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Visual novels are a popular game genre for educational games. However, they often feature pre-authored plot structures that cannot dynamically adjust to the player’s progression through learning objectives. Employing procedural storytelling techniques boosts plot dynamism, but this comes at the cost of needing a larger repository of content (dialogue and images) to support different learning progressions and objectives. In this paper, we present postmortem-style case studies describing the lessons we learned from attempting to integrate large-language models (LLMs) and text-to-image models into the development of an educational visual novel about responsible conduct of research. Specifically, we discuss our experiences employing generative AI in our dialogue, character sprite, and background image creation processes. 

Interpersonal violence (IPV) is a prominent sociological problem that affects people of all demographic backgrounds. By analyzing how readers interpret, perceive, and react to experiences narrated in social media posts, we explore an understudied source for discourse about abuse. We asked readers to annotate Reddit posts about relationships with vs. without IPV for stakeholder roles and emotion, while measuring their galvanic skin response (GSR), pulse, and facial expression. We map annotations to coreference resolution output to obtain a labeled coreference chain for stakeholders in texts, and apply automated semantic role labeling for analyzing IPV discourse. Findings provide insights into how readers process roles and emotion in narratives. For example, abusers tend to be linked with violent actions and certain affect states. We train classifiers to predict stakeholder categories of coreference chains. We also find that subjects' GSR noticeably changed for IPV texts, suggesting that co-collected measurement-based data about annotators can be used to support text annotation.