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After the initial round of feedback from my professor and TA, I made a significant shift in the focus of my story. Originally centered on AI adoption for businesses, the feedback highlighted the lack of narrative structure and the overly data-centric approach, making it feel more like a report. Based on this, I pivoted the story towards AI’s energy consumption, a topic that offers clearer storytelling potential and aligns better with data-driven narratives.
Using the sketches developed in part I, I further refined the story outline and created high-fidelity draft data visualizations for critical elements of my narrative. The Shorthand story draft is accessible here:
The story uses a scroll-driven structure with interactive visualizations, guiding the reader through AI’s energy footprint.
The target audience primarily includes the general public. After receiving feedback from Part I, I chose to explain the story in very simple terms to make it accessible to everyone. With tools like ChatGPT and Google AI being widely used, I wanted to raise awareness about the energy implications of AI through this story.
My secondary audience includes industry leaders and policy makers who play a key role in planning for energy efficiency and sustainability.
I conducted in-class interviews with classmates from different academic programs to gather diverse perspectives and more structured feedback. These interviews focused on understanding user engagement, clarity of narrative, and the effectiveness of data visualizations.
Additionally, I sought general feedback outside of class at Heinz College by asking peers simple and direct questions. For example, I asked which visualizations seemed better suited for specific data, such as choosing between a choropleth map and a spike map — to gauge intuitive preferences and improve the visual storytelling.
This combination of structured interviews and informal feedback helped ensure that the story would be universally accessible to individuals with varying levels of technical expertise.
| Goal | Questions to Ask |
|---|---|
| Comprehension | “What do you understand by this story?” |
| Effectiveness of data visualizations | “How did the data visualizations help (or not help) you understand the topic?” |
| Clarity | “Were there any sections of the story that were confusing?” |
| Positive feedback | “What is one thing you liked the most about the story?” |
| Constructive feedback | “What is one thing you would change to improve the story?” |
| Questions | Interview 1 | Interview 2 | Interview 3 | Interview 4 |
|---|---|---|---|---|
| Was the story easy to follow? | “Mostly, but Chapter 3 felt data-heavy.” | “Yes, but local grid issues need emphasis. Also, it felt too much like a report.” | “Some charts were overwhelming at first. The story felt a bit too data-centric.” | “Yes, but background images made some text hard to read.” |
| Did charts help clarify the points? | “Yes, especially the pie charts.” | “The energy growth charts were insightful.” | “The pie charts were easiest to understand.” | “The stacked bar charts were very helpful.” |
| Did scroll effects enhance experience? | “Yes, but some transitions felt too fast.” | “I liked the animations for data growth.” | “A bit too much motion at times.” | “The scroll effect in Chapter 3 was my favorite.” |
| Did the story feel balanced? | “Mostly balanced, could mention AI positives.” | “Good, but some bias towards AI risks.” | “Felt neutral overall.” | “Balanced, but background images were distracting.” |
| What stood out most? | “The map showing local grid strain.” | “The efficiency graph in Chapter 5.” | “The comparison of AI vs. refrigeration.” | “The scroll-driven visualization in Chapter 3.” |
After completing the interviews, I sought additional feedback from my TA to refine the project further. The TA provided the following suggestions:
Simplify Text Formatting: The TA noted that the text in my Shorthand draft was too heavy for the eyes. She recommended reformatting the text to improve readability and create a more engaging experience.
Highlight Data More Effectively: The TA suggested finding an easier and simpler way to highlight key data points within the narrative. This includes using visual cues like bold text, callouts, or cleaner layouts to make important information stand out.
Include Proper Citations: The TA emphasized the importance of adding citations for all data visualizations used in the story. Proper attribution will improve the credibility and academic rigor of the project.
These insights will guide my improvements in Part III, focusing on enhancing readability, highlighting data more effectively, and ensuring proper citations throughout the Shorthand story.
After receiving feedback from my professor, TA, and interviewees, I made several important changes to the project. Initially focused on AI adoption for business, the story shifted towards AI’s energy consumption to create a more focused and relevant narrative around energy concerns.
Interviewees highlighted that the story was too data-heavy and structured like a report, lacking a compelling narrative. To address this, I reworked the storytelling approach to integrate data more organically and create a clearer structure.
Additional feedback pointed out that the Shorthand draft was visually overwhelming, with dense blocks of text and complex visualizations. Users suggested a side-by-side layout for text and visualizations to enhance readability and engagement.
From the TA’s feedback, I learned that the text formatting needed simplification to be easier on the eyes. She also emphasized the need for a simpler method to highlight key data points and advised adding citations for all data visualizations to improve credibility.
For Part III, I will focus on:
| Research Synthesis | Anticipated Changes for Part III |
|---|---|
| Story felt too data-heavy and report-like | Integrate data into the narrative more smoothly; focus on storytelling over pure data presentation. |
| Text formatting was visually overwhelming | Simplify text blocks, use less bullet points, more callouts, and more whitespace to improve readability. |
| Users prefer side-by-side layout | Implement a side-by-side layout in Shorthand to pair text with visuals for better engagement. |
| Scroll effects felt too fast/overwhelming | Slow down transitions and provide visual cues to guide the reader through interactions. |
| Need for clearer data highlights | Use callouts, bold text, and cleaner layouts to emphasize key data points. |
| Missing citations on visualizations | Add proper citations for all data visualizations to improve credibility. |
The feedback from the TA and interviewees emphasized the importance of balancing narrative and data while maintaining visual clarity. For Part III, I will focus on creating a more engaging, user-friendly experience by simplifying the text, restructuring the layout, and making the data easier to digest. Implementing the suggested changes will ensure that the story remains accessible to a broad audience while maintaining depth and credibility.
de Vries, A. (2023). The growing energy footprint of artificial intelligence. Joule. https://doi.org/10.1016/j.joule.2023.01.001
International Energy Agency. (2024). World Energy Outlook 2024. IEA. https://www.iea.org/reports/world-energy-outlook-2024
U.S. Energy Information Administration. (2023). Annual Energy Outlook 2023. EIA. https://www.eia.gov/outlooks/aeo/
Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3645–3650. https://doi.org/10.18653/v1/P19-1355
Jones, N. (2018). How to stop data centres from gobbling up the world’s electricity. Nature, 561(7722), 163–166. https://doi.org/10.1038/d41586-018-06610-y
Masanet, E., Shehabi, A., Lei, N., Smith, S., & Koomey, J. (2020). Recalibrating global data center energy-use estimates. Science, 367(6481), 984–986. https://doi.org/10.1126/science.aba3758
For this assignment, I used ChatGPT to help refine my ideas and brainstorm narrative structures. I also used ChatGPT to create initial sketches of my data, which I then refined using Datawrapper. Additionally, I utilized Grammarly to check the grammar of my writing and improve sentence structure and clarity throughout the document.
All visualizations were finalized using Datawrapper, and the storytelling was built using Shorthand.