AI, Stories, and the Distributed Mind: Building A Distributed Learning Chasing Time English Lesson Plan
Mia Tarau
In last week’s blog post, Michael Rabbidge started a new conversation among our team of educators by looking at leveraging AI in the language learning classroom in the context of distributed learning (https://chasingtimeenglish.com/posts/2025/6/3/ai-stories-and-the-distributed-mind-rethinking-language-learning). Distributed learning acknowledges the diverse environments and tools that frame and encourage language learning. Today, I draw on that introduction to suggest a set of language learning tasks where students use AI to dive deeper into character stories, actions, and mindsets. I anchor these tasks in one of our original series that international students can relate to very closely: My Name Is Lucky. Let’s begin!
My Name Is Lucky – The Series
We follow Lucky, and international student living with a host family and studying English in New Zealand, as she navigates study, friendships, love, heartbreak, and difficult choices.
My Name Is Lucky – Distributed Learning Activities
1. “Rewrite the Scene” – Using AI to Explore Register and Tone
Level: Intermediate
Focus: Grammar, register, writing, and editing
Tools: Chasing Time English episode + GenAI platform (e.g. ChatGPT)
Learning Outcome: Developing awareness of tone and register; building editing skills through reflection and contrast
Activity Flow:
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Students watch a key scene from My Name Is Lucky – suggested strong scenes: Lucky meeting Ryan; Lucky seeing Ryan and her best friend together after the gig
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In pairs, they summarise the scene.
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Then, using GenAI, they rewrite the scene for a different audience or genre (text message to a classmate as a ‘witness’ to the scene, TikTok video voiceover).
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Students compare the scene script and their summaries to the AI generated rewrites and reflect on:
-tone
-vocabulary
-grammar choices
2. “Debate the Character’s Choice” – Real-Time Roleplay with AI Support
Level: Upper-Intermediate
Focus: Speaking, argumentation, vocabulary
Tools: CTE episode + GenAI + prompt cards
Learning Outcome: Learners practice oral fluency and build confidence using AI as a rehearsal/feedback tool.
Activity Flow:
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Students choose a controversial character decision from the episode – suggested scenes: Lucky’s best friend chatting alone with Ryan after his gig; Lucky ‘s difficult decision after seeing them together
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Thinking about the entire series, but also about what international students might experience these moments in their daily lives overseas, students debate in class using their notes: one team supports the character’s actions, the other argues against it
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In groups, students prepare for a debate. Each student can consult a GenAI platform of their choice to help them generate key phrases, expressions, and arguments.
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Students can also be provided with prompt cards to help them frame their debate arguments – examples:
v Ryan was right to keep the secret.
v Friends should always be honest with each other, no matter what.
v Hiding information can be justified if it protects someone.
v Lucky’s decision was selfish.
v Lucky’s actions after the gig were necessary.
v Ryan’s actions were wrong.
v Lucky should have asked for clarification instead of running out without an explanation.
v If you were in the same situation as Lucky, you would have done the same thing.
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Wrap up with AI-assisted feedback (e.g. "Rephrase this argument in more formal English").
3. “Ask the Character” – Embodied Perspective Taking with AI Roleplay
Level: Intermediate
Focus: Speaking, critical thinking, questioning
Tools: GenAI, set to act as the character
Learning Outcome: Enhances empathy, comprehension, and critical thinking through enacted and embedded interaction.
Activity Flow:
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In pairs or small groups, students generate questions to ask a character from the series (examples: “Why did you lie to Lucky?” or “What would your family say if you gave up?” or “Ryan, how do you really feel about Lucky?”).
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Students use GenAI to simulate a Q&A where GenAI plays the character.
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Students record the answers and – in their pairs or small groups - discuss whether they agree with the AI's character interpretation.
What Makes These Learning Tasks “Distributed”?
They are embodied: Learners interact through speech, movement, and creativity.
They are embedded: Tasks are anchored in story and context.
They are enacted: Language is used in meaningful ways (debates, roleplay).
They are extended: Learning is shared across tools (AI), peers, and tasks.
We hope that if you are working with our series, you will enjoy trying these distributed learning tasks – or that they inspire new ideas in your own context – and, as always, we would love to hear from you on how the lesson went!