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AREAs-Lab: An Interactive Environment for AI-driven Requirement Elicitation for AI Systems

Pengshan Cai, Zihao Zhang, Ting Jin, Chenyang Zhu, Kushal Chawla, Sangwoo Cho, Scott Novotney, Yebowen Hu, Fei Liu, Shi-Xiong Zhang, Sambit Sahu

arXiv:2608.28979v1cs.HC

TL;DR

Users often struggle to articulate the data-dependent constraints and latent expectations needed for effective AI-system requirements. AREAs-Lab combines dataset analysis, targeted clarification, synthetic benchmarking, and simulated-user evaluation; experiments identify data-feature mining as a critical bottleneck, while limitations remain in behavioral fidelity and deployment governance.

  • Problem

    AI requirement elicitation remains difficult because users struggle to translate vague objectives and latent expectations into operational specifications, especially when requirements depend on data characteristics.

  • Method

    AREAs-Lab iteratively analyzes underlying datasets and asks targeted clarification questions, evaluating the process with a 16-dataset synthetic benchmark and an AI-simulated user.

  • Results

    Experiments identify actively mining underlying data features as a critical performance bottleneck for high-quality requirement elicitation.

  • Takeaways & Limitations

    AREAs-Lab provides a scalable, controlled testbed for studying and improving AI-driven requirement elicitation.

  • Takeaways & Limitations

    Simulated users differ from real humans, and the synthetic benchmark does not reproduce governance constraints such as privacy, security, approvals, or compliance review.

Abstract

from arXiv · show

Building effective AI systems increasingly depends on writing high-quality task requirements, yet users often struggle to articulate the constraints, preferences, and edge cases that determine success. This problem is especially acute in AI development, where behavior is shaped not only by human expectations but also by data characteristics. We present AREAs-Lab, an interactive environment for AI-driven Requirement Elicitation for AI systems. In AREAs-Lab, an assistant iteratively refines an initially incomplete requirement by analyzing the underlying dataset and asking targeted clarification questions to uncover the user's latent intent. To study this setting systematically, we construct a synthetic benchmark grounded in 16 public datasets spanning diverse domains and task types. Each benchmark instance includes a user profile, a complete reference requirement, and an intentionally underspecified version that serves as the assistant's starting point. We further introduce an automated evaluation pipeline based on an AI-simulated user that reveals hidden information only when appropriately prompted, enabling scalable and reproducible assessment of interactive elicitation quality. AREAs-Lab provides a controlled testbed for studying how AI assistants can transform vague user goals into actionable requirements for AI systems.

1 Introduction

AREAs-Lab addresses the unresolved challenge of eliciting latent, data-dependent requirements for AI systems. It combines dataset analysis with targeted user interaction to refine incomplete specifications and evaluates this process in a controlled benchmark environment.

  • AI requirement elicitation remains unresolved because users struggle to translate vague objectives and latent expectations into operational specifications.
  • AI requirements differ from deterministic software requirements because they are stochastic, data-coupled, and refined through iterative data exploration and human interaction.
  • AREAs-Lab uses an assistant that analyzes datasets, identifies underspecified elements, and asks clarification questions until requirements align with latent user intent.
  • The benchmark derives tasks from 16 public datasets and pairs user profiles and complete requirements with skeletal requirements that intentionally withhold essential constraints.
  • An AI-simulated user selectively discloses latent constraints in response to targeted inquiries, enabling scalable and reproducible evaluation against reference requirements.
  • AREAs-Lab prioritizes simultaneous interaction with users and underlying data in a domain-agnostic environment for AI system requirement elicitation.

2 Related Works

Prior work studies interviews, multi-agent elicitation, dialogue analysis, human-in-the-loop collaboration, prompt optimization, and general AI environments. AREAs-Lab differs by centering data-aware requirement elicitation and evaluating it across diverse domains.

  • Existing requirement-elicitation research includes autonomous LLM interviews, multi-agent frameworks, structured dialogue analysis, and human-in-the-loop collaboration.
  • Other studies examine interaction strategies or use AI to train humans in requirement-elicitation skills.
  • AREAs-Lab targets AI specifications whose requirements emerge from user intent and data characteristics, with 16 data sources across 10 distinct domains.
  • Prompt-optimization research spans search-based, reinforcement-learning, meta-reasoning, compilation, evolutionary, Bayesian, gradient, and critique-based methods.
  • General AI environments emphasize structured reasoning, robustness, user-centered design, or social behavior simulation rather than formalized user-needs understanding through automated evaluation.

3 Environment Construction

AREAs-Lab constructs a synthetic benchmark by combining extracted data features with user personas to generate requirements, then filters and validates the resulting instances. Its simulated users disclose information selectively under controlled interaction styles.

