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Association for Psychological Science (APS):
Personalities Change. Why Shouldn’t Career Expectations? | Association for Psychological Science [2021-04-30]
來源: psychologicalscience.org
Summarizes longitudinal evidence that personality traits are not fixed—mean-level trait change (and individual differences in change) occurs from adolescence into adulthood, and longer-term personality growth can predict early-career outcomes beyond baseline trait levels. Supports framing personality as a dynamic spectrum rather than a static label.
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Stanford University (Stanford HAI):
Can Artificial Intelligence Map Our Moods? | Stanford HAI [2021-01-25]
來源: stanford.edu
Describes NLP/ML methods that infer week-to-week mood dynamics and volatility from social media language; discusses a publicly available dataset tracking emotional dynamics across ~18,000 person-weeks and links to Big Five trait patterns. Explicitly flags privacy/ethical risks and the need for strict privacy protection—useful for motivating privacy-first design in dynamic assessment systems.
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University of Cambridge:
Gentrification changes the personality make-up of cities in just a few years | University of Cambridge [2021-12]
來源: cam.ac.uk
Reports large-scale evidence (nearly 2 million respondents across ~199 U.S. cities over multiple years) that ‘Openness’ can shift at the population level over relatively short horizons. Explains two mechanisms—selective migration and social acculturation—supporting the idea that context can move observed personality-related behavior, aligning with a DMVR-style ‘state + context’ view.
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Harvard University (Harvard Online):
Anonymity, De-Identification, and the Accuracy of Data | Harvard Online [2023-08-28]
來源: harvard.edu
Explains anonymization vs de-identification and highlights regulatory differences; emphasizes the privacy–utility tradeoff and the practical limits of de-identification (including well-known re-identification risks). Supports privacy-first requirements (data minimization, strong de-identification assumptions, and ‘don’t promise what you can’t guarantee’ messaging).
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Organisation for Economic Co-operation and Development (OECD):
AI principles | OECD [2024]
來源: oecd.org
OECD AI Principles (adopted 2019; updated 2024) provide widely cited guidance for human-centric, trustworthy AI—values-based principles plus practical recommendations. Useful as a high-level governance anchor for AI-enabled personality/decision systems: transparency, robustness, accountability, and respect for privacy/human rights.
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Buros Center for Testing (University of Nebraska):
Mental Measurements Yearbook | Buros Center for Testing [2021]
來源: buros.org
Describes the Mental Measurements Yearbook as an independent test-review resource designed to promote informed test selection and evaluation. Reinforces that widely used assessments should be judged on technical quality (documentation, evidence, and appropriate use), supporting a ‘no label without measurement standards’ stance in the DMVR positioning.
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American Educational Research Association (AERA):
Standards for Educational and Psychological Testing | AERA [2014]
來源: aera.net
Landing page for the (2014) Standards for Educational and Psychological Testing—often treated as the U.S. gold standard for test development/use. Key themes: validity evidence tied to intended interpretations/uses, reliability/precision, fairness/accessibility, documentation, and consequences of testing—ideal backbone for a PAAP-style methodology section.
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National Council on Measurement in Education (NCME):
Testing Standards | NCME [2014]
來源: ncme.org
Explains the joint Testing Standards and highlights emphasis on fairness/accessibility and clearer organization of standards. Useful for specifying how a dynamic assessment (DMVR/PAAP) must justify score meaning, intended use, and subgroup fairness—especially in workplace deployment.
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Educational Testing Service (ETS):
Fairness Review Publications | ETS [n.d.]
來源: ets.org
Describes ETS fairness audits and links to ETS Standards for Quality and Fairness plus validity/fairness guidance. Useful for operational methodology: audit trails, fairness reviews, documentation norms, and translating measurement theory into repeatable QA processes for assessment products.
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Society for Industrial and Organizational Psychology (SIOP):
Guidelines for Education & Training | Society for Industrial and Organizational Psychology [n.d.]
