I created a few different audio files because I wanted to try a few different AI tools.
I used NotebookLM to create a podcast about my bag, using the picture and blog post as a guide. I also asked it to analyse transfiguration and the elements of different texts. Overall, it did a great job interpreting the information, although it focused on a few purse items that I would not have prioritised.
For example, it repeatedly mentioned the casino card and incorrectly stated that it was from a school trip. This was confusing, as the map and money forms were clearly the items related to the school trip. Nevertheless, I think it effectively described the purse in a way that allowed the listener to imagine its general appearance, even though many key details were overlooked.
https://notebooklm.google.com/notebook/5f3bccab-79f6-46c3-a873-8e72e3480315/audio
Because the podcast focused on a few things I didn’t necessarily agree with, I didn’t like some of the topics the AI hosts discussed. Instead of incorporating the class readings and my blog post, I provided a list of the items in my purse along with the photo. In the chat, I also clarified:
“The owner recently took her class on a school trip (high school students) to St. Lawrence College to learn more about programs.”
I really liked this approach. However, the AI misinterpreted my payment request form, assuming I was a parent rather than a teacher. This was an interesting assumption, as an AI wouldn’t inherently understand that certain forms are specific to teachers.
This is the final version of the podcast.
https://notebooklm.google.com/notebook/8a018277-6308-43ea-aa8b-2c466f171d94/audio
I wanted to create a song based on my purse, so I used the same list of items from the podcast prompt and entered them into a program called AI Song Generator. My prompt was:
“Upbeat Pop, Catchy Melody, Female Vocals, Playful Synths.”
I absolutely love the result—it’s a song I would probably listen to on repeat. It describes the items in my purse in an exciting and engaging way, making it easy for listeners to connect with their own personal belongings.
https://aisonggenerator.ai/share/Bag%20Full%20of%20Life&id=347147
How Does This Link to This Week’s Readings?
Peña et al. (2023) suggest that new media should be experienced through different sensory modes than the original form. In this case, reading and viewing the blog post creates a different experience from singing and dancing (musical form – song) and from listening and imagining (oral form – podcast). Each format shifts the meaning of the purse items across different media. Through the podcast, the listener gains information about the items in my purse, while the song allows for a more emotional and experiential connection (Peña & James, p. 1612).
When examining the original blog post and image, the reader/viewer could form their own assumptions about the content, but these formats lack emotional or relatable elements. The blog post provides direct reasoning and clarification for each item, whereas the audio forms engage different cognitive and emotional responses (Siegel, p. 250).
A key similarity among all three formats is signal transmediation—each creation remains digital from its inception to its final product. However, while the blog post was informative, it lacked the joy and intrigue that the podcast and song introduced. These audio-based creations allow for deeper audience connections and multiple interpretations, as they engage different areas of the brain and heart (Cope & Kalantzis, p. 169).
Feature image – Adobe Spark – AI Image Generator
Cope, B., & Kalantzis, M. (2009). Multiliteracies: New literacies, new learning. Pedagogies: An International Journal, 4(3), 164–195. https://doi.org/10.1080/15544800903076044
Peña, E., & James, K. (2023). A framework of transmediation. Convergence: The International Journal of Research into New Media Technologies, 30(5), 1610–1624. https://doi.org/10.1177/13548565231220325
Siegel, M. (1995). More than words: The generative power of transmediation for learning. Reading Research Quarterly, 30(2), 250–271.