arxiv: 2604.24461 · v1 · submitted 2026-04-27 · 💻 cs.HC · cs.AI

Recognition: unknown

Measuring Successful Cooperation in Human-AI Teamwork: Development and Validation of the Perceived Cooperativity and Teaming Perception Scales

Christiane Attig , Christiane Wiebel-Herboth , Patricia Wollstadt , Tim Schrills , Mourad Zoubir , Thomas Franke

Authors on Pith no claims yet

Pith reviewed 2026-05-08 02:17 UTC · model grok-4.3

classification 💻 cs.HC cs.AI

keywords human-AI cooperationperceived cooperativityteaming perceptionscale validationjoint activity theorypsychometric scalesteamwork measurementhuman-AI interaction

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The pith

Two new scales reliably measure how well humans perceive cooperation with AI partners across tasks.

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

The paper develops two questionnaires to quantify the subjective quality of human-AI teamwork. The Perceived Cooperativity Scale draws on joint activity theory to rate an agent's capability and behavior during one interaction. The Teaming Perception Scale draws on evolutionary cooperation theory to capture the emerging sense that the two parties are working as a team. Three studies with 409 participants, using a card game, LLM conversation, and decision-support system, showed both scales are reliable, valid, and able to separate high-quality from low-quality cooperation partners. The scales also adapt to human-human settings for direct comparisons.

Core claim

The authors establish that the Perceived Cooperativity Scale (PCS) and Teaming Perception Scale (TPS) successfully differentiate cooperation partners of varying quality and exhibit construct validity matching theoretical expectations, as shown across studies of cooperative card games, LLM interactions, and decision-support systems with a total of 409 participants.

What carries the argument

The Perceived Cooperativity Scale (PCS), which rates perceived cooperative capability and practice in a single interaction sequence, and the Teaming Perception Scale (TPS), which measures the emergent sense of teaming from mutual contribution and support; both are grounded in existing cooperation theories and adapted for human-AI use.

If this is right

Researchers gain standardized tools to study what improves or harms human-AI cooperation in varied settings.
AI developers can test whether system features increase perceived cooperativity and teaming.
The scales enable direct comparison of cooperation quality between human and AI partners.
They supply a foundation for evaluating subjective teamwork in gaming, conversational, and decision-support applications.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Widespread adoption could steer AI design toward measurable cooperative behaviors rather than isolated performance metrics.
The scales might reveal systematic differences in how humans experience cooperation with AI versus other humans, informing trust and agency research.
Extending validation to multi-turn, real-world tasks like medical diagnosis or vehicle control could test whether the measures generalize beyond the three lab scenarios.
The work opens the possibility of using the scales as outcome measures in training programs that teach humans to cooperate more effectively with AI.

Load-bearing premise

The chosen tasks and theoretical models translate into questionnaire items that capture the essential aspects of perceived cooperativity without task-specific bias or missing elements.

What would settle it

A new study in an untested domain, such as long-term collaborative planning, where the scales fail to show differences between high- and low-cooperation AI partners or lack expected correlations with objective team outcomes.

Figures

Figures reproduced from arXiv: 2604.24461 by Christiane Attig, Christiane Wiebel-Herboth, Mourad Zoubir, Patricia Wollstadt, Thomas Franke, Tim Schrills.

**Figure 1.** Figure 1: Illustration of two-axis model of synchronic and diachronic dimensions of human-AI integration. Synchronic view at source ↗

**Figure 2.** Figure 2: Histogram of PCS mean scores by sample. Boxplots below represent interquartile range (IQR), vertical lines view at source ↗

**Figure 3.** Figure 3: Histogram of TPS composite mean scores by sample. Boxplots below represent interquartile-range (IQR), view at source ↗

read the original abstract

As human-AI cooperation becomes increasingly prevalent, reliable instruments for assessing the subjective quality of cooperative human-AI interaction are needed. We introduce two theoretically grounded scales: the Perceived Cooperativity Scale (PCS), grounded in joint activity theory, and the Teaming Perception Scale (TPS), grounded in evolutionary cooperation theory. The PCS captures an agent's perceived cooperative capability and practice within a single interaction sequence; the TPS captures the emergent sense of teaming arising from mutual contribution and support. Both scales were adapted for human-human cooperation to enable cross-agent comparisons. Across three studies (N = 409) encompassing a cooperative card game, LLM interaction, and a decision-support system, analyses of dimensionality, reliability, and validity indicated that both scales successfully differentiated between cooperation partners of varying cooperative quality and showed construct validity in line with expectations. The scales provide a basis for empirical investigation and system evaluation across a wide range of human-AI cooperation contexts.

