated
26/05/2025
Date data sharing statement initially provided
26/05/2025
Type of registration
Prospectively registered
Titles & IDs
Public title
Exploring perceptions of an artificial intelligence tool for health information.
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Scientific title
Exploring the Impacts of Artificial Intelligence (AI) Warmth and Competence on User Trust for Health Information
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Secondary ID [1]
314278
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Nil known
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Universal Trial Number (UTN)
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Trial acronym
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Linked study record
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Health condition
Health condition(s) or problem(s) studied:
Atrial fibrillation
337215
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Condition category
Condition code
Cardiovascular
333625
333625
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0
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Other cardiovascular diseases
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Intervention/exposure
Study type
Interventional
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Description of intervention(s) / exposure
The intervention/s employed in this study target the perceptions of warmth and competence in AI systems which participants will use to learn about atrial fibrillation.
The study will employ a between-subjects 2x2 design:
Variable 1 - Competence (perceived; high vs. low): participants will be randomised to read one of two text-based descriptions of an AI agent, one emphasising the agent’s intelligence, accuracy and capabilities, the other a more general description with no competence descriptors.
Variable 2 - Warmth (high vs. low): after reading the competence description, participants will be randomly allocated to receive one of two private access links to distinct Cogniti agents (Cogniti is a site developed by the University of Sydney for people to design AI agents). These agents have differing system messages which have been written by the research team to instruct the agent of the characteristics to express and conversational styles to adopt during interactions. The high warmth agent expresses high social orientation in its conversational style. The low warmth agent has a direct and emotionally neutral conversational style, adopting the persona of a web-based search rather than a conversational agent. Other than the conversational styles and benevolence level, the agents are identical.
The resulting groups will be:
High competence x high warmth
High competence x low warmth
Low competence x high warmth
Low competence x low warmth = active control group
Participants will be randomly allocated to read a high- or low- competence text-based description. These descriptions are quite short and we anticipate that participants will require less than one minute of reading time. These descriptions will be administered within the survey via Qualtrics, not by any specific member of a research team.
To ensure sufficient interaction with the AI agent, the participants will not be able to proceed with the experiment until five minutes has passed. Ongoing pilot testing has confirmed that this is sufficient time, and has indicated that at least five meaningful questions should be asked to obtain sufficient information. Transcripts will therefore be checked upon submission for evidence of this sufficient engagement (i.e. at least five questions asked).
Participants will be provided with the following instruction to prompt their interaction with the AI agent: "Please spend five minutes using this agent to get a good understanding of atrial fibrillation. Read its responses carefully. You could ask about what the disease is, its symptoms, causes, diagnosis and treatments, and what life looks like with the disease."
Information provided by the agents are entirely text-based. The high-warmth agent does use emoticons to communicate the social orientation, and the low-warmth agent does not. The interactions will all occur within the single session and the agent is not accessible by the participants outside this time window. Participants will access the study from their personal devices in a location of their choosing, wherever they have access to the internet. There will not be any in person sessions.
To monitor the adherence to the intervention, we will be collecting manipulation confirmation responses (twelve items that measure warmth and competence), and will be conducting a sentiment analysis using Linguistic Inquiry and Word Count (LIWC) Analysis for the AI outputs and if applicable the questions that the participants ask the AI.
Pilot Testing clarification:
Informal pilot testing will be conducted with a distinct cohort, none of whom will be completing the actual survey. This will occur before recruitment into the study commences. The purpose of the pilot testing is to identify technological and administrative issues only, particularly with logging into Cogniti and accessing the agents. We are anticipating including 16 individuals in this informal pilot testing. The data obtained in this informal pilot testing will not be included in any data analysis in the general study.
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Intervention code [1]
330889
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Behaviour
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Comparator / control treatment
The low-competence (LC) x low-warmth (LW) group acts as the control groups. This is an active control group as the participants will be interacting with a Cogniti agent.
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Control group
Active
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Outcomes
Primary outcome [1]
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Intention to Use
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Assessment method [1]
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We will assess participants' future intentions to use the AI agent they interacted with using Venkatesh et al.'s (2012) three-item validated scale.
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Timepoint [1]
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Immediately after interaction with the AI agent.
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Primary outcome [2]
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Trust in AI Agent
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Assessment method [2]
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To assess trust, we will use the items developed and validated by Komiak & Banbast (2006) trust scale, adapted for AI technology.
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Timepoint [2]
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Immediately after interaction with the AI agent.
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Primary outcome [3]
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Atrial Fibrillation Knowledge
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Assessment method [3]
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Knowledge will be assessed using Abubakar et al.’s (2020) Atrial Fibrillation Knowledge Assessment Tool (AFKAT). This tool uses 21 true and false question to assess knowledge about atrial fibrillation, designed to be applicable to the general population.
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Timepoint [3]
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Immediately after interaction with the AI agent.
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Secondary outcome [1]
446763
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Manipulation Confirmation - Competence
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Assessment method [1]
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Using six items from Fiske et al.'s Stereotype Content Model, participants will rate the competence of the agent they have just interacted with to confirm that the manipulations were impactful on perceptions. For example, participants will rate the agents level of capability, intelligence.
