Fostering AI literacy in higher education: students’ multimodal prompting
Submitted by:
Sylvana Sofkova Hashemi
Abstract:
Generative AI (GenAI) is increasingly appealing to students for academic writing supporting source summarization, translation, automated text evaluation, and personalized feedback, potentially fostering critical engagement with literature and content (Rasul et al., 2024). The cognitive offloads of GenAI are, however, considered disrupting students’ learning processes in relation to understanding (interpreting, summarizing) and evaluating of academic texts (Anson, 2024). Research emphasizes the importance of crafting, refining, and iteratively optimizing precise prompts to generate desired outputs from GenAI (Kim et al, 2025). Also, GenAI is primarily text-based but capable of generating multimodal outputs that requires learners to develop multimodal transposition skills crafting effective prompts to translate between modes (Kalantzis & Cope, 2024).
This study examines students’ prompting and their reflective discussions on experiences of GenAI in their study practices in higher education:
1. What are their experiences of generative AI?
2. What prompting strategies they demonstrate?
3. What are their reflections on their emerging AI-literacy?
The study invited international master’s students at a Swedish university (N=28) in collaborative workshop promoting active participation in small groups (N=4/group) crafting prompts to create and iteratively revise a narrative or a poem of their choice and asking GenAI also for creating a relevant image to the AI-generated text.
The analysis revealed pronounced differences in experiences and varying ways
of GenAI use for summarizing course literature in schemes and matrices, prompting explanations on concepts and other content and diverse strategies for feedback. During the generation of narratives/poems, some students applied polite and respectful prompting (please, thank you) to increase the likelihood of getting a helpful response. In their group reflections, they emphasized the importance of developing AI-literacy as skills to “recognize AI” in texts, reflecting on “AI’s strengths and limitations” and realizations about how meanings are shaped and constrained by various systems, policies and algorithms.
Key observations reveal a tension between the potential and limitations of GenAI, with students describing a transitional state in higher education as being “trapped” by technology in terms of “doing prompting”. The students highlighted also issues of transparency and trust, and reliance on individual teachers’ decisions about AI integration in academic courses.
Anson, D. W. J. (2024). The impact of large language models on university students’ literacy development. Higher Education Research & Development, 43(7), 1465–1478. https://doi.org/10.1080/07294360.2024.2332259
Kalantzis, M., & Cope, B. (2025). Literacy in the Time of Artificial Intelligence. Reading Research Quarterly, 60(1), 1-34. https://doi.org/10.1002/rrq.591
Kim, J., Yu, S., Lee, S.-S., & Detrick, R. (2025). Students’ prompt patterns and its effects in AI-assisted academic writing: Focusing on students’ level of AI literacy. Journal of Research on Technology in Education, 1–18. https://doi.org/10.1080/15391523.2025.2456043
Rasul, T., Nair, S., Kalendra, D., et al. (2023). The role of ChatGPT in higher education: Benefits, challenges, and future research directions. Journal of Applied Learning and Teaching, 6(1), 41–56. https://doi.org/10.37074/jalt.2023.6.1.29