An agentic architecture with multimodal screening, a five-agent jury, meta-synthesis, and source attribution protocol detects biases in Romanian history textbooks more accurately than zero-shot baselines, achieving 83.3% acceptable excerpts and human preference in 64.8% of blind comparisons.
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PromptDecipher introduces a correction-based authoring workflow that turns live interaction and response editing into the primary way teachers build and validate AI tutoring chatbots.
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An Agentic Evaluation Architecture for Historical Bias Detection in Educational Textbooks
An agentic architecture with multimodal screening, a five-agent jury, meta-synthesis, and source attribution protocol detects biases in Romanian history textbooks more accurately than zero-shot baselines, achieving 83.3% acceptable excerpts and human preference in 64.8% of blind comparisons.
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PromptDecipher: Supporting AI Tutor Authoring Through Editable Simulated Interactions
PromptDecipher introduces a correction-based authoring workflow that turns live interaction and response editing into the primary way teachers build and validate AI tutoring chatbots.