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Workshop Title Slide

This module is currently under construction. Come back soon for the finished product!

Visions of Generative AI: Historical and Ethical Dimensions of Visual Media Literacy in the Era of AI

Whether you regularly engage with visual generative AI or are just dipping your toe into these waters, this module focuses on recognizing sources of image biases and its impacts and implications as artificial images become increasingly sophisticated and ubiquitous. Applying a combined historical and ethics lens, module participants will have the opportunity to generate and analyze artificial images as a means of unpacking continuity and changes in visual practices and norms. This module supports the development of critical reflection skills and tools for creating and ‘reading’ images that avoid potential pitfalls of (re)producing harmful clichés and to support identification of mis/disinformation. It has relevance for academic integrity along with navigating our daily visual landscape, which in turn has implications for democracy, civic engagement, and human relationships. At a time when visual evidence/truth, dis/misinformation and academic integrity are at stake, there is no time like the present to gain a solid foundation on visual literacy.

By the end of this module, participants will be able to:

  • Identify opportunities, pitfalls and potential harms of visual generative AI to make informed decisions on its use in educational and every-day life settings by exploring recent examples.
  • Unpack the role of audience engagement with visual images in the era of AI to critically analyse the impacts of and to support responsible use of this new technology.
  • Trace biases and ethically problematic visual stereotypes present in AI-generated images through comparison and critical reflection of historical cases

This module is great for students, educators, researchers, and professionals interested in developing critical visual literacy skills to responsibly engage with AI-generated images in academic, creative, or everyday contexts!

Acknowledgements: This asynchronous module was adapted from a workshop held in October 2025 at the Sherman Centre for Digital Scholarship. The workshop content was developed by Sonya de Laat.

Table of Contents

Facilitator Bio

Dr. Sonya de Laat is the Academic Program Advisor and Curriculum Coordinator in the School of Global Health and Social Medicine, and a Research Associate with the Department of Health Research Methods, Evidence, and Impact at McMaster University. With degrees in Anthropology and Media Studies, Dr. de Laat’s work has focused on historical and ethical dimensions of humanitarian visual culture and action. Currently, her focus is on challenging the promises/hopes of photorealistic generative AI and sharing diverse visual histories as a corrective intervention. Her postdoctoral work focused on moral and practical dimensions of palliative care in refugee camps drawing attention to small interventions having big impacts, and the importance of co-design and partnered research. Dr. de Laat is an active member of the Humanitarian Health Ethics research group, and the Canadian Network on Humanitarian History. Representative publications include, “Assembling a global health image: Ethical and pragmatic tensions through the lenses of photographers” (PLOS Glob Public Health 2024), “A case analysis of partnered research on palliative care for refugees in Jordan and Rwanda” (Confl Health 2021), and “The Camera and the Red Cross: ‘Lamentable pictures’ and Conflict Photography Bring into Focus an International Movement, 1855-1865” (IRRC 2021).

1. Introduction

This introductory video sets the stage for the module by outlining its learning objectives and focus on the historical and ethical dimensions of visual generative AI within media literacy. It highlights how past practices in photography may inform current debates about AI-generated images, bias, and responsible use.

View original here.

Quiz 1

2. Visual Media Literacy and Identifying AI Images

This section introduces visual media literacy through an interactive exercise that challenges viewers to distinguish AI-generated images from real ones. It emphasizes the increasing difficulty in detecting synthetic images and presents practical strategies to better evaluate the authenticity and implications of digital images.

Can you spot the AI?

Click on the image that you think is AI generated:

Click on the image that you think is AI generated:

View original here.

Quiz 2

3. Intersectionality, Decoloniality, and Historical Thinking

In this section, the key concepts of intersectionality, decoloniality, and historical thinking are explored to further understand how bias and power shape AI-generated images. It examines how generative AI can reinforce biases and stereotypes from visual histories and emphasizes the need for considering both social and historical perspectives when analyzing these images.

View original here.

Quiz 3

4. Histories of Humanitarian Imagery and Photographic Fakery

This section explores the long history of image manipulation in photography, highlighting how such practices existed well before generative AI. It connects these historical forms of photographic fakery to current uses of AI-generated imagery in humanitarian contexts, raising critical questions about truth, representation, and trust in visual media.

View original here.

Quiz 4

5. Case Study Examples

This section presents case study examples using generative AI tools to demonstrate how prompts influence the images produced and expose underlying biases. It highlights how AI-generated visuals can reinforce stereotypes, encouraging critical reflection on representation, diversity, and the role of training data.

View original here.

6. Considerations & Implications

This section wraps up the module by highlighting key considerations and implications of generative AI, focusing on how the technology is shaped by the data it learns from and the broader social contexts. It emphasizes issues of representation, access, and bias, while encouraging viewers to engage with AI in more critical, ethical, and inclusive ways.

View original here.

Quiz 6

Congratulations!

You’ve just finished this workshop. You should now be able to:

  • Question the authenticity and ethics of AI-generated images.
  • Identify historical and cultural influences in visual AI outputs.
  • Make more informed, responsible decisions about when and how to use generative AI visuals.

Table of contents