Describe possible use cases for GenAI in school settings at the grade level you would like to teach, or describe why it would not be appropriate for your grade level
My goal is to become a French teacher at the secondary level. French as a second language is typically assessed across four core competencies: reading, listening, writing, and speaking. Reflecting on my own experience learning French from kindergarten through Grade 12, I found the process extremely challenging. Many of my teachers were not fluent French speakers themselves, and in some cases, French was their fourth language. As a result, there was often an overemphasis on repetitive spelling tests, under the assumption that frequent written practice alone would lead to proficiency. This approach neglected the other three competencies and limited opportunities for authentic language use. Drawing from these experiences, I see many meaningful ways GenAI could be integrated into today’s French classrooms to better support student learning.
Reading.
GenAI can significantly enhance reading engagement by allowing students to access a wide range of French texts tailored to their interests. Instead of relying on outdated or unengaging articles, students could use GenAI tools to locate texts aligned with their hobbies, current events, or personal interests. Increased relevance and choice would likely improve motivation and reading efficacy, ultimately supporting stronger language development.
Listening.
GenAI also has strong potential to support listening comprehension. Tools such as speech synthesis platforms allow students to convert written texts or assignments into audio format. These tools often offer a variety of voice options, enabling students to select voices they find engaging. Increased personalization can make listening tasks more enjoyable and may encourage students to spend more time engaging with French audio content.
Writing.
Writing is perhaps the competency most transformed by GenAI. When I was in high school, the primary tool available was BonPatron, which provided limited grammatical feedback and capped the number of errors it would identify. Today, platforms such as DeepL and ChatGPT can analyze entire texts, identify errors, and explain why those errors occur. This allows students not only to correct their writing but also to develop a deeper understanding of grammatical structures and language rules, supporting long-term learning rather than surface-level correction.
Speaking.
Speaking is the one area where GenAI use is largely inappropriate. One of the greatest challenges for French teachers is facilitating authentic and meaningful oral communication opportunities. Oral language production must be natural and student-generated in order to accurately reflect proficiency. While students may use AI tools to brainstorm themes or ideas for creative projects such as skits or plays, the speaking itself should remain entirely authentic and free from AI assistance.
This is an example of how I used GenAI to find resources for an assignment and then used it to spell-check my work for grammatical errors. https://docs.google.com/document/d/1P4ljyZ9isvayMdKjKg0_W8EDZmDpwlAOnOvsoEkrc-A/edit?usp=sharing
What are some of the issues around the responsible use of GenAI in education including, the environment, property rights, and learning-related issues?
Prior to Friday’s class, I was largely unaware of the environmental impact of GenAI, particularly its water usage. While I knew that data centres require water for cooling servers, I had not considered the long-term environmental consequences. During class, a statistic was shared comparing the energy use of a large television running for an hour to the water consumption of multiple AI prompts, which prompted me to investigate further.
A CBC research article indicates that in 2023, approximately 10–50 AI prompts consumed around 500 millilitres of water. More recent estimates from 2025 suggest that the same number of prompts may now require the equivalent of one standard water bottle. These statistics were both surprising and concerning, and they have significantly influenced how frequently and intentionally I use AI tools. I also learned that image-based AI generation can consume less water than text-based prompting, which has encouraged me to be more strategic in my use of these technologies.
Moving forward, I plan to reserve GenAI for tasks that genuinely enhance learning rather than for basic skills I can practice independently, such as simple French conjugations. By engaging directly in these foundational tasks, I strengthen my own language proficiency. This aligns with principles from motor learning theory, such as the idea that “neurons that fire together wire together,” emphasizing the importance of continued practice. As a future educator, I believe it is essential to share this knowledge with students so they can develop responsible habits and make informed decisions about their use of AI, particularly in relation to environmental sustainability.

The White Hatter – https://www.thewhitehatter.ca/post/ai-is-thirsty-hungry-the-hidden-environmental-costs-of-artificial-intelligence
CBC – https://www.cbc.ca/news/ai-data-centre-canada-water-use-9.6939684
This Blog Post is an original thought and has been edited for spelling, grammar and fluidity with ChatGPT.
OpenAI. (2026). ChatGPT (GPT-5.2) [Large language model]. https://chat.openai.com/


