There is a broad agreement among EFL teachers and learners on the value of multimodal competence: the ability to understand and produce meaning across visual, auditory and textual modes. Yet delivering high-quality, pedagogically relevant visual creation in the language classroom is often difficult. Many students lack the design skills or time required to produce convincing visual artefacts, and teachers struggle to support and assess multimodal production within curriculum constraints. In this context, Leonardo AI offers a practical and innovative affordance: it enables teachers and learners to generate high-quality images and short animations from textual prompts, lowering the technical barrier to visual storytelling and poster production in EFL settings.
Leonardo AI is a generative image and video platform that transforms written prompts into original visual content (Leonardo AI, n.d.). The system supports style and aspect-ratio controls, upscaling, iterative prompt editing, and basic canvas editing—features that allow users to refine outputs for pedagogical purposes. Teachers can scaffold tasks by modelling prompt language, supplying key vocabulary items, and guiding learners through iterative refinements to achieve clearer visual representations. While Leonardo AI offers a free entry tier, advanced features such as higher resolution exports, private model training, or batch generation may require paid subscriptions.
The pedagogical potential of Leonardo AI in the EFL classroom is significant. First, it democratizes visual production: students who lack drawing skills can still visualise complex scenarios (for example, an urban plan for a sustainable Dream City), focusing their cognitive energy on language use rather than technical execution. Second, image generation naturally invites rich language work — descriptive lexis, comparative structures, and evaluative discourse — as learners negotiate prompt wording and interpret AI outputs. Third, the tool supports AI literacy: iterative prompt-editing provides a concrete context for students to observe how language choices affect machine outputs and to practise critical appraisal of automated artefacts. As EdTechFocus (n.d.) suggests, such activities help learners develop the digital and cognitive competencies needed to interact constructively with automated systems.
From the standpoint of the SAMR-AI model, Leonardo AI often moves activities beyond simple substitution. At the Substitution level it replaces manual drawing tools; at Augmentation it provides immediate visual feedback that supports revision; at Modification it changes task design by enabling rapid multimodal prototyping and peer review; and at Redefinition it enables projects that were previously impractical—such as student-designed AI exhibitions or community campaigns combining student texts and AI imagery (Gillespie, 2022; EdTechFocus, n.d.). Crucially, teachers should plan tasks that foreground pedagogical aims (language practice, critical reflection) rather than novelty effects.
Despite its affordances, Leonardo AI raises important pedagogical and ethical concerns that teachers must address. Generative models may reproduce cultural biases, generate misleading or stereotyped imagery, or fail to represent marginalized perspectives accurately; therefore, outputs should always be subject to critical interrogation (UNESCO, 2023). Overreliance on AI for creative production can also diminish learners’ agency if the tool is used as a passive generator rather than as a collaborative partner; teachers should structure tasks that require students to edit, justify and contextualize AI artefacts. Finally, privacy and copyright considerations must be observed when students upload proprietary text or when generated images are shared publicly.
Example classroom task (brief): In a lesson on Raising Environmental Awareness, learners draft a short campaign slogan and a 30–40 word descriptive prompt (e.g., “A bright, hopeful poster showing a coastal town cleaning beaches, community volunteers collecting plastic, sunrise lighting, realistic, 16:9”). Students then generate multiple images with Leonardo AI, select the most effective version, and write a short justification (100 words) explaining how specific visual elements support their persuasive message. The follow-up includes peer critique focusing on accuracy, representation, and possible bias, and a teacher-led reflection on ethical use and attribution.
Conclusion: Leonardo AI can be an excellent addition to the EFL teacher’s toolkit when integrated with clear learning intentions and critical scaffolding. It reduces technical barriers to multimodal production, fosters descriptive and evaluative language practice, and offers tangible opportunities for AI literacy. However, its classroom use must be mediated by teacher judgement and ethical frameworks to avoid uncritical acceptance of AI output and to ensure that learners remain active co-creators.
Dudeney, G., & Hockly, N. (2007). How to Teach English with Technology. Pearson.
EdTechFocus (n.d.). SAMR-AI. https://edtechfocus.com/samr-ai.html
Gillespie, R. (2022). SAMR: The Power of a Useful Technology Integration Model. Technology and the Curriculum: Summer 2022. https://pressbooks.pub/techcurr20221/chapter/samr/
Leonardo AI (n.d.). Leonardo: Generative art platform. https://app.leonardo.ai/
UNESCO (2023). Guidance for Generative AI in Education and Research. https://unesdoc.unesco.org/ark:/48223/pf0000386693