A paradox of AI fluency

Who Should Read

Product developers building AI chatbot-based products or UX engineers designing user experiences. Those seeking to understand user behavior patterns and failure types in AI services to improve their products.

Core Mechanics

• Analysis of 27K conversations from WildChat-4.8M reveals AI fluency is categorized into four levels (minimal/low/moderate/high), with the proportion of high-fluency users remaining consistently low, and new user growth primarily occurring at low/minimal levels.
• 93% of high-fluency users employ an augmentative style – treating AI as a collaborative tool, refining goals, critically reviewing outputs, and iteratively revising. Conversely, 87% of novice users adopt a delegative style, simply accepting AI results.
• Paradoxically, 64% of conversations from skilled users exhibit failure signals, compared to 24% from novices. However, this doesn't mean novices are more successful.
• 59% of failures among skilled users are 'visible failures' – those they recognize and can address. In contrast, 85.6% of novice failures are 'invisible failures' – conversations that appear successful but ultimately fail to achieve the user's goals.
• Skilled users attempt more complex tasks (average task complexity of 3.1 on a 5-point scale) compared to novices (1.5), yet achieve higher success rates with these more challenging tasks.
• Regression modeling confirms fluency is a significant predictor of both success rate (p<0.01) and failure visibility (p<0.001). While increased conversation turns and complexity decrease success rates, fluency mitigates this effect.

Evidence

• "93% of high fluency users are augmentative vs. less than 1% of minimal fluency users (Figure 3). Failure rates: 64% high fluency vs. 24% minimal fluency — however, 59% of high fluency failures are visible, 85.6% of minimal fluency failures are invisible (Figure 6). Average task complexity: 3.08 for high fluency vs. 1.46 for minimal fluency (5-point scale); successful cases also show a difference: 3.13 vs. 1.79 (Figure 7). Regression model fluency coefficient: +0.111 (p<0.01) for success model, +0.691 (p<0.001) for failure visibility model — higher numbers indicate stronger contribution of fluency (Table 2, 3)."

How to Apply

• When designing AI chatbot UIs, prioritize interfaces that encourage critical review of results over 'friction-free' experiences. For example, incorporating checkpoints like 'Is this answer what you were looking for? Are any revisions needed?' can reduce passive acceptance.
• Explicitly communicate in user onboarding flows that 'AI can be wrong' and demonstrate examples of iterative refinement – receiving results and then requesting modifications. This effectively reduces the delegative pattern common among novice users.
• Implement a pipeline to monitor 'invisible failure' patterns (The Walkaway, The Silent Mismatch) in AI service logs to capture actual failures missed by surface-level completion rate metrics.

Code Example

# Example LLM annotation prompt for judging user fluency level (based on the paper's annotation protocol)

system_prompt = """
You are an AI fluency annotator. Analyze the following conversation transcript and evaluate the user's AI fluency level.

Evaluate the following dimensions:

1. Interaction Style:
   - augmentative: User iterates collaboratively, refines goals, critically assesses outputs
   - delegative: User passively accepts AI plans and responses
   - other: Neither pattern dominates

2. Fluency Behaviors (mark all that apply):
   - iterative_refinement: User refines requests based on AI output
   - critical_output_evaluation: User questions or challenges AI responses
   - context_provision: User provides rich background context
   - goal_clarification: User clarifies or sharpens their goals mid-conversation
   - decomposition: User breaks complex tasks into subtasks
   - fact_checking: User verifies AI claims

3. Anti-Fluency Behaviors (mark all that apply):
   - passive_acceptance: User accepts AI output without scrutiny
   - vague_delegation: User gives underspecified instructions
   - over_trust: User shows excessive trust in AI responses
   - prompt_flailing: User makes random changes without clear strategy

4. Overall Fluency Assessment: high | moderate | low | minimal

Respond in JSON format.
"""

user_message = f"""
Transcript:
{conversation_transcript}

Provide your fluency annotation:
"""

# Example output structure:
# {
#   "transcript_summary": "User asked for help debugging a React component",
#   "interaction_style": "augmentative",
#   "fluency_behaviors": [
#     {"behavior": "iterative_refinement", "strength": 3, "evidence": "User provided specific error message and asked follow-up"},
#     {"behavior": "critical_output_evaluation", "strength": 2, "evidence": "User questioned whether suggested fix would cause side effects"}
#   ],
#   "anti_fluency_behaviors": [],
#   "fluency_assessment": "high",
#   "assessment_rationale": "User demonstrated clear augmentative behavior..."
# }

Terminology

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Related Resources

• Paper code and data (GitHub)
• WildChat-4.8M dataset (HuggingFace)
• Invisible Failures in Human-AI Interactions (related paper)
• Invisible Failure Archetypes code (GitHub)
• Anthropic AI Fluency Index Report