Let's use Bing in Class

B1 English Lesson Plan: Crafting a Travel Guide with AI

Unit of Work: "Traveling the World"

Target Group: B1 (Intermediate) English learners.

Lesson Title: My AI-Powered Travel Guide

Time Allotted: 90 minutes

Lesson Objectives

By the end of this lesson, students will be able to:

• Use a generative AI tool (Bing) to create and edit a simple travel guide.

• Practice and apply vocabulary related to travel, attractions, and local cuisine.

• Work collaboratively to produce a creative digital text.

• Understand and evaluate AI-generated content critically.

Lesson Procedure

1. Warm-Up (10 min)

• Topic Activation: The teacher asks students to think about their dream travel destination and discuss their ideas in pairs.

• Elicit Key Vocabulary: The teacher writes key vocabulary on the board as students share their answers (e.g., attractions, landmarks, cuisine, accommodation).

2. Introduction to Generative AI (15 min)

• Discussion: The teacher explains what generative AI is and how it can be used to create content.

• Teacher Demonstration: The teacher models how to use Bing to generate a simple text about a chosen city, such as a paragraph about Paris, demonstrating how to use a clear prompt.

3. Collaborative Content Creation (40 min)

• Task Introduction: Students work in pairs to create a short digital travel guide for their dream destination. The guide should include a description of the place, a list of three main attractions, and a few suggestions for local food.

• AI as a "Co-pilot": Students use the AI tool (Bing) to generate a draft with a prompt like, "Create a short travel guide for [city name] for B1 English learners."

• Modification and Refinement: This is the core of the activity. Students do not simply copy the AI's text. They are tasked with:

  • Correcting any grammatical errors.

  • Replacing generic vocabulary with more specific or personal language.

  • Adding new information or a section that the AI didn't include.

  • Collaborating on a shared document to perform real-time peer editing.

4. Presentation and Feedback (15 min)

• Show and Tell: Each pair presents their finalized travel guide to the class.

• Peer Evaluation: Students give constructive feedback, focusing on the content and language used.

5. Wrap-Up and Reflection (10 min)

• Individual Reflection: Students write a short paragraph answering questions like: "Was the AI helpful? What was the hardest part about editing its work? What are the benefits and risks of using AI in this way?"

• Class Discussion: The teacher facilitates a discussion based on the students' reflections, reinforcing the main learning outcomes and the importance of critical thinking when using AI.

The lesson plan is built on three key educational frameworks: the SAMR Model, Connectivism, and Bloom's Digital Taxonomy. Together, they provide a robust justification for using AI in the classroom.

According to Romrell et. al. (2014), the SAMR model, developed by Ruben Puentedura, is a framework used to classify how technology is integrated into learning. It organizes technology use into four levels, moving from simple enhancement to complete transformation. The first level, Substitution, involves using technology to replace a traditional tool without any change in function. For example, typing a document on a computer instead of writing it by hand. The next level, Augmentation, uses technology to improve a task with functional enhancements, such as using a word processor with a spell-checker. The third level, Modification, is a more significant step where technology allows the learning task to be redesigned. The final and most transformative level, Redefinition, involves creating entirely new learning activities that were previously impossible without the use of technology.

Choudhuri (2023) adapts the SAMR model to analyze the integration of AI in education, moving from simple replacement to transformative redefinition. The author explains that Substitution occurs when AI automates existing tasks, like generating a worksheet, which saves time but doesn't change core teaching practices. Augmentation enhances content delivery, while Modification begins to reshape teaching methods. The ultimate goal, however, is Redefinition, where AI enables entirely new learning experiences that were previously impossible, such as personalized learning. The author argues that while most current AI tools for educators are stuck at the substitution and augmentation levels, the true potential of AI lies in its ability to redefine education and amplify human capabilities, creating innovative and personalized learning opportunities that address long-standing educational challenges.

The proposed lesson is structured to move learners progressively through the levels of the SAMR model, from basic enhancement to true transformation.

• Substitution: The lesson begins at this level by using the AI to generate an initial text, which substitutes the traditional task of searching for information and writing a draft from scratch. This saves time and provides a baseline for students to work from.

• Augmentation: The lesson quickly moves beyond simple substitution. The AI provides a functional improvement by generating a cohesive, if imperfect, text. However, the students' role is to act as the primary editors, correcting errors and adding their own ideas. This process augments the learning experience by providing a scaffolded entry point for writing.

• Modification: The collaborative element of the lesson is where it fundamentally modifies the learning task. Students work in pairs on a shared digital document, a process that is enabled by technology and transforms the activity from an individual writing assignment into a collaborative peer-editing project.

• Redefinition: The lesson reaches this transformative level by requiring students to engage in an activity that would be impossible without generative AI: the critical evaluation and co-creation of machine-generated text. This a new and essential skill for the modern world, making the activity a form of "redefinition" rather than a mere technological enhancement.

