AI Literacy Checkpoints for Safe and Trustworthy AI-Supported Learning

About the research project

Generative artificial intelligence (GenAI) tools such as ChatGPT, Gemini, and Llama-based models are increasingly integrated into everyday learning and teaching practices. Students and educators now rely on these systems for drafting, summarising and explaining complex material. While these technologies offer clear benefits for accessibility and productivity, they also introduce significant risks for factual accuracy, academic integrity, and privacy when AI outputs are accepted uncritically. Large language models can produce factual inaccuracies, fabricated citations and privacy-sensitive content, and many users struggle to detect these issues without structured support. Existing AI literacy initiatives raise awareness of these risks but provide limited guidance within the actual workflows where AI is used. This project aims to design, develop and evaluate mechanisms that help users verify AI-generated content before incorporating it into academic work. The focus is on creating practical, embedded checkpoints that support accuracy checking, citation verification, privacy awareness and critical reflection during AI-assisted learning activities.

• Cetindamar, D., Kitto, K., Wu, M., Zhang, Y., & Knight, S. (2024). Explicating AI literacy of employees at digital workplaces. IEEE Transactions on Engineering Management, 71(2), 585–598.
• DeVerna, M. R., Yan, H. Y., Yang, K.-C., & Menczer, F. (2024). Fact-checking information from large language models can decrease headline discernment. Computers in Human Behavior, 152, 107298.
• Li, H., Huang, J., Ji, M., Yang, Y., & An, R. (2025). Use of retrieval-augmented large language model for COVID-19 fact-checking: Development and usability study. Journal of Biomedical Informatics, 149, 104456.
• Liu, J. H. (2025). When usefulness fuels fear: The paradox of generative AI dependence and the mitigating role of AI literacy. Telematics and Informatics, 94, 102066.
• Ravi, M., Kaur, K., Wright, C., Bawn, M., & Cutillo, L. (2025). University staff and student perspectives on competent and ethical use of AI: Uncovering similarities and divergences. Higher Education Research & Development, 44(1), 1–20.
• Rheu, M., & Cho, J. (2025). The trap of AI literacy: The paradoxical relationships between college students’ use of LLMs, AI literacy, and fact-checking behavior. Computers & Education: Artificial Intelligence, 6, 100255.
• Uygun, E. (2025). Clashing AIs: Artificial intelligence literacy and academic integrity in an EFL context. Computers & Education, 215, 105123.

Primary supervisor

Meet Doctor Soonja Yeom

Funding

Applicants will be considered for a Research Training Program (RTP) scholarship or Tasmania Graduate Research Scholarship (TGRS) which, if successful, provides:

• a living allowance stipend funded by University of Tasmania of $34,315 per annum for 3.5 years (PhD) or 2 years (Master by Research)
• a relocation allowance of up to $2,000
• a tuition fees offset covering the cost of tuition fees for up to four years (domestic applicants only)

A tuition fee offset may be offered to eligible international applicants following competitive assessment 

As part of the application process you may indicate if you do not wish to be considered for scholarship funding.

Other funding opportunities and fees

For further information regarding other scholarships on offer, and the various fees for undertaking a research degree, please visit our Scholarships and fees on research degrees page.

Eligibility

Applicants should review the Higher Degree by Research minimum entry requirements.

Ensure your eligibility for the scholarship round by referring to our Key Dates.

Additional eligibility criteria specific to this project/scholarship:

• Applications are open to applications from ICT/Computer Science discipline background only.
• English language score must be above minimum entry requirements for this project.

Selection criteria

The project is competitively assessed and awarded. Selection is based on academic merit and suitability to the project as determined by the College.

Additional essential selection criteria specific to this project:

• Demonstrated capacity for critical thinking and independent research.
• Background in subject areas relevant to the project (e.g., ICT, AI/ML, learning analytics, HCI, or educational technology).
• Programming skills and/or experience with data analytics, experiment design, or prototype development.
• Quantitative research skills and an interest in mixed-methods or user-study designs.
• Strong written communication skills and interest in publishing in international venues.

Additional desirable selection criteria specific to this project:

• Prior experience with AI literacy, educational technology, or human–AI interaction research.

Application process

1. Select your project, and check that you meet the eligibility and selection criteria, including citizenship;
2. Contact Doctor Soonja Yeom to discuss your suitability and the project's requirements; and
3. In your application:
   • Copy and paste the title of the project from this advertisement into your application. If you don’t correctly do this your application may be rejected.
   • Submit a signed supervisory support form, a CV including contact details of 2 referees and your project research proposal.
4. Apply prior to 1 October 2026.

Full details of the application process can be found under the ' How to apply ' section of the Research Degrees website.

Following the closing date applications will be assessed within the College. Applicants should expect to receive notification of the outcome by email by the advertised outcome date.

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