  • Benchmark construction: The benchmark begins with 16 public data sources spanning multiple domains and task settings, which ground synthesized personas and task requirements rather than serving as instances directly.
  • Benchmark construction: The synthesis pipeline extracts schema and instance features, creates personas with skills and objectives, and combines them to generate high-fidelity requirements at multiple difficulty levels.
  • Quality control: Two high-capability LLMs independently audit persona-task triplets across six dimensions, assigning scores from 1 to 5 to reduce self-reinforcement bias.
  • Quality control: Instances are discarded below an average score of 4.0 or below 3.0 on any critical dimension, yielding 151 data source–user–task tuples.
  • Simulated users: Simulated users respond using the assistant’s question, persona, full requirement, and interaction style, while withholding requirements unless relevantly prompted.
  • Simulated users: Passive, Normal, and Active styles vary in expressiveness, directness, and willingness to disclose relevant details when appropriately triggered.

4 Experimental Design

The experiments compare five elicitation strategies and assess generated requirements through simulated-user interaction and atomic semantic alignment with reference requirements. The design also varies interaction rounds by strategy.

  • The study asks which elicitation strategies are most effective, whether more interaction rounds improve performance, and how user cooperation affects requirement quality.
  • The five strategies include No Interaction, Data Interaction, User Interaction (Fixed), User Interaction, and Hybrid Interaction.
  • Data Interaction provides dataset exemplars for inferring requirements from data structures, formatting artifacts, schema conventions, and edge cases.
  • User and Hybrid Interaction run for 10 rounds, whereas Data Interaction uses one round because additional iterations do not significantly improve performance.
  • After simulated-user conversation, the assistant’s generated requirement is compared with the reference full requirement.
  • Atomic evaluation decomposes requirements into indivisible units and uses LLM-based semantic matching to identify directional entailments or entailment pairs.

5 Main Results and Analysis

Interaction generally improves requirement elicitation, with hybrid user-and-data interaction achieving the strongest overall performance while adaptive questioning helps recover data-derived constraints. However, interaction can increase variance, and performance remains limited by information asymmetry, metric strictness, and task-specific noise.

  • Interaction Strategy Comparison: Hybrid Interaction achieves the highest overall F1 across all three backbone models by combining adaptive user clarification with direct data inspection.Its data-grounded clarification produces more task-specific questions than user-only interaction.
  • Interaction Strategy Comparison: All interaction-based strategies outperform No Interaction, but they exhibit higher F1 variance because interaction can add irrelevant, redundant, or conflicting specifications.The resulting variability reflects a trade-off between broader requirement coverage and precision across tasks.
  • Interaction Strategy Comparison: 41.64 is the best overall F1 reached by frontier models, reflecting the benchmark’s information asymmetry and strict atomic evaluation metric.Relative strategy comparisons remain meaningful because all approaches share the same reference requirements and evaluation protocol.
  • Interaction Strategy Comparison: Data-derived requirements have higher F1 variance, especially under interaction-based strategies, because task-specific constraints differ in complexity and salience.Some constraints are visible in schemas or examples, while others require recognizing subtle patterns or rare edge cases.
  • Interaction Strategy Comparison: 40.06 F1 for User Int. (Adaptive) versus 29.87 for User Int. (Fixed) and 27.58 for Data Interaction on data-derived requirements.Adaptive interaction asks users about expected data characteristics, uncovering task-specific constraints without direct data access.
  • Impact of Interaction Rounds: Across interaction rounds, Data Interaction remains largely stagnant, whereas User Interaction and Hybrid Interaction show consistent average improvement.Hybrid filters data-derived features through user confirmation, reducing distracting information and addressing task-specific gaps; individual rounds can still fluctuate.
  • Impact of User Collaboration: Performance consistently improves as simulated users become more collaborative under Hybrid Interaction.Active users provide additional relevant information beyond answering questions, helping the assistant understand the task more fully.

6 Human Assessment

Human assessment examined the assistant’s interaction quality, requirement quality, and alignment between human and simulated users. Results were strong on coverage, requirement quality, and experience, while inspiration and behavioral fidelity remained weaker.

  • Human evaluation procedure: 12 human evaluators initiated a 10-round Hybrid Interaction dialogue, after which they assessed the generated requirement against their original text.The evaluation used four dimensions scored on a 1–5 Likert scale.
  • Human evaluation results: Coverage and Requirement Quality each achieved a mean score of 4.36, while general Experience reached 4.00.Evaluators found the system accurate, usable, and pleasant to co-create with.
  • Human evaluation results: Inspiration scored 3.73 with wider variance, indicating less consistent introduction of novel perspectives or unexpected insights.The assistant more reliably fulfilled explicit requirements than generated unexpected perspectives.
  • Human evaluation results: Human-written requirements were generally less complex than synthesized tasks, and evaluators often addressed unexpected elements during interaction.These observations highlight the role of elicitation in uncovering requirements users had not initially considered.
  • Human–simulated response alignment: 63%, 59%, and 68% of simulated responses were Similar or Partially Similar to human feedback in Active, Normal, and Passive settings, respectively.The Passive setting had the lowest Different rate at 32%, with simulated responses averaging 24 versus 17 words for humans.
  • Human–simulated response alignment: Simulated responses differed from human behavior in 32%–41% of cases, so the simulator is a controlled proxy rather than a faithful model of real-user elicitation dynamics.The alignment analysis captures selected disclosure behaviors rather than full behavioral equivalence.

