AI Confederate: Methodology and Validity

An AI confederate is a scripted or model-driven agent that stands in for a human accomplice in deception, social-pressure, and negotiation paradigms. Unlike a trained human confederate, its behavior can be specified, logged, and reproduced exactly across every participant, which is the central methodological argument for using one, and also the reason it needs to be evaluated as a design decision rather than adopted by default.

This page treats AI as a tool standing in for a human role inside an otherwise classic paradigm: the AI is the instrument, not what is being measured. That is a different question from studies where the AI itself is the subject under study, covered in the FAQ below.

What Counts as an AI Confederate

A confederate, in the classic sense, is someone who appears to the participant as another participant or an ordinary interaction partner but is in fact acting on a script the experimenter controls, most familiar from deception paradigms (a fake co-participant in a conformity study), social-pressure paradigms (a partner who behaves uncooperatively in a trust game), and negotiation or bargaining tasks (a counterpart whose offers follow a predetermined pattern). An AI confederate stands in for that human accomplice with a model-driven agent: the participant still believes, or is led to believe, they are interacting with another party in the study, but the party's responses are generated or scripted through an AI system rather than performed by a trained human actor.

This is not a new experimental design. It is an alternative approach within designs that already exist, and the field already has a lower-tech version of the same idea: Labvanced's own multi-user studies documentation describes a "virtual opponent" strategy, where a participant is told they are playing against another participant but the other side is actually an algorithm making randomized choices. That approach is a genuine ancestor of the AI-confederate idea, but a limited one: a randomized-choice algorithm cannot hold a scripted social-pressure exchange, negotiate, or sustain a believable deceptive conversation. What generative AI adds is the capacity to produce natural, context-responsive language, and in some designs audio or images, in place of a human confederate's live improvisation, which is what makes this approach viable for paradigms a random-choice algorithm never could support.

The Case for an AI Confederate

An AI confederate is not a full-on replacement for a human one; it is another viable, and in some designs more powerful, option. Four considerations argue for it: control, replicability, ethics, and scale.

Control

A trained human confederate is asked to reproduce a fixed behavioral protocol across every participant: the same tone, the same pushback, the same timing of a concession in a negotiation task. In practice, confederates drift. Fatigue, mood, and the accumulated experience of running many sessions all introduce variation the researcher did not design in, and that variation is a confound because it correlates with time and session order rather than with the experimental manipulation. An AI agent generating from a fixed prompt and a fixed configuration does not fatigue or accumulate session experience the way a person does, which removes that specific source of between-session drift, though it introduces its own consistency requirements (see below).

In Labvanced's implementation: the same Send to OpenAI configuration runs unchanged for every session on the task, since it is a fixed setting rather than a protocol a person has to reproduce from memory each time.

Replicability

A human confederate's exact behavior is rarely fully specified in a methods section. Even a detailed script leaves gaps a human actor fills in with judgment, so a different lab attempting to replicate the study is reproducing an approximation of the original confederate's performance, not the performance itself. A documented prompt, model version, and generation parameters are a complete, portable specification: another lab can run the identical configuration rather than train a new actor to approximate the original one.

In Labvanced's implementation: model version, temperature, and the prompt itself are saved directly in the task configuration, so the complete specification this section calls for is not extra documentation work, it is what is already stored.

Ethics

Deceiving a participant with a live human accomplice carries an ethical weight that most institutional review processes already require researchers to justify and debrief: an actual person was, in some sense, complicit in the deception. An AI-generated confederate changes the ethical texture of that arrangement, though it does not remove the underlying deception in the study design itself, which still requires the same debriefing and consent handling any deception paradigm requires. This softens one specific ethical dimension of confederate use; it does not make deception paradigms ethically simpler overall.

Scale

Online and remote studies can recruit far more participants than a single lab session ever could, and that creates a scaling problem specific to human confederates: every additional participant needs another confederate session, so a larger sample means recruiting, training, and scheduling more confederates, plus keeping all of them behaviorally consistent with each other, on top of the within-confederate drift Control (above) already covers. An AI confederate does not have this problem. The same configuration runs for the thousandth participant exactly as it did for the first, with no added staffing, training, or inter-confederate consistency checks to manage. That is what makes an AI confederate especially well suited to large-sample online research.

In Labvanced's implementation: the same AI confederate configuration runs unmodified whether a study collects 20 participants or 2,000, since it is a task setting rather than staffing that has to scale with sample size.

Methodological Considerations When Using AI Confederates

Adopting an AI confederate is not a plug-and-play choice. It introduces its own design requirements, and skipping them just trades a human-confederate confound for a model-confederate one.

