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arxiv: 2510.01189 · v2 · submitted 2025-08-20 · 💻 cs.HC · cs.AI

Beyond Value Elicitation: Towards Moral Profiles in Early Requirements Engineering via Role-Playing Games and Anthropologist LLMs

Gianluca De Ninno , Paola Inverardi , Francesca Belotti This is my paper

Pith reviewed 2026-05-18 21:50 UTC · model grok-4.3

classification 💻 cs.HC cs.AI
keywords moral profilesrequirements engineeringrole-playing gameslarge language modelstacit valuesvalue elicitationnarrative anthropologyuser preferences
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The pith

Role-playing games combined with a specialized LLM can generate coherent narrative moral profiles from users' tacit values for early requirements engineering.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes shifting from directly asking users to state their moral values to instead capturing those values as they appear in real decisions. It uses immersive role-playing game sessions to create context-rich stories where participants make choices that reveal their underlying moral orientations. A large language model trained with anthropological grounding then converts those stories into narrative profiles of each user's morals. These profiles support interpreting user preferences in the specific setting of a digital system under design. If the method holds, requirements engineers gain a way to incorporate moral considerations early without relying on abstract or forced articulations.

Core claim

RPG environments effectively support the generation of rich, context-dependent data for eliciting tacit values, and 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道德值s

What carries the argument

Individual anthropological moral profiles (IAMPs): narrative reconstructions of a user's moral orientation produced by an anthropologically grounded LLM from data generated in role-playing game sessions, serving as the mechanism that converts tacit values into context-aware representations usable in requirements engineering.

Load-bearing premise

Cross-comparison between the generated profiles and participants' actual responses in new moral scenarios will reliably detect cases where the LLM has invented or distorted the user's moral orientation.

What would settle it

A follow-up session in which users respond to additional unseen moral dilemmas and their choices contradict the predictions implied by the LLM-generated profile in a consistent pattern.

Figures

Figures reproduced from arXiv: 2510.01189 by Francesca Belotti, Gianluca De Ninno, Paola Inverardi.

Figure 2
Figure 2. Figure 2: Sample of Player Sheet B. Recruiting and engaging audience A total of ten participants were selected for this pilot study. To ensure a balanced sample, we included 5 women and 5 men, aged between 21 and 66 years old and coming from different socio-economic backgrounds. Five of them were skilled players and participated in another previous research based on RPGs. Their familiarity helped streamline the game… view at source ↗
Figure 1
Figure 1. Figure 1: RPG scenario 1 provides each player with a document called The Player Sheet, structured into two sections (see [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: The first question of the questionnaire The questionnaire was developed through a structured AI￾assisted process. A document containing the criteria for the game’s development, the guidelines for scenario design, the key area of investigation and the original scenarios was up￾loaded to GPT. GPT was prompted to use the information in the files and to refer to the pre-existing scenarios. All scenarios were r… view at source ↗
Figure 4
Figure 4. Figure 4: Demographic and Self-Description Table These narrative tables organize data collected during the game, helping to structure participants’ interactions, ethical reasoning, and conflicts of values and assessing the impact of in-game experiences. The goal was to provide the LLM model with comprehensive access to contextual, behavioral, social, and experiential factors for any user [PITH_FULL_IMAGE:figures/fu… view at source ↗
Figure 5
Figure 5. Figure 5: Player’s choices and interpretation of game scenarios [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The manuscript presents a proof-of-concept for eliciting moral profiles in early requirements engineering by combining immersive role-playing games (RPGs) to generate context-rich narrative data on tacit values with an anthropologically grounded LLM (GPT-A) that produces Individual Anthropological Moral Profiles (IAMPs). These profiles are validated via cross-comparison of model outputs against participants' direct responses in unseen moral scenarios. The central claim is that RPG environments yield rich data for tacit values and that the LLM can transform this into coherent narrative representations enabling contextual interpretation of preferences, trade-offs, and domain expertise, while preserving situated and dynamic aspects better than predefined value taxonomies.

