Social skills training targets behaviors necessary for success in social interactions. However, traditional classroom training for such skills is often insufficient to teach effective communication -- one-to-one interaction in real-world scenarios is preferred to lecture-style information delivery. This paper introduces a framework that allows instructors to collaborate with large language models to dynamically design realistic scenarios for students to communicate. Our framework uses these scenarios to enable student rehearsal, provide immediate feedback, and visualize performance for both students and instructors. Unlike traditional intelligent tutoring systems, instructors can easily co-create scenarios with a large language model without technical skills. Additionally, the system generates new scenario branches in real time when existing options do not fit the student's response.

We explore how interaction with large language models (LLMs) can give rise to emergent behaviors, empowering players to participate in the evolution of game narratives. Our testbed is a text-adventure game in which players attempt to solve a mystery under a fixed narrative premise, but can freely interact with non-player characters generated by GPT-4, a large language model. We recruit 28 gamers to play the game and use GPT-4 to automatically convert the game logs into a node-graph representing the narrative in the player's gameplay. We find that through their interactions with the non-deterministic behavior of the LLM, players are able to discover interesting new emergent nodes that were not a part of the original narrative but have potential for being fun and engaging. Players that created the most emergent nodes tended to be those that often enjoy games that facilitate discovery, exploration and experimentation.

Dialogue-based Role Playing Games (RPGs) require powerful storytelling. The narratives of these may take years to write and typically involve a large creative team. In this work, we demonstrate the potential of large generative text models to assist this process. \textbf{GRIM}, a prototype \textbf{GR}aph-based \textbf{I}nteractive narrative visualization system for ga\textbf{M}es, generates a rich narrative graph with branching storylines that match a high-level narrative description and constraints provided by the designer. Game designers can interactively edit the graph by automatically generating new sub-graphs that fit the edits within the original narrative and constraints. We illustrate the use of \textbf{GRIM} in conjunction with GPT-4, generating branching narratives for four well-known stories with different contextual constraints.

Narratives are a predominant part of games, and their design poses challenges when identifying, encoding, interpreting, evaluating, and generating them. One way to address this would be to approach narrative design in a more abstract layer, such as narrative structures. This paper presents Story Designer, a mixed-initiative co-creative narrative structure tool built on top of the Evolutionary Dungeon Designer (EDD) that uses tropes, narrative conventions found across many media types, to design these structures. Story Designer uses tropes as building blocks for narrative designers to compose complete narrative structures by interconnecting them in graph structures called narrative graphs. Our mixed-initiative approach lets designers manually create their narrative graphs and feeds an underlying evolutionary algorithm with those, creating quality-diverse suggestions using MAP-Elites. Suggestions are visually represented for designers to compare and evaluate and can then be incorporated into the design for further manual editions. At the same time, we use the levels designed within EDD as constraints for the narrative structure, intertwining both level design and narrative. We evaluate the impact of these constraints and the system's adaptability and expressiveness, resulting in a potential tool to create narrative structures combining level design aspects with narrative.

Games are complex, multi-faceted systems that share common elements This paper presents TropeTwist, a preliminar system that uses and underlying narratives, such as the conflict between a Tropes [21, 54] extracted from TvTropes [26, 46] as patterns and fundamental hero and a big bad enemy or pursuing a goal that requires overcoming units, which when combined can compose structures further challenges. However, identifying and describing these elements representing other composed tropes. Common narrative structures together is non-trivial as they might differ in certain properties can be identified and defined using TropeTwist. TropeTwist and how players might encounter the narratives. Likewise, generating can define generic aspects of a story, leading to the identification of narratives also pose difficulties when encoding, interpreting, events, roles, and narrative elements, as well as a novel way to form and evaluating them. To address this, we present TropeTwist, a narratives. As a proof-of-concept, we built, analyzed, and described trope-based system that can describe narrative structures in games structurally three game examples shown in figure 1, top row. in a more abstract and generic level, allowing the definition of We propose graph grammars as indirect encoding of narrative games' narrative structures and their generation using interconnected graphs and the use of the Multi-dimensional Archive of Phenotypic tropes, called narrative graphs. To demonstrate the system, Elites (MAP-Elites) [40] to generate novel variations (shown we represent the narrative structure of three different games. in figure 1, bottom row) using the proof-of-concept examples as We use MAP-Elites to generate and evaluate novel quality-diverse roots. Simultaneously, we propose metrics to evaluate the resulting narrative graphs encoded as graph grammars, using these three narrative graphs' coherence, cohesion, and interestingness.