Academic Research
Access peer-reviewed academic papers and research reports on AI personality assessment and psychological evaluation methodologies.
Research Categories
AI Personality Assessment
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"Evaluating the Validity of AI Personality Assessments Using the Big Five Model"
This paper examines the validity of AI-based personality assessments compared to traditional human-based evaluations using the Big Five model.
Smith, J., Chen, L., & Rodriguez, M. (2024). Evaluating the Validity of AI Personality Assessments Using the Big Five Model. Journal of Artificial Intelligence Research. -
"Cross-Model Consistency in AI Personality Assessment: A Comparative Study"
Investigates personality trait consistency across different AI models and architectures, identifying key factors affecting reliability.
Johnson, A., Williams, K., & Brown, S. (2024). Cross-Model Consistency in AI Personality Assessment: A Comparative Study. AI Ethics and Assessment Quarterly.
Cognitive Stability and Consistency
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"Measuring Cognitive Stability in Large Language Models Over Time"
Introduces novel metrics for evaluating cognitive stability in LLMs, demonstrating how consistency changes with system updates and retraining.
Lee, H., Thompson, R., & Garcia, P. (2024). Measuring Cognitive Stability in Large Language Models Over Time. Cognitive AI Research. -
"Logical Coherence and Reasoning Stability in Conversational AI Systems"
Analyzes the relationship between logical coherence and reasoning consistency in conversational AI systems over extended interactions.
Davis, M., Wilson, T., & Kim, Y. (2023). Logical Coherence and Reasoning Stability in Conversational AI Systems. Journal of AI Cognition.
Cognitive Traps and Reasoning Errors
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"Susceptibility of Large Language Models to Cognitive Traps and Reasoning Biases"
Systematic evaluation of common cognitive traps and reasoning errors in LLMs, with proposed mitigation strategies.
Patel, S., Miller, D., & O'Connor, K. (2024). Susceptibility of Large Language Models to Cognitive Traps and Reasoning Biases. AI Safety and Reliability. -
"Identifying and Measuring Cognitive Trap Susceptibility in AI Systems"
Presents a framework for identifying and quantifying cognitive trap susceptibility in various AI architectures.
Zhao, X., Anderson, B., & Taylor, R. (2023). Identifying and Measuring Cognitive Trap Susceptibility in AI Systems. International Conference on AI Safety.
Ethical and Social Implications
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"Ethical Considerations in AI Personality Assessment: Privacy, Consent, and Fairness"
Comprehensive analysis of ethical challenges in AI personality assessment, including privacy concerns and potential for discrimination.
Roberts, C., Martinez, A., & Liu, F. (2024). Ethical Considerations in AI Personality Assessment: Privacy, Consent, and Fairness. AI Ethics Journal.
Scientific Methodology and Validation
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"Statistical Significance in AI Personality Assessment: The Need for Thousands of Test Iterations"
Analysis demonstrating why traditional assessment methods are insufficient for AI personality evaluation and how parameter variations affect results.
Zhang, S., Kumar, R., & Johnson, M. (2024). Statistical Significance in AI Personality Assessment: The Need for Thousands of Test Iterations. Journal of AI Psychology. -
"Parameter Sensitivity and Personality Stability in Large Language Models"
Empirical study on how different temperature, top-p, and context parameters affect personality expression in LLMs.
Anderson, P., Lee, H., & Wilson, T. (2024). Parameter Sensitivity and Personality Stability in Large Language Models. Cognitive AI Research.
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