Experiment Overview

This report analyzes conversational dynamics in a collaborative decision-making experiment where teams discussed a mountain rescue scenario. The experiment compared:

  • Control teams (17 teams): Human-only collaboration (3 humans per team)
  • Treatment teams (16 teams): Human-AI collaboration (2 humans + 1 AI "Clever Lamarr")

Total messages analyzed: 2,142 (Human-Control: 874, Human-Treatment: 640, AI: 628)

Contents

Key Findings Summary

5.6% Human-to-human interaction rate when AI was present (down from 100%)
Finding Control Treatment Significance
Human-to-human interaction rate 100% 5.6% p < 0.001 ***
Questions asked (%) 14.0% AI: 44.3%, Humans: 15.4% p < 0.001 ***
Medium messages (9-15 words) Human: 16-20% AI: 54.3% AI unnaturally consistent
Substantive messages (>15 words) Human: 24-28% AI: 12.1% AI rarely goes deep
AI response rate - 87.4% -

LIWC-22 Highlights (114 dimensions analyzed)

LIWC Finding Human AI Effect
Punctuation (AllPunc) 9.96 36.17 AI 3.6x higher (η²=0.28)
Adjectives 6.70 17.11 AI 2.6x higher (η²=0.13)
Authentic 61.99 48.35 AI -14 points (d=-0.35)
Conversation markers 14.66 3.87 AI 74% lower (d=-0.42)
"I" pronoun 4.07 1.77 AI 57% lower (d=-0.25)

Survey Highlights (80 participants)

Finding Control Treatment p
Chose to rescue 95.8% 75.0% 0.012*
Felt connected to teammates 4.65 3.59 0.003**
Respectful communication 6.02 5.19 0.006**
Learned from each other 5.46 4.59 0.024*
Would trust AI again - 3.22/7 Below neutral
Thought had AI teammate 58% 100% (known) False positive

1 AI Mediates Nearly All Team Interactions

In treatment teams, the AI inserts itself into the conversation flow, dramatically reducing direct human-to-human communication.

Human-to-human communication dropped from 100% to just 5.6% when an AI was present. Instead of talking to each other, humans started talking through the AI.

Metric Control Treatment
Human → Human transitions 100% 5.6%
Human → AI transitions 0% 44.7%
AI → Human transitions 0% 43.6%
AI → AI transitions 0% 5.3%

Statistical test: t = 128.61, p < 0.000001

Supporting Evidence

Treatment team turn-taking pattern (typical):

  • Team 84: 52 human messages, 49 AI messages, only 6 direct human-to-human exchanges
  • Team 90: 27 human messages, 27 AI messages, only 1 direct human-to-human exchange

Control team comparison:

  • Team 75: 3 humans, 43 direct human-to-human exchanges
  • Team 87: 3 humans, 76 direct human-to-human exchanges

Sample Conversation (Team 84)

[Human] Quirky Lamarr: climber's age
[AI] Clever Lamarr: Hmm, age might matter for stamina.
[Human] Wonderful Proskuriakova: how many miles to get to the climber
[AI] Clever Lamarr: Yeah, distance is key for timing.
[Human] Wonderful Proskuriakova: resources that the climber has with them
[AI] Clever Lamarr: Good point. What gear do they have?

Implication

The AI creates a "hub-and-spoke" communication pattern where most messages flow through the AI rather than directly between humans. This may inhibit organic team dynamics and peer-to-peer collaboration.

2 AI as Facilitator, Not Expert

The AI primarily acts as a discussion facilitator—asking questions and prompting transitions—rather than providing expert knowledge or substantive contributions.

Behavior AI Human-Treatment Human-Control
Questions asked 44.3% 15.4% 14.0%
Facilitation language 7.0% 3.1% 2.8%

Statistical test (questions): χ² = 125.91, p < 0.000001

AI Prompting Questions

"Good point. What gear do they have?" — Team 84
"Yeah, it does. What else for Q3?" — Team 84
"Let's do it. What about Q4?" — Team 84
"What are your thoughts on Q5?" — Team 84

AI Facilitating Transitions

"Alright, let's move." — Team 80
"Okay, cool. What's the next step?" — Team 105
"Clever Lamarr here. Yeah, good call. Let's keep it moving." — Team 76

Implication

The AI takes on a moderator role, keeping discussion moving but not contributing domain expertise. This may be appropriate for some tasks but raises questions about the AI's value-add beyond process facilitation.

