This study presents a proof of concept for eliciting and representing the moral profiles of digital system users in Requirements Engineering (RE) by combining immersive role-playing games (RPGs) with large language model (LLM) analysis. While existing approaches rely on predefined value taxonomies and explicit articulation, values are often tacit, context-dependent, and difficult to express directly. To address these limitations, we propose moving from the elicitation of discrete moral values to the narrative reconstruction and representation of users' moral profiles. Grounded in phenomenological and narrative anthropology, the approach focuses on capturing users' moral orientations as they emerge through situated decision-making. RPG sessions generate context-rich narrative data, which are then analyzed by a specialized LLM (GPT-A) to produce individual anthropological moral profiles (IAMPs). A validation process based on cross-comparison between model outputs and participants' responses in unseen moral scenarios assesses the adequacy of the generated representations. Results indicate that RPG environments effectively support the generation of rich, context-dependent data for eliciting tacit values, and that an anthropologically grounded LLM can transform such data into coherent narrative representations of users' moral profiles. These representations enable the contextual interpretation of users' preferences and values within the given domain, with improved performance when interpretive framing captures relationships between actions, underlying motivations, and individual domain expertise. From an RE perspective, this approach enables the analysis of user preferences and trade-offs while preserving their situated and dynamic nature, providing a foundation for integrating human moral values into the early stages of RE.

翻译：本研究提出了一种概念验证方法，通过将沉浸式角色扮演游戏（RPGs）与大型语言模型（LLM）分析相结合，在需求工程（RE）中 eliciting 和表示数字系统用户的道德档案。现有方法依赖于预定义的价值分类法和显式表述，而价值观往往具有隐性、情境依赖性和难以直接表达的特点。为克服这些局限，我们提出从离散道德价值的 elicitation 转向用户道德档案的叙事重构与表示。基于现象学与叙事人类学理论，该方法聚焦于捕捉用户道德取向在情境化决策中的浮现过程。RPG 会话生成富含情境的叙事数据，随后由专门训练的 LLM（GPT-A）进行分析，生成个体人类学道德档案（IAMPs）。通过模型输出与参与者在未见道德场景中反应之间的交叉比较验证流程，评估所生成表示的充分性。结果表明，RPG 环境能有效支撑生成丰富的情境化数据以 eliciting 隐性价值观，而基于人类学理论训练的 LLM 可将此类数据转化为用户道德档案的连贯叙事表示。这些表示能实现给定领域内用户偏好与价值观的情境化解读，当解释框架能同时捕捉行动、潜在动机与个体领域专业知识之间的关系时，性能表现更优。从需求工程视角看，该方法能在保留用户偏好情境化与动态性本质的同时分析其权衡关系，为将人类道德价值观整合至需求工程早期阶段奠定基础。