Leonardo AI

At the moment, educators are searching for innovative methods to connect with digital-native students: there is an emerging trend in the use of tools like Google Classroom, Kahoot, Edmodo, Mentimeter, Canva, and even artificial intelligence tools like chatbots or image/video generating AI. However, there is also a growing concern over the fact that “technological innovations need to be transformed from tools of obsession into tools of education” (Wedlock & Growe, 2017, p. 26). In other words, how can we make sure that we as educators are incorporating these tools because they possess educational value and not just because of that trend. In this article we will explore how a digital AI tool like Leonardo AI can be used in a lesson while tackling its educational benefits and challenges.

Leonardo AI is an image and video generator launched in 2022 that can create high-quality visual assets (Eghosa, 2025). It has a wide variety of features ranging from simple image and video generation to upscaling, canvas edition, modelling and training. Nevertheless, according to Silajdzic (2025), it is a well-designed and guided workspace intuitive and feature rich for both beginners and professionals. In the following tutorial the use and features of the tool is explained: 

This AI tool seems to offer many benefits to the language classroom. For example, it can provide opportunities for learners to co-create audiovisual content: the AI providing output and options for human selection, and the human evaluating the AI output and augmenting it with creativity, experience and critical understanding, which is what Jain & Samuel (2025) call the level of “co-curation” in the reconceptualised Bloom’s Taxonomy. This reconceptualised Bloom's Taxonomy is a framework in which the traditional hierarchical, linear model of cognitive skills is replaced by a network of interconnected cognitive nodes, reflecting how human learners and generative-AI tools co-pilot the learning process (Jain & Samuel, 2025). It introduces two new cognitive levels (“Ventriloquising” and “Co-curating”) and a new capability labelled “Critical Understanding”, thus aligning this new taxonomy with the dynamics of AI-enhanced educational environments. Plus, according to Romrell (2014), the possibility of using the learners’ own devices to access the AI tool allows for a kind of learning (called the mLearning Framework) which is personalized, situated and connected: It is personalized because devices are owned and customized by learners, creating a personal learning experience; situated since portability allows learning to happen in real-world contexts; and connected because there is instant access to information, people and community (Romrell, 2014). However, it will effectively pose a series of challenges for both educators and learners. According to Bekiaridis (2024), the Digital Competency Framework for Educators (or DigCompEdu for short) is a systematic reference framework for educators’ digital competence, structured around six key areas (Professional Engagement; Digital Resources; Teaching & Learning; Assessment; Empowering Learners; Facilitating Learners’ Digital Competence). For example, the framework's area of Learner Empowerment guides teachers and learners to overcome challenges like understanding how AI works and being aware of its biases, understanding the ethical dimensions of AI like in privacy or fairness or interpreting AI results critically; all of which will probably appear when this AI tool is used in the classroom.

By way of illustration, we propose a unit of work, and then, a sample lesson to apply these concepts:

In the following lesson, we propose integrating the use of Leonardo AI in the classroom making use of the mentioned benefits while tackling its challenges: 

In conclusion, Leonardo AI (like many other AI tools) is an excellent addition to the language classroom that can enhance activities making them more appealing to the digital-native students at the same time that it can transform classroom dynamics. Nevertheless, its use show be approached with wariness, since the challenges it may pose should be taken into account. 

REFERENCES

Bekiaridis, G. (2024). Supplement to the DigCompEdu Framework. AI Pioneers. https://aipioneers.org/wp-content/uploads/2024/01/WP3_Supplement_to_the_DigCompEDU_English.pdf 

Eghosa, F. (2025). Leonardo AI vs Midjourney: Which is the Best AI Art Generator in 2025? Techpoint Africa. https://techpoint.africa/guide/leonardo-ai-vs-midjourney/ 

Jain, J. & Samuel, M. (2025). Bloom Meets Gen AI: Reconceptualising Bloom's Taxonomy in the Era of co-piloted Learning. ResearchGate. https://doi.org/10.20944/preprints202501.0271.v1 

Romrell, D. (2014). The SAMR Model as a Framework for Evaluating mLearning. https://www.researchgate.net/publication/264549561_The_SAMR_Model_as_a_Framework_for_Evaluating_mLearning

Silajdzic, M. (2025). Leonardo AI Review: Is It Worth it? Cybernews. https://cybernews.com/ai-tools/leonardo-ai-review/ 

Wedlock, B. C. & Growe, R. (2017). The Technology Driven Student: How to Apply Bloom’s Revised Taxonomy to the Digital Generations. Journal of Education and Social Policy. https://jespnet.com/journals/Vol_4_No_1_March_2017/4.pdf