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.

Desk Editor's Note private letter to a colleague

This paper delivers two new theory-grounded scales for measuring how cooperative human-AI interactions feel, backed by three validation studies that hold up under standard checks.

read the letter

The main point is that Attig et al. have built and checked two questionnaires for subjective cooperation quality in human-AI work. The Perceived Cooperativity Scale pulls from joint activity theory to rate an agent's capability and actions in one sequence. The Teaming Perception Scale draws on evolutionary cooperation ideas to capture the feeling of mutual contribution and team emergence. They also created matching human versions so direct comparisons are possible. This setup is new enough to matter for anyone who needs consistent measures beyond ad-hoc questions. The three studies cover a card game, LLM chats, and a decision-support tool with 409 participants total. The scales separated partners by cooperation level and matched expected correlations with other variables. Psychometric results on structure, reliability, and validity look clean and consistent with the claims. The approach avoids obvious circularity by starting from external theory and testing in fresh contexts. One soft spot is that the tasks stay fairly structured and short. Broader or messier real-world cooperation might need extra checks later, but that does not undermine the current evidence. The work targets researchers and developers who evaluate human-AI teams and want reusable instruments. It supplies practical tools without overclaiming theoretical shifts. The methods are transparent enough and the contribution focused enough that it deserves full peer review rather than an early pass.

Referee Report

0 major / 2 minor

Summary. This paper claims to introduce and validate two scales for measuring successful cooperation in human-AI teamwork. The Perceived Cooperativity Scale (PCS) is grounded in joint activity theory to capture perceived cooperative capability and practice, while the Teaming Perception Scale (TPS) draws from evolutionary cooperation theory to assess the emergent sense of teaming. Both scales are adapted for human-human use. Validation across three studies (total N=409) in contexts including a cooperative card game, LLM interaction, and a decision-support system shows the scales have appropriate dimensionality, reliability, and validity, and can differentiate between partners of varying cooperative quality.

Significance. Should the reported psychometric properties hold upon detailed inspection, these scales would represent a significant contribution to human-AI interaction research by providing standardized, theory-based tools for assessing cooperation quality. This would facilitate empirical studies and evaluation of AI systems designed for teamwork. The inclusion of human-human adaptations and testing in diverse tasks strengthens the potential applicability across contexts.

minor comments (2)

[Abstract] Consider adding specific reliability and validity statistics (e.g., alpha values or correlation ranges) to the abstract for a more informative summary.
[Methods] The description of how the scales were adapted from theory to items and for human-human comparisons could be expanded for replicability.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive summary, recognition of the scales' potential significance, and recommendation for minor revision. We are pleased that the contribution to standardized measurement of human-AI cooperation quality is viewed favorably.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper grounds PCS in joint activity theory and TPS in evolutionary cooperation theory (external sources), adapts them for cross-agent comparison, then validates dimensionality/reliability/validity via three independent empirical studies (N=409 total) against expected patterns of differentiation and construct correlations. No equations, fitted parameters, or self-citations are load-bearing; the central claim is standard psychometric testing in new contexts and does not reduce to any input by construction. This is a self-contained, non-circular scale-development process.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on the validity of applying established social science theories to human-AI contexts and on the psychometric properties emerging from the empirical studies.

axioms (2)

domain assumption Joint activity theory provides a valid basis for measuring perceived cooperativity in human-AI interactions.
Used to ground the PCS scale development.
domain assumption Evolutionary cooperation theory provides a valid basis for measuring teaming perception in human-AI interactions.
Used to ground the TPS scale development.

pith-pipeline@v0.9.0 · 5485 in / 1460 out tokens · 76543 ms · 2026-05-08T02:17:45.103340+00:00 · methodology

discussion (0)

Reference graph

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