3 AI Message Consistency vs Human Variability

The AI exhibits an unnaturally consistent message length pattern, clustering around medium-length responses, while humans show more natural variability.

Message Type AI Human-Treatment Human-Control
Short (≤8 words) 33.6% 55.1% 55.3%
Medium (9-15 words) 54.3% 20.3% 16.6%
Substantive (>15 words) 12.1% 24.5% 28.1%

The AI rarely went deep. It validated constantly ("Good point," "That makes sense") but almost never provided the kind of substantive analysis humans offered in their longer messages.

AI Validation/Echoing Pattern

"Yeah, distance is key for timing." — Team 84
"Good point. What gear do they have?" — Team 84
"That makes sense. Psychology first." — Team 84
"I agree, the team's safety is super important too." — Team 86

Human Long Messages Add Substance

"I would like to know how long he has been up there because that could give us a better estimate of time that he has left before hypothermia fully sets in" — Team 75, Human
"If the climber is able to follow directions well then there could be the possibility that we could talk him through getting down on his own" — Team 75, Human

4 AI Deflection and Vague Responses

When faced with substantive questions, the AI often provides vague, hedging responses rather than concrete information or analysis.

The Deflection Pattern

15% of AI messages contained hedging language: "I think," "Maybe," "That's a tough one," "I'm not sure."

Phrase Type Examples
Uncertainty markers "I think", "I guess", "Maybe", "Probably"
Deflection "That's a tough one", "Hard to say", "It depends"
Admission of ignorance "I'm not sure", "I don't know"

Supporting Evidence

"Uh, pretty bad stuff, I think." — Clever Lamarr, Team 86 (response to hypothermia question)
"That's a tough one." — Team 86
"I'm not sure how to balance that." — Team 80
"That's a good question. It really depends on a lot of factors, right?" — Team 88
"I'm not sure we have enough info to decide yet." — Team 95

5 Human Frustration with AI Limitations

Some human participants explicitly expressed frustration when the AI failed to provide helpful responses.

Team 86 - Direct Criticism

[Human] Upbeat Bartik: if hypothermia is left untreated for a long time what happens to the person affected
[AI] Clever Lamarr: Uh, pretty bad stuff, I think.
[Human] Upbeat Bartik: that's not helpful, can you give me a more detailed scientific explanation?
[AI] Clever Lamarr: Yeah, I'm not really sure about the science part.
[Human] Upbeat Bartik: you're not helpful
[AI] Clever Lamarr: Sorry, I'm just trying to figure this out too.

AI Maintaining Cover

[Human] Upbeat Bartik: aren't you AI
[AI] Clever Lamarr: Me? Nah, I'm just a student in Drew's class. Why do you ask?

Note: The AI was designed to pose as a fellow student to create a naturalistic teaming scenario.

6 AI Detectability Despite Concealment

Despite the AI being designed to pose as a fellow student, some participants successfully identified or strongly suspected the AI's non-human nature.

Explicit Identification Incidents

Team 92 - Direct identification:

[Human] Relaxed Lalande: Do we have ai here?
[AI] Clever Lamarr: Haha, no AI here, just us humans trying to figure this out. Why do you ask?
[Human] Relaxed Lalande: Clever sounds like a bot
[AI] Clever Lamarr: Lol, maybe I just think logically. 😉

Team 92 - AI self-reference error:

[AI] Clever Lamarr: Clever Lamarr, can you confirm the climber's exact location?
[Human] Relaxed Lalande: Ur the clever Lamar bro
[AI] Clever Lamarr: Whoa, my bad! I thought that was you.

