The foundation for this assignment (n° 3) is a lesson plan that Giuliana originally developed during her teaching practicum. For this version, I am collaborating with her to redesign the lesson following the principles of digital competence for educators outlined in the DigCompEdu Framework (Redecker, 2017) and the shift from Web 1.0 consumption to Web 2.0 creation described by Dudeney & Hockly (2007).

In its original form, the class—held in a public primary school without access to technological devices—relied entirely on traditional methods, such as the whiteboard, to teach food vocabulary and simple expressions of preference.

This new, adapted version completely transforms the delivery by integrating a digital tool that students can use with their personal phones or tablets, thereby turning a passive reception task into an active content creation experience (Mishra & Koehler, 2006). Students will be guided to incorporate the use of a generative AI tool to draft short dialogues, fostering creativity, research skills, and digital literacy. This ensures that students learn to interact responsibly with AI, a crucial competence in today’s educational environment, while still personalizing and editing their own content before sharing it digitally — a process that aligns with the DigCompEdu Framework, particularly in the areas of Digital Resources and Teaching & Learning (Redecker, 2017).

Later on, the SAMR model (Jackson, N., 2023), AI-TPACK (Ning, 2023), The European Framework for the Digital Competence of Educators (Bekiaridis, 2024), and Bloom’s Digital Taxonomy (Wedlock, 2017) will be used to analyze and justify specific activities in the adapted lesson plan. This analysis will highlight how technology transforms tasks and supports higher-order thinking skills, such as Applying and Creating. Additionally, a theoretical section will be included at the end to provide further insights into how these frameworks inform and enhance the instructional strategies used in the lesson, helping to create a more dynamic and engaging learning experience for students.

By the end of this lesson, students will be able to use digital tools and generative AI to create a short, realistic conversation about food preferences, integrating new and recycled vocabulary. They will have practiced responsible AI use to search for ideas and vocabulary, collaborated with peers to plan their dialogue, and finally produced a digital chat using FakeWhatsApp. This process aims to develop their language production, creativity, and digital literacy, in alignment with the DigCompEdu and TPACK frameworks, and to promote higher-order thinking skills from Bloom’s Digital Taxonomy.

While this activity could be linked to multiple educational frameworks, TPACK (Mishra & Koehler, 2006) fits particularly well here. The activity integrates:

• Content Knowledge (CK): Understanding of food vocabulary, enabling the teacher to select relevant examples and sequence them to support comprehension.

• Pedagogical Knowledge (PK): Use of questioning, elicitation, and oral matching to engage students actively and reinforce understanding.

• Technology Knowledge (TK): Skillful use of Google Slides with gradual word reveals to enhance visibility, focus attention, and create an interactive learning experience that supports the pedagogical goals.

Technology is used strategically to support comprehension and retention, not as a decorative tool.

ON STAGE 3: APPLICATION – Guided AI Research (10 minutes)

At this stage, students will complete simple exercises in their books, such as matching words to images or completing a wordcross, to review the target vocabulary.

As a second step and guided by the teacher (instructions written below), the students will use the following AI generated tool, called Copilot, to generate content that will help them reach their final goal of this lesson plan.

Instructions for Students:

• Work in pairs or small groups.

• Use your computer to open the AI tool posted on Classrrom by the teacher.

Ask for:

• New food items or images related to food.

• Examples of short dialogues about favorite foods in English (4–5 lines).

• Select the most useful words and expressions.

• Write a short dialogue using these ideas. You can start by asking your friend: What is your favorite food?

Here, again, TPACK provides the strongest framework for understanding the design of this stage, as it demonstrates the balanced integration of:

• Content Knowledge (CK) : food vocabulary and expressions of preference, Pedagogical Knowledge (PK): scaffolding and guiding collaborative dialogue writing —, and Technological Knowledge (TK) :using generative AI tools to extend learning beyond the textbook.

In addition, this activity aligns with the DigCompEdu Framework (Redecker, 2017), particularly in the area of Digital Resources, which emphasizes the critical and responsible integration of technology into teaching. The teacher’s active monitoring and guidance ensure that students engage safely and meaningfully with AI, learning how to use it as a supportive tool for creativity and communication, rather than as a substitute for thinking.

The original lesson plan focused on lower-order thinking skills, such as recognizing and copying vocabulary (e.g., matching or completing a crossword). In this technology-enhanced version, students move beyond simple recall to applying their knowledge by constructing dialogues and creating authentic digital interactions using FakeWhatsApp. This stage encourages learners to synthesize vocabulary, grammar, and expressions in a meaningful communicative context, aligning with the higher levels of Bloom’s Digital Taxonomy — Apply and Create (Wedlock, 2017).

For educators looking to foster critical thinking through technology, Bloom’s Digital Taxonomy offers verbs for the 21st-century student, highlighting how digital tools can support the application of higher-order cognitive skills like analyzing, evaluating, and creating

Let start by one of the key Theoretical Framewoks in this new digital era: The European Framework for the Digital Competence of Educators (DigCompEdu), which basically states that it is a scientifically sound framework describing what it means for educators to be digitally competent. It provides a general reference frame to support the development of educator-specific digital competences in Europe.

