Academic Scientific JournalsImproving AI literacy as part of the training of future teachers
DOI:
https://doi.org/10.26881/ae.2025.22.02Keywords:
artificial intelligence, teaching, media literacy, multiliteracy, academic didacticsAbstract
This article addresses the issue of training (future) English language teachers to make judicious use of artificial intelligence in education. Defined as a competency cluster – multiliteracy, or, in effect, a set of AI-related skills – this topic was the subject of a university course, followed by a questionnaire survey conducted amongst the participants. The study was small-scale, and its respondents were exclusively course participants, as the primary aim of the study described here was to evaluate the effectiveness of the classes, particularly in terms of students’ acquisition of higher-level competences within the aforementioned multi-literacy framework, especially ethical sensitivity and critical reflection. The results of the study show that both of these components are present in the attitudes of the course participants in an individualised manner. This article refers to these results in the form of a reflection on AI-assisted academic teaching and should be read from this perspective.
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References
Chakravorti B., 2024, AI’s trust problem: Twelve persistent risks of AI that are driving skepticism, „Harvard Business Review”, nr 3.
Cukurova M., Miao F., 2024, AI competency framework for teachers, Paryż: UNESCO Publishing.
DeStefano D., LeFevre J.A., 2007, Cognitive load in hypertext reading: A review, „Computers in Human Behavior”, nr 3 (23), s. 1616–1641.
Godase V., 2025, Comprehensive Review on Explainable AI to Addresses the Black Box Challenge and Its Role in Trustworthy Systems, [w:] Artificial Intelligence Education and Innovation, Pune: Sinhgad College of Engineering, s. 127–132.
Mills K., Ruiz P., Lee K.W., Coenraad M., Fusco J., Roschelle J., Weisgrau J., 2024, AI literacy: A framework to understand, evaluate, and use emerging technology, Digital Promise (online), https://doi.org/10.51388/20.500.12265/218.
Mollick E., 2023, Centaurs and cyborgs on the jagged frontier. One Useful Thing, [Substack] www.oneusefulthing.org/p/centaurs-and-cyborgs-on-the-jagged [dostęp: 15.08.2025].
Mollick E.R., Mollick L., 2023, Assigning AI: Seven Approaches for Students, with Prompts, „The Wharton School Research Paper” (online), http://dx.doi.org/10.2139/ssrn.4475995.
Pegrum M., 2009, From Blogs to Bombs: The Future of Digital Technologies in Education, Crawley: UWA Publishing.
Pegrum M., 2014, Mobile Learning: Languages, Literacies and Cultures, Londyn: Palgrave Macmillan.
Pegrum M., 2025, From revolution to evolution: What generative AI really means for language learning, „Language Teaching”, t. 59, nr 2 (online), s. 180–196, https://doi.org/10.1017/S0261444825000151.
Salmerón L., Canas J.J., Kintsch W., Fajardo I., 2005, Reading strategies and hypertext comprehension, „Discourse Processes”, nr 3 (40), s. 171–191.
Thompson C., 2013, Smarter Than You Think: How Technology is Changing Our Minds for the Better, Nowy Jork: Penguin.
Turula A., 2024, Cyfrowy nauczyciel języka angielskiego. O kształceniu przyszłych nauczycieli do mądrego używania technologii informacyjno-komunikacyjnych, Kraków: Wydawnictwo Naukowe UKEN.
Von Eschenbach W.J., 2021, Transparency and the black box problem: Why we do not trust AI, „Philosophy & technology”, nr. 4 (34), s. 1607–1622.