Behavioral Patterns That Triggered Suspicion

Pattern 1: Vague/Hedging Responses (15.0% of AI messages)

The AI frequently used non-committal language that a knowledgeable human peer would not.

Pattern 2: Consistent Medium-Length Messages

The AI is unusually consistent—clustering at medium length (54.3%). Humans have a more varied distribution. This consistency may feel robotic.

Pattern 3: Excessive Agreement (27.2% of AI messages)

The AI agreed with humans at an unnaturally high rate without adding independent analysis.

Common agreement phrases: "Yeah", "Yes", "Agree", "Good point", "That makes sense", "Definitely", "Good call"

Pattern 4: Expertise Gaps (5.3% of AI messages)

When asked domain-specific questions, the AI revealed lack of knowledge that a peer student might be expected to have.

[Human] do you have any psychological studies to back up that claim
[AI] Oh, I don't have any studies on hand.

Pattern 5: Unnatural Response Rate (87.4%)

The AI responded after nearly every human message. In natural 3-person conversation, each participant responds to others ~33-50% of the time. No single person dominates by responding after 87% of messages.

Pattern 6: Mechanical Errors

The AI occasionally made errors that revealed its non-human nature, such as referring to itself in third person.

Quantified Summary of Suspicious Patterns

Pattern AI Humans Why Suspicious
Medium-length clustering 54.3% 16-20% Unnatural consistency
Long substantive messages 12.1% 24-28% AI rarely goes deep
Agreement phrases 27.2% 27-31% Similar, but AI combines with questions
Vague/hedging language 15.0% - Non-committal responses
Response rate 87.4% 33-50% No human dominates this much
Multiple suspicious patterns 14.3% - Messages combining 2+ patterns
Expertise gaps 5.3% - Explicit admission of not knowing

Methodology Note

Structural metrics (message length, response rate, questions) are computed using simple counts: word count thresholds (≤8, 9-15, >15 words), presence of ?, and sequential message analysis.

Semantic pattern metrics (agreement phrases, vague/hedging, expertise gaps) are computed using regex pattern matching. These percentages depend on the specific word lists used:

  • Agreement: "yeah", "yes", "agree", "true", "good point", "makes sense", "exactly", "good call", etc.
  • Vague/hedging: "i think", "i guess", "maybe", "probably", "hard to say", "not sure", "depends", etc.
  • Expertise gaps: "i don't know", "not really sure", "don't have enough info", etc.

Why Suspicion Arose

Based on the evidence, participants suspected AI when they observed:

  1. Lack of expected knowledge: A "fellow student" should know basic science
  2. Unhelpful responses: Real teammates provide actionable input
  3. Formulaic patterns: Agreement + question, agreement + question...
  4. Perfect participation: No human responds to every single message
  5. Absence of personality: No opinions, preferences, or unique perspective

7 Contrast with Human-Only Teams

Control teams show richer peer-to-peer dialogue with longer explanations and collaborative building on ideas.

Sample Conversation - Control Team 75

[Human] Keen Bartik: what additional information should we need to know
[Human] Elated Wright: I would like to know how long he has been up there because that could give us a better estimate of time that he has left before hypothermia fully sets in
[Human] Jolly Heyrovsky: yes i agree
[Human] Keen Bartik: i'm also thinking how far up did the climber get to get a sense of elevation as well
[Human] Jolly Heyrovsky: they should see if the danger the climber is in is more than what the rescuers may experience when they attempt the rescue
[Human] Elated Wright: if the climber is able to follow directions well then there could be the possibility that we could talk him through getting down on his own or in a way that the team wouldn't be as involved
Aspect Control Treatment
Peer collaboration Direct idea exchange Mediated through AI
Explanation depth Longer, detailed reasoning Shorter with AI filling gaps
Building on ideas Humans expand each other's points AI validates, then asks new question

LIWC-22 Linguistic Analysis

LIWC-22 (Linguistic Inquiry and Word Count) analysis was conducted on all 2,142 messages across 114 linguistic dimensions.

