Enterprise AI Analysis
Evaluating Quality of Gaming Narratives Co-created with AI
This paper introduces a structured framework for evaluating AI-generated game narratives, combining literary theory, expert validation via a Delphi study, and the Kano model to map story quality dimensions to player satisfaction, providing game developers with a strategic tool for prioritizing AI content quality.
Executive Impact Summary
This research establishes a data-driven methodology for creative studios to de-risk the use of generative AI in narrative design, shifting quality assurance from a subjective art to a predictable science.
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Deep Analysis & Enterprise Applications
Select a topic to dive deeper into the core findings from the research, rebuilt as interactive, enterprise-focused modules.
A Repeatable Process for AI Narrative QA
The paper outlines a systematic, three-stage process to move from a broad list of potential quality factors to a prioritized, actionable framework for development teams.
Compile Story Quality Dimensions
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Validate with Expert Panel (Delphi Study)
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Categorize via Kano Model
Mapping Quality to Player Satisfaction
Experts classified each story quality dimension based on its impact on player satisfaction. This allows studios to strategically allocate resources, focusing first on 'Must-haves' to prevent dissatisfaction, then on 'Performance' metrics to build value, and finally on 'Delighters' to create exceptional experiences.
| Category | Key Dimensions & Impact |
|---|---|
| Must-Haves | Baseline expectations. Their absence causes significant player frustration and breaks immersion.
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| Performance | "More is better." Player satisfaction is directly proportional to how well these linear attributes are executed.
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| Delighters | Unexpected value. Their presence creates delight and memorable moments, but their absence goes unnoticed.
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| Indifferent | No significant impact on player satisfaction in this context. Experts found 'Diversity' among multiple stories from the same model to be non-critical for a single narrative experience. |
Beyond the Checklist: The Emergent Need for 'Voice' and 'Genre Alignment'
A critical finding from the expert panel was that a technically proficient story can still fail if it lacks a unique authorial 'Voice' or fails to meet 'Genre' expectations. These dimensions, not present in the initial literature review, emerged as 'Must-be' and 'Important' requirements respectively. This highlights the limits of purely automated evaluation and the indispensable role of expert human judgment in refining AI-generated content.
Key Takeaway for Enterprise: AI creative systems must be designed not just for correctness, but for stylistic nuance and contextual appropriateness. The Delphi method proved essential in uncovering these "hidden" requirements that standard benchmarks miss, providing a model for enterprises to validate their own domain-specific AI outputs and ensure brand alignment.
ROI & Implementation
Quantify the potential impact of a structured AI Quality Framework on your content pipeline and see a typical implementation roadmap.
Estimate Your Content Efficiency Gains
Potential Annual Savings
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Hours Reclaimed Annually
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Your Implementation Roadmap
Adopting this AI quality framework is a phased process, moving from discovery and expert panel formation to the integration of a live QA system into your content pipeline.
Phase 1: Discovery & Dimension Mapping (Weeks 1-2)
We work with your team to identify the core narrative quality dimensions specific to your brand, genre, and audience, adapting the paper's baseline list.
Phase 2: Internal Delphi Study & Kano Analysis (Weeks 3-5)
We facilitate a structured validation process with your internal experts to rank and categorize dimensions, creating a prioritized quality scorecard for your studio.
Phase 3: AI Evaluator Prompt Engineering (Weeks 6-8)
Using the validated scorecard, we develop a custom "LLM-as-a-Judge" system to automate the evaluation of your generated content against your unique quality criteria.
Phase 4: Pipeline Integration & Monitoring (Weeks 9-12)
The AI evaluator is integrated into your development pipeline, providing real-time quality scores and feedback loops to continuously improve your generative models.
Unlock Predictable Quality in AI Content
Stop guessing and start measuring. Schedule a complimentary strategy session to discuss how this expert-validated framework can be tailored to your enterprise needs, ensuring your generative AI content consistently meets the highest standards of quality and brand alignment.
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