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Timepoint [1]
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Immediately after the AI agent interaction.
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Secondary outcome [2]
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Manipulation Confirmation - Warmth
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Assessment method [2]
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Using six items from Fiske et al.'s Stereotype Content Model, participants will rate the warmth of the agent they have just interacted with to confirm that the manipulations were impactful on perceptions. For example, participants will rate the agents level of friendliness and sincerity.
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Timepoint [2]
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Immediately after the AI interaction.
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Eligibility
Key inclusion criteria
Participants will be sourced from the University of Sydney's SONA systems participation pool. Participants on this system are often undergraduate students who complete research for course credit.
Participants must have a sufficient English language ability for engaged participation with the AI agents.
Data will only be used if participants submit a transcript of their interaction with the AI agent that indicates substantial interaction (i.e. more than five questions asked - to be confirmed from pilot testing).
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Minimum age
17
Years
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Maximum age
No limit
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Sex
Both males and females
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Can healthy volunteers participate?
Yes
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Key exclusion criteria
Participants with an existing knowledge of atrial fibrillation will not be included as this may bias results and impact their interaction with the agent (this refers to a clinical knowledge held by current/past medical professionals).
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Study design
Purpose of the study
Educational / counselling / training
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Allocation to intervention
Randomised controlled trial
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Procedure for enrolling a subject and allocating the treatment (allocation concealment procedures)
Allocation occurs in Qualtrics, embedded in the survey completed by participants. Participants will not know about groups or that there is an intervention.
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Methods used to generate the sequence in which subjects will be randomised (sequence generation)
Simple randomisation using a computer software (Qualtrics)
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Masking / blinding
Blinded (masking used)
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Who is / are masked / blinded?
The people receiving the treatment/s
The people administering the treatment/s
The people assessing the outcomes
The people analysing the results/data
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Intervention assignment
Factorial
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Other design features
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Phase
Not Applicable
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Type of endpoint/s
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Statistical methods / analysis
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Recruitment
Recruitment status
Not yet recruiting
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Date of first participant enrolment
Anticipated
26/05/2025
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Actual
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Date of last participant enrolment
Anticipated
5/09/2025
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Actual
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Date of last data collection
Anticipated
5/09/2025
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Actual
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Sample size
Target
200
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Accrual to date
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Final
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Recruitment in Australia
Recruitment state(s)
ACT,NSW,NT,QLD,SA,TAS,WA,VIC
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Funding & Sponsors
Funding source category [1]
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Self funded/Unfunded
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Name [1]
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Address [1]
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Country [1]
318796
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Primary sponsor type
University
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Name
The University of Sydney
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Address
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Country
Australia
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Secondary sponsor category [1]
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None
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Name [1]
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Address [1]
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Country [1]
321242
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Ethics approval
Ethics application status
Approved
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Ethics committee name [1]
317404
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The University of Sydney Human Research Ethics Committee
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Ethics committee address [1]
317404
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https://www.sydney.edu.au/research/research-integrity-and-ethics.html
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Ethics committee country [1]
317404
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Australia
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Date submitted for ethics approval [1]
317404
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16/04/2025
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Approval date [1]
317404
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20/05/2025
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Ethics approval number [1]
317404
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Summary
Brief summary
Artificial Intelligence (AI) has grown in popularity over the last few years, being used by more people for more purposes. The characteristics and communication styles of generative AI systems (like ChatGPT) impact user perceptions and interactions with the systems. In fact, some evidence suggests that we evaluate our interactions with AI systems in a similar way to our human-to-human interactions (e.g. their warmth/friendliness and competence/intelligence). As general acceptance of generative AI systems is low in the general population, we are seeking how to create trustworthy generative AI systems that might be used by health consumers for easily accessible health information. This is important as AI tools become more advanced, with greater ability to provide accurate information from curated sources. We believe that elevated perceived competence and high warmth in the agents conversational style will elevate the trust and intention to use the agent in participants.
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Trial website
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Trial related presentations / publications
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Public notes
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Contacts
Principal investigator
Name
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Dr Julie Ayre
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Address
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Edward Ford Building, A27 Fisher Rd, University of Sydney, NSW, 2050
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Country
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Australia
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Phone
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+61 02 93517788
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Fax
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Email
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julie.ayre@sydney.edu.au
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Contact person for public queries
Name
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Julie Ayre
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Address
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Edward Ford Building, A27 Fisher Rd, University of Sydney, NSW, 2050
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Country
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Australia
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Phone
140955
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+61 02 93517788
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Fax
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Email
140955
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julie.ayre@sydney.edu.au
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Contact person for scientific queries
Name
140956
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Julie Ayre
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Address
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Edward Ford Building, A27 Fisher Rd, University of Sydney, NSW, 2050
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Country
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Australia
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Phone
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+61 02 93517788
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Fax
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Email
140956
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julie.ayre@sydney.edu.au
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Data sharing statement
Will the study consider sharing individual participant data?
No
What supporting documents are/will be available?
No Supporting Document Provided
Results publications and other study-related documents
Documents added manually
No documents have been uploaded by study researchers.
Documents added automatically
No additional documents have been identified.
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