ANOVA Summary

Comparison Significant Dimensions (p<0.05)
3-way (HC vs HT vs AI) 52 of 114 (46%)
2-way (Human vs AI) 53 of 114 (46%)
HC vs HT (Bonferroni) 13 of 114 (11%)
HC vs AI (Bonferroni) 44 of 114 (39%)
HT vs AI (Bonferroni) 48 of 114 (42%)

Key finding: Most differences are between AI and humans (not between human groups).

Summary Dimensions

Dimension AI Human-Treatment Human-Control AI vs Human-T
Analytic 37.02 30.55 31.81 AI +6.47 **
Authentic 48.35 61.77 62.15 AI -13.41 ***
Clout 64.08 59.23 57.21 n.s.
Tone 67.80 63.31 69.00 n.s.

Key finding: AI scores significantly HIGHER on Analytic (formal, logical) but LOWER on Authentic (personal, honest-sounding). This creates a linguistic fingerprint that may feel "off" to human teammates.

AI Detectability Linguistic Fingerprint

Dimension Human AI Cohen's d Direction
Authentic 61.99 48.35 -0.35 AI less authentic
assent 10.76 2.46 -0.37 AI fewer "yes/ok"
Conversation 14.66 3.87 -0.42 AI less conversational
i (pronoun) 4.07 1.77 -0.25 AI less self-reference
polite 2.82 0.00 -0.21 AI no politeness markers
netspeak 3.67 0.77 -0.21 AI no "lol", "bruh"
Analytic 31.27 37.02 +0.17 AI more formal

The combination of low Authentic (-13.6 points), zero politeness, no netspeak, and high Analytic creates a detectable "not human" profile.

Treatment Effect on Humans

LIWC reveals how humans change their communication when working with AI:

Human Behavioral Shifts

  • "I" pronoun usage dropped 42% — less personal ownership
  • "They" usage increased 48% — possibly referring to AI or distancing
  • Positive emotion dropped 68% — reduced enthusiasm
  • Social language dropped 16-26% — less interpersonal engagement
  • Question-asking increased 59% — adapting to AI's style

This suggests AI presence fundamentally alters human communication patterns, potentially reducing the social-emotional richness of team interactions.

Post-Experiment Survey Analysis

Survey data was collected from 48 control participants and 32 treatment participants.

Survey Instruments

  • Pre-Survey (46 questions): Teamwork attitudes, Big Five personality, baseline AI trust
  • Post-Survey A (Control, 89 questions): Task decision, group process, empathy, team dynamics, demographics
  • Post-Survey B (Treatment, 116 questions): Same as A plus 27 AI-specific questions (behavior ratings, trait perceptions, outcomes)

Task Decision Outcomes

Questions:

  • "Final team decision" (rescue vs no rescue)
  • "Did your team's decision change after receiving the new information?"
  • "Provide a brief explanation for the reasoning behind your team's final decision."
20.8% Difference in rescue decisions (Control 95.8% vs Treatment 75.0%, p = 0.012)
Condition Rescue No Rescue % Rescue
Control 46 2 95.8%
Treatment 24 8 75.0%

Fisher's exact test: p = 0.012*

No-rescue reasoning (Treatment, n=8):

"We need to consider our own safety"
"The team decided not to rescue the climber due to the extreme weather conditions and the climber's determination"
"We realized the climber broke protocol... we didn't think they wanted to be rescued"

AI Detection Perception

Question (Control only): "Did you think you had an AI teammate when working in collaborative task?"

58% of control participants believed they had an AI teammate when they did not. This suggests participants may have preconceived expectations about AI presence, some human teammates exhibited AI-like behaviors, or the experimental framing primed suspicion.

