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Yes, presentation can change how people treat an AI assistant, and one 2026 experiment found lower monetary rewards for a female-presenting assistant. That result does not show that customers generally pay less for feminine-sounding software. The strongest study changed more than voice: participants saw assistants with different names, avatars and levels of human likeness. In that controlled setting, the female-presenting assistant was judged less human and received fewer allocated rewards than a technically identical male-presenting assistant.
Voice studies also report differences in interruptions, politeness and perceived warmth, but the direction depends on the task, accent, error response and who is listening. “Feminine” is therefore not one variable. It can mean a perceived vocal gender, a name, visual embodiment or a conversational style, and those cues should not be treated as interchangeable.
What the best direct evidence shows
The clearest monetary evidence comes from a peer-reviewed paper by Isabelle Cuber, Tarek Alakmeh, Mary Hausfeld, Anand van Zelderen, Moritz Jenny, Jochen Menges and Thomas Fritz in the Proceedings of the 14th Nordic Conference on Human-Computer Interaction (NordiCHI ’26), published on 3 October 2026. The experiment took place in a controlled virtual-reality workplace and involved 189 participants who completed work tasks, rated assistants and allocated monetary rewards. Researchers also interviewed 34 participants afterward.
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- human-like assistants presented as Johanna (female) or Johan (male).
The more human-like assistants received more reward overall. Within the human-like conditions, the female-presenting assistant was perceived as less human-like and received lower rewards than the male-presenting counterpart. The finding is a group-level difference in one constructed workplace scenario, not a market price observation.
Participants were assigned to either the male-presenting or female-presenting condition rather than comparing both assistants directly. That between-groups design means individual preferences cannot be inferred from a person’s allocation. It also means the study does not isolate a feminine voice from the avatar, name, visual appearance and other embodiment cues. The authors reported that female presentation coincided with lower perceived human likeness, which may be related to the reward decision.
Thomas Fritz, a professor of informatics and study co-author, described the result this way: “I found it surprising that AI assistants presenting as male were perceived as more human and received higher rewards than female ones, although they were technically identical. The only thing that was different was their representation.” He later cautioned that the researchers “can’t conclusively say what causes this discrepancy.”
Does this mean users pay less for feminine-sounding AI?
No general subscription, license or consumer willingness-to-pay effect has been established by the evidence described here. Allocating a reward in a laboratory or VR task is not the same as choosing between paid products, accepting a price, renewing a subscription or negotiating a contract.
The 2026 experiment supports a narrower statement: in that workplace simulation, participants gave less monetary reward to a female-presenting assistant than to a male-presenting counterpart described as technically identical. It does not support a claim such as “customers pay 10% less for female voices,” because the study did not measure a percentage price gap, real purchases or a labor-market wage.
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Commercial outcomes could be influenced by many factors that were not tested together: perceived competence, trust, brand familiarity, voice quality, task performance, pricing, accessibility and whether the assistant is used for a social or functional job. A product team should therefore treat the finding as a warning about possible evaluation bias, not as a pricing rule.
What voice-assistant studies add
A 2025 simulated-shopping study
A Johns Hopkins University report dated 16 January 2025 describes an in-person study with 40 participants. People used a simulated assistant for an online shopping task while researchers varied the assistant’s feminine, masculine or gender-neutral voice and changed how it handled errors: it either apologized or offered monetary compensation.
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Amama Mahmood, the study lead and a PhD student in the Whiting School’s Department of Computer Science, argued that “thoughtful design—especially in how these agents portray gender—is essential to ensure effective user support without the promotion of harmful stereotypes.”
A 2024 voice, accent and task experiment
A Korea University research record describes a 2024 experiment with 270 participants. It varied Siri’s perceived voice gender, American or Indian accent, social or functional task, and whether the assistant’s gender matched the participant. This design is important because a response to a voice may depend on accent, task and listener–assistant gender matching. A result observed with a social task and one accent should not be generalized to every voice interface.
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Taken together, these studies support a context-dependent conclusion: perceived gender can affect interaction, but it is only one part of a larger design and social situation.
Why “feminine” is not the same as “female voice”
When people describe an assistant as feminine, they may be reacting to several cues at once:
- Acoustic cues: pitch, resonance, speaking style, rhythm and pronunciation can influence perceived gender.
- Identity cues: a name such as Johanna or Johan changes the interpretation of an otherwise similar system.
- Visual embodiment: an avatar, robot body or human-like face can make an assistant seem more or less human.
- Conversational behavior: apology style, warmth, deference, assertiveness and turn-taking can be gender-coded by users.
- Context: an assistant helping with shopping, work, navigation or emotional support may be judged by different standards.
Because these cues can move together, an observed reward or trust difference cannot automatically be attributed to vocal sound. A fair test must hold the words, timing, task success and interface constant while changing one cue at a time, or use a factorial design that can estimate interactions.
