Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Education and Culture Executive Agency (EACEA). Neither the European Union nor EACEA can be held responsible for them. Project number: 2023-1-NL01-KA220-HED-000155675. Casus 21: Generatieve kunstmatige intelligentie: implicaties en overwegingen voor de praktijk in het hoger onderwijs General information Reference/Source: Farrelly, T., & Baker, N. (2023). Generative artificial intelligence: Implications and considerations for higher education practice. Education Sciences, 13(11), 1109. https://doi.org/10.3390/educsci13111109 University/HE institution: Munster Technological University Course and subject domain: N/A Aim: To explore the potential impact of generative artificial intelligence on international students and provide recommendations and strategies for educators and policymakers to prioritise ethical AI usage and cultivate AI literacy. Target group: Primarily educators, policymakers, and institutions in Western HE who work with international students and those for whom English is an additional language. Description of case AI tool used: N/A Ng et al.'s AI literacy framework: https://doi.org/10.1016/j.caeai.2021.100041 Hillier's AI literacy framework: https://teche.mq.edu.au/2023/03/a-proposed-ai-literacy-framework/ A detailed description of what happened: A comprehensive review of academic articles, books, and reports focused on the use of AI in HE, analysing the opportunities and challenges of integrating AI approaches in the classroom, and identifying potential solutions to ensure ethical and inclusive AI practices. Lessons learned N/A Implications for practice The study highlights the potential benefits and limitations of current AI approaches in the classroom, including the ethical implications, linguistic, and cultural contexts. It is recommended that future research should prioritise promoting AI literacy, developing ethical AI guidelines and policies, and identifying and addressing biases in AI algorithms and models for equitable and responsible AI practices in higher education institutions.
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