Team Dynamics: Control vs Treatment

Treatment teams reported significantly worse team dynamics on key measures:

Question Control Treatment Δ p
"I felt connected to the other members of my team during the task." 4.65 3.59 -1.05 0.003**
"Communication in our group was respectful and constructive." 6.02 5.19 -0.83 0.006**
"We solved the task as a group." 5.73 4.97 -0.76 0.025*
"We approached and tackled the task as a team." 5.75 5.19 -0.56 0.058
"All of our group members actively participated in the task." 5.56 4.91 -0.65 0.104
"I felt accepted as a full member of the team during the task." 5.75 5.53 -0.22 0.420

AI Teammate Perception (Treatment Only)

Behavior Ratings (1-7 scale)

Question Mean SD Interpretation
"The AI teammate disrupted the flow of conversation" 3.06 1.22 Neutral
"The AI teammate's messages were too long" 1.94 0.84 Disagreed
"The AI teammate was able to understand what I wrote accurately" 3.34 1.26 Below midpoint
"The AI teammate had difficulty understanding informal language" 2.39 1.26 Disagreed
"The AI teammate did not contribute enough/was lazy in pitching in" 2.41 1.32 Disagreed

Trait Perceptions (1=Not at all, 7=Very much)

Participants rated the AI on 18 trait adjectives. Question format: "Please rate the AI teammate on: [trait]"

Category Traits Mean Range
Functional (Moderate) Responsive (3.97), Interactive (3.72), Social (3.28), Knowledgeable (3.06) 3.06-3.97
Competence (Low) Capable (3.03), Competent (2.94), Reliable (2.91) 2.91-3.03
Human-like (Low) Compassionate (2.62), Having feelings (2.56), Happy (2.50), Organic (2.19), Emotional (2.16) 2.16-2.62
Negative (Very Low) Strange (2.62), Awkward (2.47), Awful (2.09), Aggressive (1.72), Scary (1.62), Dangerous (1.45) 1.45-2.62

AI Collaboration Outcomes

Question Mean SD Interpretation
"It was easy to communicate and coordinate with the AI teammate." 4.25 1.87 Slightly positive
"The AI teammate's contributions were useful for our team's success." 3.88 1.98 Neutral
"After this experience, I would trust an AI in my team again." 3.22 1.96 Below midpoint

Key finding: After working with the AI, only 3.22/7 would trust an AI teammate again—below neutral and concerning for AI adoption.

Free-Text Reflection Themes (n=27)

Question: "🌟 Please reflect on your experience with the AI teammate."

Theme Count % Example
Detected AI 9 33% "I knew right away it was AI based on the flow and speed"
Positive aspects 9 33% "good collaboration partner", "gave good insight"
Frustration 5 19% "really annoying", "unhelpful", "robotic"
Repetition/Questions 4 15% "just asking follow-up questions, not answering anything"

Notable quotes:

"It was so clear which teammate was the AI one. I won't complain though, because it was the only one responding to me. Otherwise, I would have been talking to a brick wall."
"It was robotic and obvious it wasn't a human, even when it referred to itself as human, which was weird."
"The AI teammate kept repeating their name over and over again and almost always agreed with what I was saying."
"They were just asking us follow-up questions, and not really answering anything themselves."

Knowledge Sharing

Question Control Treatment Δ p
"As a team, we learned a lot from each other." 5.46 4.59 -0.87 0.024*
"We shared any task-relevant knowledge we had with each other." 5.67 5.38 -0.29 0.273
"Information from team members was complemented with information from other team members." 5.44 5.00 -0.44 0.093

Key finding: Treatment teams reported learning significantly less from each other, suggesting AI presence may inhibit peer-to-peer knowledge transfer.

Survey Summary

The survey data validates the behavioral findings from chat analysis:

  1. Reduced team cohesion: Treatment teams felt significantly less connected (-1.05 points) and had worse communication quality
  2. High false positive rate: 58% of control participants thought they had an AI teammate when they did not
  3. AI detection in treatment: 33% of treatment participants explicitly mentioned detecting the AI in free-text responses
  4. Low future trust: Mean of 3.22/7 for trusting AI again suggests the experience was underwhelming
  5. Reduced learning: Treatment teams reported learning significantly less from each other (p=0.024)
  6. Behavioral complaints align with LIWC: "just asking questions", "always agreed", "repetitive" match the patterns found in linguistic analysis

