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This research aims to measure the impact of accumulated operational experience on the performance of AI assistants in specific tasks.

April 14, 2026Open Access

Operational Experience as a Performance Multiplier in AI Assistants: A Controlled Study with Triple-Judge Blind Evaluation

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Controlled experiment shows that accumulated experience improves AI assistant performance on specific tasks, suggesting new improvement strategies.

This research aims to measure the impact of accumulated operational experience on the performance of AI assistants in specific tasks.
Conducted a controlled experiment using a 2×2 factorial design with 8 conditions.
Utilized 50 real-world questions from public sources for evaluation.
Received 1,200 blind judgments from three independent AI judges (GPT-5.4, Gemini 3.1 Pro, Claude Opus 4.6).
Experience-augmented AI assistant significantly outperformed baseline models (Cohen's d = 1.07, p < 10⁻²⁵).
Performance increase was domain-specific with a +1.65 standard deviation on operational tasks.
Independent classification revealed 2.2× more genuinely experiential content in the experience-augmented model.

Ravshan et al. (2026) studied this question.

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April 14, 2026Open Access

View Full Paper

Ask AI

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Authors

Controlled experiment shows that accumulated experience improves AI assistant performance on specific tasks, suggesting new improvement strategies.

This research aims to measure the impact of accumulated operational experience on the performance of AI assistants in specific tasks.
Conducted a controlled experiment using a 2×2 factorial design with 8 conditions.
Utilized 50 real-world questions from public sources for evaluation.
Received 1,200 blind judgments from three independent AI judges (GPT-5.4, Gemini 3.1 Pro, Claude Opus 4.6).
Experience-augmented AI assistant significantly outperformed baseline models (Cohen's d = 1.07, p < 10⁻²⁵).
Performance increase was domain-specific with a +1.65 standard deviation on operational tasks.
Independent classification revealed 2.2× more genuinely experiential content in the experience-augmented model.

Ravshan et al. (2026) studied this question.

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