Controlling AI behavior as an independent variable

If the AI confederate's responses vary unpredictably across participants, in tone, in how cooperative or uncooperative it appears, in response length, the study has reintroduced exactly the kind of uncontrolled variation an AI confederate is meant to remove. Labvanced's AI and psychology research integration, built on native OpenAI text, image, and audio generation, exposes the specific parameters a researcher needs to treat the confederate's behavior as a controlled variable rather than an assumption:

• A Model Version pinned for the study's duration, so a provider-side model update between sessions cannot change the confederate's behavior mid-study.
• A detailed system-role message, set via Insert Message, that puts the confederate's persona and behavioral boundaries in writing rather than leaving them to the model's default judgment: what language register and slang fit the participant pool, how far its awareness of current events should extend, and how it should respond if a participant directly asks whether it is an AI.
• Temperature set toward the deterministic end of its range for a scripted-feeling confederate, since temperature governs how random versus predictable the generated response is.
• Max Tokens capped to keep response length consistent across participants, since an uncapped confederate can vary widely in how much it says from one session to the next.
• For audio-based confederates, a fixed Voice and written Instructions (for example, specifying a neutral, calm delivery) to hold vocal tone constant across participants rather than leaving it to vary trial by trial.
• A linked Chat History Dataframe, so the full exchange with every participant is logged and available for post-hoc verification that the confederate behaved consistently, the same kind of audit trail a manipulation check provides for a human confederate's script adherence.

Together, these settings help ensure greater consistency for genAI performance across sessions. They are levers a researcher has to pull deliberately, the same way a human-confederate study requires a training protocol and inter-session fidelity checks rather than assuming an actor will reproduce a script unaided.

OpenAI Text-based Chat

This template sets up the real-time communication channel between Labvanced and OpenAI, the basis any confederate-based study built on this integration relies on. Use it as the starting point and modify the prompt, model, and parameters to fit your paradigm.

Note: For this particular demo, the token length (ie. the length of the response) is set to 100 and the temperature (ie. creativity) is set to 1. This can be adjusted under the Study Settings.

Prompt design as a validity variable

The system-role message is where the researcher writes the confederate's entire behavioral policy by hand: its language register and slang, how far its awareness of current events extends, and how it responds if a participant directly asks whether it is an AI. Two labs running the same substitution with different slang calibration, a different current-events cutoff, or a different scripted answer to the AI question could get meaningfully different confederate behavior, a form of prompt sensitivity well documented in language-model research generally (Zhuo et al., 2024). A study that finds participants respond differently to an AI confederate than to a human one could be picking up a genuine substitution effect, or it could be picking up an under-specified or poorly designed prompt on one of these dimensions. Treating prompt design as a variable to be reported and scrutinized, rather than an implementation detail, is what makes those two explanations distinguishable.

In Labvanced's implementation: this is the system-role message set via Insert Message, saved once as part of the task rather than typed fresh for each session, which is what makes it possible to report and audit exactly as written.

The same authorship also opens a deliberate use: a researcher can vary the system prompt across conditions as the manipulation itself, for instance, a cooperative-persona confederate versus a competitive-persona confederate in a negotiation task. Here the prompt is itself the independent variable, deliberately varied across conditions rather than held constant, and the methodological bar shifts accordingly: prompt variants need to differ only along the intended dimension (cooperative vs. competitive) rather than incidentally along others (length, formality, vocabulary), or the manipulation is confounded before the study begins. A manipulation check on how participants actually perceived the confederate along the intended dimension becomes essential here, the same way a pilot test would confirm a human confederate's scripted persona came across as intended.

Paradigm fit

An AI confederate suits paradigms where the interaction can be specified in advance or generated from a bounded set of response types, deception scenarios, structured social-pressure exchanges, negotiation and bargaining tasks with a defined offer space. An AI negotiation study is one of the clearest fits here, since offers and counter-offers fall within a defined space rather than requiring open-ended improvisation. It suits paradigms less well where the interaction depends on the confederate improvising convincingly outside that bounded space, or where the deception depends specifically on the participant believing they are dealing with a same-status human peer in a context where an AI presence, once suspected, would collapse the manipulation entirely.

In Labvanced's implementation: the Send to OpenAI action generates the confederate's side of a deception, social-pressure, or negotiation exchange within a single-participant study. The confederate is not a second real connected user, so building these paradigms does not require Labvanced's multi-user infrastructure, the participant's session is the only live one.

The open validity question

Whether a participant who is deceived by an AI-driven confederate behaves the same way as one deceived by a human confederate, in a trust game, a conformity paradigm, or a negotiation task specifically, has not yet been directly tested: the substitution is new enough that this head-to-head comparison simply hasn't been run yet. It is plausible that suspecting or discovering an AI presence changes the social stakes of the interaction in ways a human confederate does not, which would mean an AI confederate trades a human-consistency confound for a different, AI-specific one rather than simply removing a confound. A rigorous use of an AI confederate treats this as an open empirical question to be checked, for instance through a manipulation check on whether participants correctly believed they were interacting with a human, rather than an assumption resolved by design.

Frequently Asked Questions

Further Reading

Working on a dyadic or group interaction design and want the broader context? The dyadic effect in psychology research covers how the presence of another party changes individual behavior in paired designs, the same territory a human or AI confederate operates in. A related pattern shows up wherever more than a simple pair is involved, covered in team dynamics research.

Where a negotiation or bargaining paradigm is the target design, the Ultimatum Game task page is a concrete example of the bounded-offer-space structure an AI confederate is well suited to generate within.