Sample Lesson Plan Using Quillionz

Theme: Raising Environmental Awareness

Age Group & Language Level: 16-year-old, B1-level students, state-run school

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

Identify main ideas and details in environmental texts.
Generate comprehension questions using AI.
Evaluate and improve AI-generated questions for accuracy and clarity.
Discuss the role of AI in promoting environmental awareness.
Reflect critically on the reliability and ethics of AI-generated educational content.

Materials: Interactive board, traditional board, students’ mobile devices or laptops.

Warmer:

The teacher begins by asking students whether they have ever participated in or seen environmental awareness campaigns online or on social media. After a brief discussion, the teacher displays a short infographic or video about pollution and climate change, prompting students to identify some key environmental problems mentioned.

Then, the teacher explains that the class will learn how to create comprehension questions about environmental issues — but instead of writing them manually, they will use an AI tool called Quillionz to help generate them.

Web:

The teacher asks students to form groups of three or four. Each group receives a short environmental text — for example, an article about plastic waste or renewable energy sources.

Each group reads the text and identifies the main ideas and key vocabulary.
Then, the teacher guides students to access Quillionz using their mobile devices.
Students copy and paste the environmental text into Quillionz and click Generate Questions. The AI produces different types of questions (e.g., multiple choice, short answer, true/false).
Students review all the generated questions, discussing in their groups which are accurate, useful, or need correction.
Each group edits and rewrites the questions that contain mistakes or ambiguous wording, adapting them to the B1 language level.
Once finished, the groups exchange their improved question sets and quiz each other.

Finally, students submit their revised question sets to the class platform (Google Classroom, Padlet, etc.) to create a shared resource bank of comprehension quizzes on environmental topics.

What’s Next:

The teacher displays one group’s set of questions on the interactive board and asks the class to reflect:

How accurate were the AI-generated questions?
Were any of them misleading or too difficult?
Do you think AI can help teachers save time?
Can AI be trusted to understand complex issues like the environment?

After a short class discussion, the teacher concludes by emphasizing that while AI can be a valuable assistant, human critical thinking and contextual understanding are irreplaceable. Students then write a brief reflection paragraph on how they used AI responsibly to promote environmental awareness.

The teacher may evaluate students’ performance using a rubric that assesses collaboration, critical evaluation, and responsible AI use.

Analysis of the Lesson Plan

This lesson reflects Jain & Samuel’s (2025) reconceptualised Bloom’s Taxonomy by positioning learners within a co-piloted cognitive environment where human and AI reasoning intertwine. Students begin by “ventriloquising” AI (accepting its output), then move toward “co-curation” as they edit and refine AI-generated questions, demonstrating critical understanding of how meaning can be constructed collaboratively between humans and technology.

According to Romrell’s (2014) mLearning framework, this activity is personalized, situated, and connected. Students use their own devices (personalized learning), interact with real-world environmental topics (situated learning), and share digital resources (connected learning).

From the SAMR-AI perspective, the lesson moves beyond simple substitution of traditional questioning:

At the Augmentation level, AI enhances question creation by providing instant examples.
At the Modification level, students co-edit and evaluate AI-generated questions collaboratively.
Finally, the Redefinition level is reached as learners critically discuss the ethical use of AI in spreading environmental awareness — an activity that would not be possible without the technology itself.

Aligned with Bekiaridis’s (2024) DigCompEdu Framework, the task promotes digital competence in the areas of Teaching & Learning, Assessment, and Empowering Learners. Students not only employ AI for educational purposes but also reflect on bias, reliability, and the broader implications of AI in learning and communication.

Analysis of the Lesson Plan

This lesson reflects Jain & Samuel’s (2025) reconceptualised Bloom’s Taxonomy by positioning learners within a co-piloted cognitive environment where human and AI reasoning intertwine. Students begin by “ventriloquising” AI (accepting its output), then move toward “co-curation” as they edit and refine AI-generated questions, demonstrating critical understanding of how meaning can be constructed collaboratively between humans and technology.

According to Romrell’s (2014) mLearning framework, this activity is personalized, situated, and connected. Students use their own devices (personalized learning), interact with real-world environmental topics (situated learning), and share digital resources (connected learning).

From the SAMR-AI perspective, the lesson moves beyond simple substitution of traditional questioning:

At the Augmentation level, AI enhances question creation by providing instant examples.
At the Modification level, students co-edit and evaluate AI-generated questions collaboratively.
Finally, the Redefinition level is reached as learners critically discuss the ethical use of AI in spreading environmental awareness — an activity that would not be possible without the technology itself.

Aligned with Bekiaridis’s (2024) DigCompEdu Framework, the task promotes digital competence in the areas of Teaching & Learning, Assessment, and Empowering Learners. Students not only employ AI for educational purposes but also reflect on bias, reliability, and the broader implications of AI in learning and communication.

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

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