7 Conclusion

AREAs-Lab establishes a controlled environment for evaluating AI-driven requirement elicitation through a diverse synthetic benchmark and agent-simulated pipeline. Experiments identify active mining of underlying data features as a critical performance bottleneck.

  • Conclusion: AREAs-Lab introduces a scalable, controlled, and domain-agnostic environment for studying AI-driven requirement elicitation.The framework is paired with a diverse synthetic benchmark and an agent-simulated evaluation pipeline.
  • Conclusion: Experiments across four interaction strategies identify the necessity of actively mining underlying data features for high-quality requirement elicitation.The conclusion frames data-feature mining as a critical performance bottleneck for assistants.
  • Conclusion: The released resources are intended to support development of more sophisticated elicitation-capable AI systems.The paper makes the framework’s code and data available to the community.

8 Limitations

AREAs-Lab’s evaluation remains bounded by gaps between simulated and real users, text-based and structured modalities, dependence on underlying LLMs, and synthetic benchmark coverage.

  • Behavioral validity: Simulated users do not reproduce the full spectrum of human cognitive and conversational behavior, including shifting attention, emotional friction, inconsistent preferences, and fatigue.Larger and more diverse randomized human studies are needed to establish external validity.
  • Modality scope: The benchmark is constrained to text-based and structured data, leaving multimodal requirement elicitation involving mockups, diagrams, and complex schemas open.Extending the environment to multimodal interaction remains an open challenge.
  • LLM dependence: Evaluation depends on foundation-model context limits and instruction-following stability, with ultra-long dialogues vulnerable to information decay or hallucination.These effects can introduce variance into evaluation metrics.
  • Benchmark scope: The synthetic benchmark may not fully capture niche or proprietary enterprise workflows or their legal, organizational, privacy, and security constraints.The framework does not evaluate role-based access, PII redaction, organizational approvals, or compliance review.

9 Potential Risks

Deploying elicitation assistants in enterprise or high-stakes settings introduces privacy, reliability, and governance risks. Data-derived requirements require traceability and confirmation, while compliance claims require expert review.

  • Deployment risks: Data inspection may expose sensitive information, while assistants may infer unsupported requirements and influence downstream systems through generated specifications.These risks are especially relevant in enterprise and high-stakes deployments.
  • Requirement drift: Multi-turn interaction can introduce irrelevant or conflicting constraints, and data artifacts or correlations can cause requirement drift from stakeholder objectives.Data-derived requirements should be traceable to observations and distinguished from user-confirmed requirements.
  • Governance risks: Unsupported legal or compliance claims may be generated or applicable constraints may be overlooked when absent from inspected data or undisclosed by users.Proposed compliance requirements require qualified-expert review against authoritative policies and legal sources.
  • Governance risks: Generated specifications may appear authoritative, creating automation bias and unequal stakeholder influence.The cited risk section identifies this as a consequence of relying on assistant-produced specifications.

A.1 Data Source

The benchmark draws on public, content-rich datasets spanning diverse domains to support transparent, realistic, and broad study of user-needs understanding.

  • The selected data sources are publicly accessible through Hugging Face Datasets, supporting transparency and reproducibility.
  • Each source contains sufficiently rich content to generate realistic user profiles and task requirements.
  • The sources span diverse domains, enabling analysis of user-needs understanding beyond a single application setting.

A.2 Synthesized Data Analysis

The synthesized benchmark emphasizes complex, high-accountability user profiles and tasks requiring varied structural and cognitive demands. Its analyses separate user-defined from data-derived requirements and evaluate interaction, human grounding, and metric alignment, while identifying important scope and evaluation limitations.

  • User Roles Distribution: 80 synthesized user profiles are predominantly complex, high-stakes professionals requiring transparency, controllability, and domain-appropriate reasoning.
  • Task Difficulty Distribution: 151 synthesized tasks are classified by structural complexity and cognitive load into four regimes defined by median scoring thresholds.
  • Requirement Source Analysis: Task requirements combine user-defined constraints and goals with data-derived constraints inferred from dataset formats, structures, and input-output patterns.
  • Impact of Data Instances for Interaction: 33.79% F1 is achieved with defining data, compared with 23.79% F1 for No Interaction, while non-defining data reaches 25.68 F1.
  • Impact of Data Instances for Interaction: Active collaboration consistently outperforms Passive and Normal settings across Medium and Hard tasks, whereas Passive collaboration is lower and more variable.
  • Human Evaluation Details: 12.11 precision, 64.07 recall, and 18.55 F1 were obtained for 12 AI-generated requirements, alongside a 134.3-versus-694.8-word length mismatch.
  • Limitations: The simulator differs from real humans, and the automated metric is intended for controlled relative comparisons rather than replacing human evaluation.
  • Human Evaluation Details: Automated rankings align positively with human evaluators, although pair-level matcher agreement is more moderate than evaluator-to-evaluator agreement.
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