This post is week 5 of 8 in the #8WeeksofSummer Blog Challenge for educators with the question: What (teacher) librarian tasks would you try with an LLM or image artificial intelligence?

As a person who teaches students (and adults!) about fake imagery, the image artificial intelligence area is a big concern. When I recently presented at a national conference on fake images (Picture This!), one of the attendees asked if I taught about fake video creation too. I told him that when I used to teach middle school, I taught my students how to make fake videos. The more you know how something works, the more aware you are when you are being manipulated (Shen et al., 2018).

This holds true for general images too. But there is a real danger in using and manipulating fake images. Studies show that false images can change your memory of an event (Wade et al., 2002). The images can evoke emotions that when found out to be false may be hard for a person to handle. Some material may be too traumatic for a person where the image is more intense to someone than a description (DART, 2014). In the tsunami of social media and images, the best protection you have is to know more about image manipulation, social media, and general internet skills (Shen et al., 2018).

My answer to what would I do with ‘image artificial intelligence’ is to use it to show students how much images can be manipulated. I also want to make clear how common this is already. Did you know you can do this in Canva, Microsoft Bing, Google/Imagen, and many other sites? I want students to explore, create, modify, and recognize. Ignoring this will only make it harder on students (and everyone else). I think it works best if we work together to create images and understand how easily we can be misled or fooled by an image.

References:

DART Center for Journalism & Trauma. (2014). Working with traumatic imagery. https://dartcenter.org/content/working-with-traumatic-imagery

Du, Yilun. (October 2022). 3 Questions: How AI image generators work. MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). https://www.csail.mit.edu/news/3-questions-how-ai-image-generators-work

Shen, C., Kasra, M., Pan, W., Bassett, G. A., Malloch, Y., & O’Brien, J. F. (2018). Fake images: The effects of source, intermediary, and digital media literacy on contextual assessment of image credibility online. New Media & Society, 21(2), 438–463. https://doi.org/10.1177/1461444818799526

Wade KA, Garry M, Read JD, et al. (2002). A picture is worth a thousand lies: using false photographs to create false childhood memories. Psychonomic Bulletin & Review 9: 597–603.