Lesson plan with Perplexity AI

Let us consider a unit of work that addresses issues in public transportation.

We will now create a lesson for the final step in this unit of work.

Since we have decided to rely on SAMR-AI and a Gen-AI adaptation of RBT for this lesson plan, we must first focus on some aspects underscored by these frameworks. The lesson will be designed later as informed by the theory.

However, the rapid advance of technology has also required a rethinking of BT in order to incorporate technological advances as "tools of education" and not simply "tools of obsession" (Wedlock & Growe, 2017, p. 2).

Jain and Samuel’s (2025) own revision of RBT rebrands the traditional “creating” category in Bloom’s taxonomy to “co-curating,” signifying a groundbreaking change in students’ goals, now better aligned with cognitivist views: students are no longer expected to craft the entirety of a product, but mostly to show how a given result was achieved. That is, our learning assessment must “[allow] for a deeper understanding of students' cognitive engagement and problem-solving strategies” (Jain and Samuel, 2025, p. 9). Consequently, we will ask our students to document (e.g., by screenshotting) the entire process, and we will give as much weight to this task as to the final video product.

Moreover, Jain and Samuel (2025) argue that Gen-AI displaces “student efforts in memorization, factual processing, and data analysis” (p. 6) which span a number of categories in RBT (i.e., from remembering to evaluating). This is of particular importance when students need to find solutions to issues in public transportation. In these categories, Jain and Samuel (2025) propose that the role of AI should include “[describing] concepts and [curating] possible examples with more details” (pp. 10-11), whereas the students’ role should revolve around “making appropriate critical evaluations” (p. 11). Therefore, the use of Gen-AI to obtain ideas will not only be permitted, but pivotal: it is not the ideas that concern us completely, but the students’ reasoning and critical thinking when engineering prompts and filtering AI output.

In the lesson plan below, the students will go through virtually all categories in Jain and Samuel's (2025) RBT, from ventriloquising in the Web stage (using the AI to retrieve information), to evaluating that information (deciding which information to use) and co-creating the final product with the AI in the What-next stage.

While each level of integration comes with specific requirements, these can be further grouped due to their similarities. Hilton (2016, as cited in Gillespie, 2022) argues that "Substitution and Augmentation are situated in the 'Enhancement' subgroup because they utilize technology to exchange or improve the tools already present in the learning task," whereas Modification and Redefinition belong in a "Transformation" subgroup, as "they promote learning opportunities that could not be achieved easily without technology" (Kirkland, 2014, as cited in Gillespie, 2022).

Although the SAMR model was not created with Gen-AI in mind, it has lately been brought to attention by teachers in need to properly integrate this technology into their lessons.

From the point of view of SAMR-AI, the activities suggested in the previous discussion of Bloom's taxonomy can be classed as an instance of Transformation: the activity is redesigned from a classical “brainstorming and redacting” task, to a “prompting and co-curating” task, vastly widening the range of possible results. More importantly, Hockly (as cited in Romrell et al., 2014) suggests that “it is in modification and redefinition that the true potential [of technological learning] is fully realized” (p. 7). Thus, the students will use Gen-AI to create a product (a video with generated images, and possibly audio) they could not make without these specific technologies. Specifically, this occurs at the end of the What-next stage. 

With these considerations in mind, let us now design the lesson plan.

We will now apply Dudeney and Hockly’s (2008) WWW lesson format. To summarize, there will be three stages in this lesson: a Warmer stage to introduce students to the lesson topic; a Web stage to obtain resources from the Internet; and a What-next stage to create the video. Because the lesson makes such heavy use of Gen-AI, it will be best to have access to computers in all three stages.

Web (30 minutes)

The teacher tells the students that they will make a video on solutions to common transportation problems in their city. To this end, they will first use Perplexity AI to discuss the problems with the app, and to collect possible solutions. The students will:

1. Form groups of 3 to 4 students.

2. Explore at least 3 problems they consider relevant using the three questions from the Warmer stage as a starting point (e.g., “what is overcrowding in transportation?”; “why does noise pollution happen in transportation?”; “how can we fix high fares in transportation?”). Asking more questions is encouraged, especially regarding solutions imagined by themselves.

The teacher must warn the students to be careful with their prompts (e.g., pointing out that “in transportation” is provided in all questions to give clear context to the AI), as well as asking them to check the sources provided by the AI.

3. Keep a record of their conversations, using the “Export as PDF” feature found at the top-right corner menu. Click here to see a sample exported conversation.

References

Center for Teaching Vanderbilt University. (2016). Bloom's Taxonomy [Photograph]. Flickr. https://www.flickr.com/photos/vandycft/29428436431

CGP Grey. (2016, August 31). The Simple Solution to Traffic [Video]. YouTube. https://www.youtube.com/watch?v=iHzzSao6ypE

Dudeney, G. & Hockly, N. (2008). How to teach English with technology. Pearson Education Limited.

Gillespie, R. (2022). SAMR: The Power of a Useful Technology Integration Model. Technology and the Curriculum: Summer 2022. https://pressbooks.pub/techcurr20221/chapter/samr/

Hamel, M.-J., Landry, J., & Bibeau, L.-D. (2022). Language instructors on their emergency remote teaching pedagogy during the pandemic. EUROCALL 2022, pp. 135-140. doi.org/10.14705/rpnet.2022.61.1448

Jain, J. A., & 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., Wood, E., & Kidder, L. C. (2014). The SAMR Model as a Framework for Evaluating mLearning. Journal of Asynchronous Learning Network, 18(2). https://www.doi.org/10.24059/olj.v18i2.435

Wedlock, M. S., & Growe, R. (2017). The Technology Driven Student: How to Apply Bloom’s revised Taxonomy to the Digital Generations. Journal of Education & Social Policy, 7(1). https://jespnet.com/journals/Vol_4_No_1_March_2017/4.pdf