Fostering AI literacy: A teaching practice reflection
Open accessAbstract
This paper provides the reflections of an educator regarding the pedagogical use of generative artificial intelligence and the need for students to develop artificial intelligence literacy. It starts by providing an overview of generative artificial intelligence and the controversy that surrounds the technology in the contemporary higher education context. This is followed by an argument highlighting that the controversy in the field ignores the pedagogical applications of generative artificial intelligence. Finally, this paper demonstrates an example of how generative artificial intelligence has been used as part of a teaching strategy to help research students develop their artificial intelligence literacy. The paper argues that explicit modelling by educators can help students develop an understanding of generative artificial intelligence as a tool to improve their learning.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Approaches to fostering AI literacy competencies tend to revolve around student-centered learning or flipped classroom approaches, open and transparent communication practices, and hands-on experience using relevant AI tools.
“Approaches to fostering these sets of competencies tend to revolve around a few key ideas: student-centered learning or fl ipped classroom approaches (Kong et al., 2022), open and transparent communication practices, and ' hands-on ' experience using relevant AI tools (Chan, 2023a; Pretorius, 2023).”
From Artificial Intelligence for Academic Purposes (AIAP): Integrating AI literacy into an EAP module (2025) · cites this paper as Pretorius, 2023
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
No references could be resolved yet.
Research works printed but not linked: 7
- AAIN Generative AI Working Group. (2023). AAIN generative artificial intelligence guide- lines. Australian Academic Integrity Network. https://doi.org/10.26187/sbwr-kq49 · DOI
- Cahusac de Caux, B., Pretorius, L., & Macaulay, L. (2023). Research and teaching in a pan- demic world: The challenges of establishing academic identities during times of crisis. Springer. https://doi.org/10.1007/978-981-19-7757-2 · DOI
- Cutri, J., Abraham, A., Karlina, Y., Patel, S. V., Moharami, M., Zeng, S., Manzari, E., & Preto- rius, L. (2021). Academic integrity at doctoral level: The influence of the imposter phe- nomenon and cultural differences on academic writing. International Journal for Edu- cational Integrity, 17, 1-16. https://doi.org/10.1007/s40979-021-00074-w · DOI
- Dai, W., Lin, J., Jin, F., Li, T., Tsai, Y., Gasevic, D., & Chen, G. (2023). Can large language models provide feedback to students? A case study on ChatGPT. EdArVix Preprint. https://doi.org/10.35542/osf.io/hcgzj · DOI · Paper page
- Dawson, P. (2021). Defending assessment security in a digital world: Preventing e-cheating and supporting academic integrity in higher education. Routledge. https://doi.org/10.4324/9780429324178 · DOI · Paper page
- Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2), Article 100790. https://doi.org/10.1016/j.ijme.2023.100790 · DOI · Paper page
- Lund, B. D., & Wang, T. (2023). Chatting about ChatGPT: How may AI and GPT impact aca- demia and libraries? Library Hi Tech News, Preprint. https://doi.org/10.2139/ssrn.4333415 · DOI
Cited by
Sorted by citations
In search of artificial intelligence (AI) literacy in teacher education: A scoping review
Sperling, K., Stenberg, C.-J., McGrath, C., Åkerfeldt, A., Heintz, F., & Stenliden, L. (2024) · Computers and Education Open · doi:10.1016/j.caeo.2024.100169 · Cited by 498 · see citing works
ChatGPT in Teaching and Learning: A Systematic Review
Ali, D., Fatemi, Y., Boskabadi, E., Nikfar, M., Ugwuoke, J., & Ali, H. (2024) · Education Sciences · doi:10.3390/educsci14060643 · Cited by 146 · see citing works
Artificial Intelligence for Academic Purposes (AIAP): Integrating AI literacy into an EAP module
Ngo, T. N., & Hastie, D. (2025) · English for Specific Purposes · doi:10.1016/j.esp.2024.09.001 · Cited by 72 · see citing works
- Authors
- Pretorius, L.
- Affiliations
- Monash University (AU)
- Corresponding author
- Pretorius, Lynette
- ORCID
- Pretorius, Lynette
- Venue
- Monash University
- Publisher
- Monash University
- Published
- 2023
- ISSN
- 1835-5196 (ISSN-L)
- OpenAlex
- W6927223920
- Cited by
- 17
- References
- 0
- Citations by year
- 2025: 9 · 2024: 6 · 2023: 2
- FWCI
- 3.08
- Type
- journal article
- Language
- en
- Open access
- diamond
- journal.aall.org.au
- Locations
- Journal of academic language and learning (publishedVersion)
- Monash University Research Portal (Monash University) (submittedVersion, cc-by-nc-nd)
- Figshare (submittedVersion, cc-by-nc-nd)
- Monash University
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Digital literacy in education
- Keywords
- AI literacy, generative AI, AI in teaching, university teaching, research students, teaching methods
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
