Reflections on My Case-Study Stories (EN)

I've identified two potential issues in my dialogue-driven case-study stories. Firstly, some of my characters' emotional and intellectual intelligence may be unrealistically high, but not superhuman. Secondly, the context in which these stories unfold is not grounded in a specific, real-world setting, but rather in an idealized environment that facilitates the narrative.

However, as I consider the purpose of these case-studies, I'm not so sure if they are significant flaws or not. These stories are not meant to be representative of everyday life, but rather to illustrate the possibilities of growth in the face of mental health challenges.

By setting my stories in an idealized context, I'm able to focus on the emotional journeys of my characters, unencumbered by the harsh realities of the world we live in. I can explore the complexities of anxiety and the power of supportive relationships without the distractions of austerity, stupidity, or other external factors that might hinder the narrative.

My goal is not to create realistic portrayals of mental health struggles, but to craft stories that inspire and educate. I want readers to feel a sense of connection as they witness characters navigating their emotional landscapes. I want to show the value of empathetic listening and the impact it can have on someone's life.

In doing so, I'm deliberately choosing to prioritize the emotional and intellectual intelligence of my characters. I want them to be relatable, yet also exceptional in their capacity for self-awareness, empathy, and personal growth. This may not be reflective of everyday reality, but it's essential to what I'm trying to convey.

Regarding AI-Generated Portraits: Male or gender-ambiguous characters often lack diverse visual representation because current AI models struggle to produce distinct facial geometries for them with the same prompt. Conversely, female characters tend to yield multiple variants due to the overrepresentation of female facial data in training sets, resulting in more nuanced and varied outputs despite identical prompts.