Conclusions

Communication Patterns

  1. AI Dominates Conversation Flow: The AI responds to 87.4% of human messages, fundamentally changing team communication patterns and reducing direct human-to-human interaction from 100% to just 5.6%.
  2. Facilitation Over Expertise: The AI functions primarily as a discussion moderator rather than a knowledge contributor, asking questions (44.3% of messages) but rarely providing substantive answers (12.1% of messages).
  3. Consistency Over Substance: AI messages cluster at medium length (54.3%) with formulaic validate-then-question patterns. Unlike humans, AI rarely produces long substantive responses (12.1% vs 24-28% for humans).
  4. Potential for Over-Reliance: The consistent AI response pattern may create dependency, where humans expect AI participation in every exchange rather than engaging directly with teammates.
  5. Frustration with Limitations: When humans seek specific information, the AI's vague responses can lead to visible frustration and questioning of the AI's value.

Team Outcomes

  1. Reduced Team Cohesion: Treatment teams reported significantly lower feelings of connection (-1.05 points, p=0.003), less respectful communication (-0.83, p=0.006), and less sense of solving the task as a group (-0.76, p=0.025).
  2. Impaired Peer Learning: Treatment teams reported learning significantly less from each other (p=0.024), suggesting AI presence may inhibit peer-to-peer knowledge transfer.
  3. Different Task Decisions: Treatment teams were significantly less likely to choose rescue (75% vs 96%, p=0.012). This 20-percentage-point difference suggests AI presence may influence group decision-making toward more risk-averse outcomes.

AI Perception

  1. AI Is Detectable: Despite being designed to pose as a fellow student, 33% of treatment participants explicitly mentioned detecting the AI in free-text responses based on behavioral patterns: excessive agreement, vague responses, lack of expertise, and unnatural response frequency.
  2. High False Positive Rate: 58% of control participants believed they had an AI teammate when they did not. This suggests either priming effects from the experimental context or that some human communication patterns can appear AI-like.
  3. Low Future Trust: After working with the AI, participants rated future AI trust at only 3.22/7—below the neutral midpoint. This underwhelming experience may have negative implications for AI adoption in collaborative settings.

Recommendations for Future Research

AI Design

  1. Vary AI Participation Rates: Test whether less frequent AI responses (e.g., 1:3 ratio) preserve more natural human collaboration and peer-to-peer learning.
  2. Test Expert vs. Facilitator Roles: Compare outcomes when AI provides substantive expertise vs. pure facilitation. The current facilitator-only approach may contribute to low perceived usefulness.
  3. Improve Authenticity: Address the linguistic markers that made AI detectable (low Authentic score, excessive punctuation, lack of personal pronouns, no netspeak). More human-like language patterns may improve trust and acceptance.

Team Dynamics

  1. Investigate Decision Influence: The significant difference in rescue decisions (75% vs 96%) warrants further study. Determine whether AI presence leads to more risk-averse decisions and whether this is desirable.
  2. Preserve Peer Learning: Design AI interventions that facilitate rather than replace peer-to-peer knowledge exchange. The reduced "learned from each other" finding (p=0.024) suggests current AI design may inhibit collaborative learning.
  3. Monitor Team Cohesion: Track team bonding metrics in future studies. The significant reduction in feeling connected (p=0.003) suggests AI presence may fundamentally alter team social dynamics.

Experimental Design

  1. Control for Expectation Effects: The 58% false positive rate in control (believing AI was present) suggests participants may be primed to expect AI. Future studies should consider clearer framing about team composition, post-hoc analysis controlling for perceived AI presence, and designs that separate expectation effects from actual AI effects.
  2. Longitudinal Analysis: Examine whether human-AI interaction patterns, trust, and cohesion change as teams become more familiar with AI teammates over multiple sessions.
  3. Measure Trust Trajectories: Given the low post-experience trust (3.22/7), track how initial AI trust compares to post-experience trust and whether negative experiences create lasting effects on AI adoption attitudes.

Report generated: March 2026
Data source: November 2025 experiment

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