Significance. If the validation reliably confirms faithful reconstruction, the work could advance requirements engineering by shifting from discrete value elicitation to narrative moral profiles that support analysis of context-dependent trade-offs. A strength is the independent grounding from fresh participant RPG data and direct cross-checks rather than self-referential fitting. This addresses a recognized gap in handling tacit moral orientations in RE and HCI contexts.

major comments (1)
  1. [Abstract] Abstract (validation paragraph): The cross-comparison validation is described only at a high level and supplies no quantitative fidelity metrics, participant counts, error analysis, inter-rater protocols, or explicit procedures for detecting discrepancies between IAMPs and the original RPG transcripts (e.g., over-generalization or addition of unstated motivations). This is load-bearing for the claim that the profiles faithfully reconstruct moral orientations rather than producing internally coherent but unfaithful narratives.
minor comments (1)
  1. [Abstract] The acronyms 'GPT-A' and 'IAMPs' are introduced without an initial expansion or brief definition; adding this on first use would improve readability for readers outside the immediate subfield.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the positive assessment of our proof-of-concept and for the constructive feedback on the validation description. We address the major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract (validation paragraph): The cross-comparison validation is described only at a high level and supplies no quantitative fidelity metrics, participant counts, error analysis, inter-rater protocols, or explicit procedures for detecting discrepancies between IAMPs and the original RPG transcripts (e.g., over-generalization or addition of unstated motivations). This is load-bearing for the claim that the profiles faithfully reconstruct moral orientations rather than producing internally coherent but unfaithful narratives.

    Authors: We agree that the abstract summarizes the validation at a high level, which is constrained by typical abstract length limits. The full manuscript provides a more detailed account of the cross-comparison process in the Methods and Results sections, including the overall procedure for comparing generated IAMPs against participants' direct responses in unseen scenarios and qualitative discussion of fidelity. As this is an exploratory proof-of-concept rather than a large-scale validation study, we did not include quantitative fidelity metrics, formal inter-rater protocols, or statistical error analysis. We will revise the abstract to more explicitly note the qualitative nature of the validation and the cross-comparison approach. We will also expand the manuscript to include additional examples of how potential discrepancies (such as over-generalization or unstated motivations) were identified and addressed during the comparison. revision: partial

Circularity Check

0 steps flagged

No significant circularity: empirical grounding via new participant data

full rationale

The paper's central claims rest on fresh empirical data collected from RPG sessions with participants, followed by LLM processing to produce IAMPs and validation via direct cross-comparison against the same participants' responses in unseen scenarios. This process introduces independent external input at each stage rather than reducing outputs to inputs by definition, fitted parameters renamed as predictions, or load-bearing self-citations. No equations, uniqueness theorems, or ansatzes are invoked that equate the moral profiles or validation results to the original data by construction. The derivation chain remains self-contained against the collected participant responses and does not exhibit any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 2 invented entities

The paper rests on domain assumptions about the tacit nature of values and introduces new constructed entities to frame its outputs.

axioms (2)
  • domain assumption Moral values are often tacit, context-dependent, and difficult to express directly
    Explicitly stated in the abstract as the core limitation of existing value-elicitation approaches.
  • domain assumption Phenomenological and narrative anthropology supplies an appropriate lens for capturing moral orientations through situated decision-making
    The method is grounded in these fields as described in the abstract.
invented entities (2)
  • Individual Anthropological Moral Profiles (IAMPs) no independent evidence
    purpose: Narrative reconstructions of users' moral orientations produced by the LLM
    New representational object introduced to move from discrete values to profiles.
  • GPT-A no independent evidence
    purpose: Specialized LLM configured for anthropological analysis of moral narratives
    Custom model proposed as the analysis engine without prior external description.

pith-pipeline@v0.9.0 · 5813 in / 1538 out tokens · 55691 ms · 2026-05-18T21:50:17.508495+00:00 · methodology

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