How treatment differs from payment
“How users treat an assistant” covers observable conduct such as interruptions, politeness, cooperation, ratings and social distance. “Pay” can mean at least three different outcomes:
| Outcome | What it measures | What the current evidence says |
|---|---|---|
| Interaction behavior | Interruptions, impolite turns, cooperation or social engagement | Differences appeared in the 40-person 2025 study, but depended on voice and error handling. |
| Perceived human likeness | Whether the assistant is judged human-like | The female-presenting assistant was judged less human-like in the 189-person 2026 VR study. |
| Experimental reward | Money allocated inside a controlled task | The female-presenting assistant received lower allocations than the male-presenting counterpart in that study. |
| Consumer willingness to pay | Price a person accepts for a product or subscription | Not established by these sources. |
| Market price or wage | Observed prices, revenue or compensation in commerce | Not measured by these studies. |
Keeping these outcomes separate prevents an experimental allocation from being reported as a proven commercial discount.
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What the studies say about stated beliefs versus behavior
The University of Zurich account of the 2026 work reports a gap between what participants said and what they did. Many interviewees said gender did not matter, while reward allocations differed between conditions. Interviews can reveal conscious beliefs, but an allocation captures a decision made under the experiment’s incentives and presentation. The mismatch does not identify a single cause: participants may have been unaware of a bias, may have responded to human-likeness cues rather than gender, or may have interpreted the task differently than researchers intended.
This is why product research should record both self-report and behavior. Asking testers whether a voice feels competent is useful, but it should be paired with interruption logs, task success, time to completion, retention and blinded evaluations.
Could a gendered assistant change attitudes toward people?
Evidence about spillover to human gender stereotypes is mixed. An open-access 2022 study summary describes work on perceived voice gender and agency and refers to earlier findings in which exposure to a female-featured assistant affected some participants’ expectations and treatment of female subordinates. A 2025 study summary, however, reports no statistically significant effect of gendered assistant interaction on the explicit stereotype measures it tested.
These findings are not contradictory unless they are treated as measuring the same thing. Direct treatment of an assistant, implicit expectations, workplace behavior and explicit questionnaire scores are different outcomes. The current evidence does not justify saying that a feminine assistant either inevitably reinforces stereotypes or has no social effect.
How to evaluate a feminine-sounding assistant fairly
1. Define the outcome before changing the voice
Decide whether the goal is fewer interruptions, higher task completion, better accessibility, more trust, perceived warmth or revenue. Do not use “users like it” as a substitute for a measurable endpoint.
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2. Separate voice from embodiment
If you are testing vocal gender, keep the name, avatar, animation, wording, response time and task performance constant. If you want to test a complete persona, label the result as a persona or embodiment effect rather than a voice-only effect.
3. Balance the participant groups
Use comparable groups and report who was tested, how many people participated and whether each person heard multiple conditions. The 189-person VR study and the 40-person shopping study are informative samples, not population prevalence estimates.
4. Include task and error conditions
Test functional and social tasks separately. Record whether an error produces an apology, explanation, correction or compensation, because the Johns Hopkins findings show that error handling can alter interruptions and politeness.
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Log turn-taking, interruptions, completion and ratings. If money matters, run a clearly defined willingness-to-pay or purchase-choice study rather than inferring price from a laboratory reward allocation.
6. Check for accent and gender-match effects
Include the accents and locales relevant to your users, and test whether participant–assistant gender matching changes responses. The 2024 270-participant experiment demonstrates why a single voice sample cannot stand in for every audience.
7. Audit for unequal service quality
Compare response accuracy, escalation rates, refusal behavior and time to resolution across voices. A voice should not be made warmer by making it less assertive, less informative or more apologetic unless users explicitly need that behavior.
Common mistakes when interpreting this evidence
- Calling the VR reward an actual price gap: it was an experimental allocation, not a subscription or market transaction.
- Blaming sound alone: the monetary study changed representation, including human likeness and presentation.
- Generalizing from 40 participants: the shopping study was small and used a simulated task.
- Ignoring accent and task: the 270-person study varied both, showing that context matters.
- Reporting a universal preference: some participants behaved more socially toward a feminine voice, while other conditions reduced interruptions with a neutral voice and apology.
- Using sample size as an effect size: 189, 40 and 270 describe particular studies; they do not indicate how common a bias is.
- Equating self-report with conduct: people can say gender does not matter while behaving differently in an allocation or interaction task.
What a responsible product decision looks like
If testing reveals lower trust, lower reward or more interruptions for a feminine presentation, do not respond by assuming the feminine voice is the problem or by charging different users different prices. First test whether the difference comes from human-likeness, task framing, accent, error handling, response style or participant expectations. Offer users meaningful voice choice where practical, but keep service quality and pricing consistent.
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For a published evaluation, report the exact presentation cues, task, sample, assignment method and behavioral measures. State whether the result is exploratory, whether participants compared conditions directly and whether the study measured payment, reward allocation or only attitudes. That level of precision prevents a narrow laboratory finding from becoming a misleading claim about all feminine-sounding AI.
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