According to Bates (2022), Connectivism is a modern learning theory that addresses how people learn in a digital world where information is constantly changing and distributed across networks. Developed by George Siemens and Stephen Downes, its core idea is that knowledge exists in networks and is not solely held by individuals. This means that learning is about making and maintaining connections between different nodes, which can be people, systems, or databases. The theory emphasizes that the network itself is more important than the content it holds because knowledge is dynamic, continuously evolving as connections are formed, broken, or changed. Consequently, organizations and individuals must connect to external information flows to stay current, rather than trying to contain knowledge internally.

• Knowledge in Networks: The proposed lesson positions the generative AI as a "non-human appliance" or a node in a vast network of information. Students are not expected to have all the knowledge in their heads; rather, they learn how to access, use, and critically interact with this external knowledge source.

• Learning as Navigation: The core learning activity is not about memorizing facts but about "navigating" the AI's output. Students must learn to prompt the AI effectively, evaluate its response, and make connections between the AI's information and their own knowledge, embodying the connectivist principle that "the capacity to know more is more critical than current knowledge."

• Decentralized Learning: The lesson breaks from the traditional model where the teacher is the central authority. Instead, the teacher's role is to facilitate the students' interaction with the AI and with each other, creating a learning environment where knowledge and skills emerge through decentralized connections.

An article by TeachThoughtStaff (2025) titled 126 Digital Learning Verbs Based on Bloom’s Taxonomy introduces a list of 126 "digital learning verbs" that align technology-based tasks with the six cognitive levels of Bloom’s Taxonomy: Remember, Understand, Apply, Analyze, Evaluate, and Create.

The purpose of this framework is to help educators intentionally integrate technology into their lessons, ensuring it is a tool for purposeful thinking rather than just a source of busywork. By using this list, teachers can align digital activities—from blogging and video production to AI prompting—with specific learning goals and make the thinking behind the digital work "visible and intentional."

The article organizes the verbs into the following categories:

• Remembering: Verbs related to recalling information, such as Googling, searching, prompting (e.g., ChatGPT), and curating.

• Understanding: Verbs that involve grasping concepts, including annotating, summarizing, comparing, and tweeting.

• Applying: Verbs for using knowledge in a new situation, like loading, streaming, preparing, and presenting.

• Analyzing: Verbs for breaking down information into parts, such as calculating, deconstructing, mind-mapping, and strategic hyperlinking.

• Evaluating: Verbs that involve making judgments, including debating, criticizing, moderating, and rating.

• Creating: Verbs for producing something new, such as blogging, podcasting, filming, and wiki building.

The proposed lesson design intentionally aligns with Bloom's Revised Taxonomy, ensuring students engage in higher-order thinking skills through their interaction with the AI.

• Lower-Order Skills (Remember/Understand): The initial stage of prompting the AI for information handles these basic cognitive tasks. Students are engaging in digital skills such as "prompting" and "curating" information, which form the base of the learning pyramid.

• Higher-Order Skills (Analyze/Evaluate): The most critical part of the lesson focuses on these skills. Students must actively analyze the AI's output for accuracy and tone. They then evaluate the content and decide what to keep, what to change, and what to remove. This critical thinking is paramount, as it teaches students to be discerning consumers of information in the digital world.

• Highest-Order Skill (Create): The final product—the student-edited travel guide—is a form of "co-curation," the highest level of thinking in the Bloom meets Gen AI framework. By blending their own knowledge and creativity with the AI's output, students are producing something new and original, moving beyond simple information retrieval to a truly creative act.

Bates, A. (2022) Teaching in a Digital Age: Third Edition. Chapter 2.6  Connectivism.

https://pressbooks.pub/teachinginadigitalagev3m/chapter/3-6-connectivism/ 

Choudhuri, S. (2023). SAMR and AI: Don’t get stuck on substitution.  https://www.flintk12.com/blog/samr-and-ai-dont-get-stuck-on-substitution 

Romrell, Kidder, Wood. (2014). The SAMR Model as a Framework for Evaluating mLearning. 

TeachThoughtStaff. (2025, May 28). Bloom’s Digital Taxonomy Verbs: 100+ Examples for Technology-Rich Teaching. TeachThought. https://www.teachthought.com/critical-thinking-posts/blooms-digital-verbs/ 

Notes:

• All the work has been checked against language mistakes with Gemini: Gemini Advanced [Large language model]. Retrieved November, 7, 2025, from https://gemini.google.com 

• The image in the lesson plan was taken from pexels (free of copyright).

• The images from the theoretical justification are part of the articles cited in the references.

• SAMR Model image taken from: Redirect Notice. (2025). Google.com. https://www.google.com/url?sa=i&url=https%3A%2F%2Ftheowlteacher.com%2Fall-about-the-samr-model%2F&psig=AOvVaw07aUDGbwXUojqLPZVO1aDl&ust=1762618717605000&source=images&cd=vfe&opi=89978449&ved=0CBUQjRxqFwoTCMj6n8i44JADFQAAAAAdAAAAABAE