KEY LEARNING

At the beginning of the project, I struggled to grasp the full scope of what was required. Specifically, I wasn’t sure how extensively I needed to visualize my data. Once I collected the data, I jumped straight into creating visualizations without fully considering the overall strategy or purpose. I also faced challenges in finding concrete examples of bias within Ed-tech. AI frequently appeared as a result in my searches, as it’s deeply integrated with many educational technologies. The sheer volume of data made it difficult to streamline and organize effectively. I experimented with multiple approaches to visualizing the data, using spreadsheets to organize the information. However, I lacked the technical skills to use JavaScript for creating infographics. In the future, I’d like to learn these skills to produce more polished, efficient visuals rather than relying solely on manual methods. Although my attempts at visualization fell short, I gained valuable insight into how bias impacts data and technology. I realized how complex and significant this issue can be for future generations. Next time, I would like; Gain a deeper understanding before I start visualizing. Use better search terms and narrow down ideas to find examples more effectively. Organize various methods to represent data. Learn how to use JavaScript for creating infographics. As a designer, I am committed to continuously advancing my design justice practice in everything I create. I aim to advocate for design justice whenever possible in my work. The root of bias lies in the systems humans build, not in AI itself. AI is not inherently biased—humans are.

This project explores my journey of completing a project for the Design 6400 class, taught by Professor Mathew Lewis, titled “Visualizing Research Spaces.” The goal of this assignment was for students to explore three different topics and find convergence among them to inspire future research topics. Students were asked to select three topics from different areas: 

1. Emerging technology 

2. Social issues 

3. Any topic of personal interest 

The assignment was to research each topic individually, combine two topics, and conduct further research on the combined themes. Finally, students were asked to integrate all three topics to explore new insights. I chose “Generative AI” as my emerging technology, “Racial and Gender Bias in Visual Communication” as my social issue, and “Educational Technology” as my topic of interest. 

I began by researching each topic separately using Google and Google Scholar. As I delved into each area, I focused on understanding their definitions and extracting key themes that interested me. To gain deeper insights, Professor Lewis asked students to research each topics’ history and related research papers. Exploring the historical context and background of each topic, examining how they originated and evolved over time. My research included a balanced perspective, looking at both the positive and negative aspects of each topic. 

After exploring definitions on Wikipedia, searching articles and research papers on each topic to get basic knowledge and understanding. I first combined Generative AI and Racial and Gender Bias in Visual Communication and researched examples of gender and racial bias in generative AI within visual communication, focusing on AI bias and its challenges. 

Next, I combined Racial and Gender Bias in Visual Communication with Educational Technology. I discovered that certain genders face limitations in access to and representation in educational technology compared to males. 

For Generative AI in Educational Technology, I explored applications like chatbots, virtual classes, personalized learning, customized classes, and tailored curricula, as well as the future potential and challenges of these technologies. 

I began synthesizing the research data from all three topics and created a mind map (Fig 1) to identify overlaps between two and three topics. I used Venn diagrams (Fig 2) and Excel sheets to categorize and organize keywords from each topic. 

Fig 2. Van Diagram to categorize keywords for each topic 

After examining all the keywords I found from the research, I could see the patterns and that I could categorize in different ways. Classifying the keywords into categories like Humanity vs. Dehumanization and Visible vs. Invisible helped me visualize the data differently. I observed that generative AI and educational technology function as tools, whereas racial and gender bias is more of an idea and societal phenomenon. 

To visually represent my research data, I experimented with word clouds and tidy tree diagrams to compare findings and draw connections among the three topics. 

Reflecting on the histories and evolution of each topic, I noticed that racism and gender bias have been persistent issues since the beginning of multiracial societies. As technology developed, so did educational technology, culminating in new advancements like generative AI, which now influences education. However, racial and gender bias has remained an inevitable issue throughout history. 

Bias in society and data has shaped both education and technology, as they are influenced by humans operating within biased social systems. This raises the question: How can we de-bias education and technology to help create a fairer society? While biases may never be eliminated, recognizing them and striving to neutralize their effects through inclusive and diverse approaches can make a difference. For example, reframing prompts to get more diverse and inclusive visuals in generative AI could trigger a butterfly effect—small changes that could positively impact both generative AI and education for future generations. I have created a visual framework to illustrate the current relationship between three topics. The diagram on the left shows how racial and gender biases affect both education and AI, creating an additional outer boundary of bias. The framework on the right represents my vision for how these three topics should ideally interact. This diagram demonstrates that while AI continues to expand in education and beyond, education should increasingly influence both racial/gender bias and AI development to reduce overall systemic bias.