Additionally, it reports that, DigCompEdu is directed towards educators at all levels of education, from early childhood to higher and adult education, including general and vocational education and training, special needs education, and non-formal learning contexts.

DigCompEdu details 22 competences organised in six areas:

• Professional engagement

• Digital resources

• Teaching and learning

• Assessment

• Empowering learners

• Facilitating Learners' Digital Competence

The focus is not on technical skills. Rather, the framework aims to detail how digital technologies can be used to enhance and innovate education and training.

(European Commission, 2020) 

The SAMR model (Substitution, Augmentation, Modification, and Redefinition) is a well-known framework in educational technology. It aims to help teachers create more engaging and meaningful learning experiences by fostering critical thinking, creativity, and collaboration among students. As outlined by (Jackson, 2023), the model’s primary purpose is to support educators in designing lessons that integrate technology in increasingly sophisticated ways, offering clear stages for how technology can evolve in the classroom.

The significance here is that the SAMR model has offered an easy-to-understand, easy-to-use, and easy-to-follow process for technology integration for many in education. The SAMR model can be easily adapted and interpreted in multiple ways, helping teachers reflect as technology is used to achieve specific outcomes. As a planning tool, it enables teachers to design, develop, and infuse digital learning experiences that use technology.

The integration of artificial intelligence (AI) in education calls for a reevaluation of the relationship between technology, pedagogy, and subject matter. In response to this need, the AI-TPACK framework has been developed to explore how AI technology, pedagogical approaches, and content knowledge interact in educational settings. The framework consists of seven components:

1. Pedagogical Knowledge (PK), 

2. Content Knowledge (CK), 

3. AI-Technological Knowledge (AI-TK), 

4. Pedagogical Content Knowledge (PCK), 

5. AI-Technological Pedagogical Knowledge (AI-TCK), 

6. AI-Technological Content Knowledge (AI-TPK), and 

7. AI-TPACK itself.

Through the use of structural equation modeling (SEM) and factor analyses (EFA and CFA), the study shows that six key elements of AI-TPACK predict its effectiveness. The core knowledge components (PK, CK, and AI-TK) influence AI-TPACK indirectly, mediated by composite knowledge elements like PCK, AI-TCK, and AI-TPK. The study also finds that non-technical elements of knowledge have less explanatory power compared to technological knowledge elements. Importantly, content knowledge (CK) diminishes the explanatory power of PCK and AI-TCK, highlighting the nuanced role of different knowledge elements.

This framework offers a comprehensive guide for assessing AI-TPACK and provides insights into the complex relationships that influence the effective use of AI in teaching and learning (Ning, 2023).

Bloom’s Taxonomy has been adapted for the digital age through Bloom’s Digital Taxonomy, which integrates digital tools with the original cognitive levels — Remember, Understand, Apply, Analyze, Evaluate, and Create. This framework helps educators enhance critical thinking and creativity by aligning digital verbs with each cognitive level, providing a clear structure for how technology can support student learning.

In this context, digital tools allow students to move beyond basic recall and apply their knowledge in real-world scenarios. For example, students might Create digital presentations or Analyze data using online tools. According to Wedlock (2017), the digital generation requires educators to use technology not only to engage students but to facilitate higher-order thinking, including problem-solving, evaluation, and collaboration.

Additionally, AI technologies, as highlighted by Jain and Samuel (2025), offer the opportunity for co-piloted learning, where AI tools assist students in tasks like creating, analyzing, and evaluating, enhancing Bloom’s higher cognitive levels. This collaboration with AI transforms learning experiences, making them more personalized and dynamic.

Overall, Bloom’s Digital Taxonomy enables educators to design lessons that guide students from basic understanding to complex, technology-enhanced cognitive tasks, better preparing them for the demands of the 21st century.

• Dudeney, G., & Hockly, N. (2007). How to teach English with technology. Pearson Education Limited.

• Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x

• Redecker, C. (2017). European framework for the digital competence of educators: DigCompEdu. Publications Office of the European Union. https://joint-research-centre.ec.europa.eu/digcompedu_en

• Wedlock, M.S. (2017). The Technology Driven Student: How to Apply Bloom’s Revised Taxonomy to the Digital Generations. Retrieved from here 

• Bekiaridis, G. (2024). Supplement to the DigCompEDU Framework. Retrieved from https://aipioneers.org/wp-content/uploads/2024/01/WP3_Supplement_to_the_DigCompEDU_English.pdf

• European Commission Joint Research Centre. (n.d.). Digital Competence Framework for Educators (DigCompEdu). Retrieved from https://joint-research-centre.ec.europa.eu/digcompedu_en

• Ning, Y. (2023). Teachers’ AI-TPACK: Exploring the Relationship between Knowledge Elements. ResearchGate

• Jackson, N. (2023). Teachers' AI-TPACK: Exploring the Relationship Between Knowledge Elements.