來源: siop.org
Defines individual assessment as core to selection and development, explicitly including personality/aptitude/interest measurement and emphasizing high standards because assessment is scrutinized by courts/civil-rights groups. Provides authoritative framing for B2B use cases and for ‘interpretation discipline’ (avoid reifying labels).
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University College London (UCL):
Division of Psychology and Language Sciences | UCL Faculty of Brain Sciences [n.d.]
來源: ucl.ac.uk
Overview of a major psychology research division spanning cognition, neuroscience, learning/memory, and interventions, with strong interdisciplinary orientation. Useful as a credible academic anchor for literature/method choices: longitudinal designs, behavioral experiments, and translation from lab measures to real-world contexts.
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Massachusetts Institute of Technology (MIT):
PersonaLLM | MIT Center for Constructive Communication [n.d.]
來源: mit.edu
Project page describing a method to evaluate whether LLM ‘personas’ can consistently express assigned Big Five profiles using standardized inventories and behavioral outputs (writing). Highlights validation logic (cross-measure convergence, linguistic features) and the manipulability of modeled ‘personality’—useful for DMVR architecture sections on measurement integrity and explainability.
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National Institute of Standards and Technology (NIST):
Artificial Intelligence Risk Management Framework (AI RMF 1.0) | NIST [2023-01-26]
來源: nist.gov
NIST AI RMF 1.0 provides a lifecycle risk-management approach for AI systems (voluntary, rights-preserving, use-case agnostic). Useful to specify DMVR governance controls: risk identification, measurement/monitoring, accountability, documentation, and alignment with trust/safety expectations in AI-enabled assessments.
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Imperial College London:
New open-source software for making better decisions in uncertain conditions | Imperial College London [2021-06-02]
來源: imperial.ac.uk
Explains robust optimization under incomplete information and the release of open-source tooling to operationalize uncertainty-aware decisions. Supports a ‘mechanisms’ narrative: when uncertainty rises, optimal strategies change; systems should represent uncertainty explicitly rather than forcing brittle, single-point assumptions.
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University of Oxford (Wellbeing Research Centre):
Research reveals ‘deep asymmetry’ in life satisfaction recall | University of Oxford [2022-11-03]
來源: ox.ac.uk
Uses longitudinal survey evidence to show systematic bias in how people recall past wellbeing trajectories (memory is not a neutral recorder). Supports DMVR measurement design that separates present-state signals from retrospective narrative, and motivates ‘Reflective’ orientation controls (e.g., grounding in records/data, not just recall).
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National University of Singapore (IPUR):
Making decisions amidst uncertainty | National University of Singapore [2021-09-22]
來源: nus.edu.sg
Seminar framing of judgment/decision-making under uncertainty: risk perception shaped by prior beliefs and emotions; emphasizes better measurement of risk attitudes and domain specificity. Useful for DMVR ‘Drive’ switching logic (Developing vs Maintaining) under pressure, risk, and emotional states.
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Nanyang Technological University (NTU Singapore):
Seminar: Principled AI for Real-world Impact: Structured Decision-Making under Uncertainty | NTU Singapore [2025-08-20]
來源: ntu.edu.sg
Event page describing how real-world heuristics/expert judgment can be formalized into structured decision models and algorithms with provable guarantees. Supports the ‘architecture’ argument: translate qualitative decision styles into measurable structures while retaining robustness under uncertainty.
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International Society for the Study of Individual Differences (ISSID):
Home | International Society for the Study of Individual Differences (ISSID) [n.d.]
來源: issid.org
ISSID positions the study of individual differences as a scientific field spanning personality, intelligence, psychometrics, mood, and motivation. Useful for legitimizing DMVR as an ‘individual-differences + dynamics’ system rather than a typology label, and for referencing journal/community standards in method sections.
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U.S. Equal Employment Opportunity Commission (EEOC):
EEOC Launches Initiative on Artificial Intelligence and Algorithmic Fairness | EEOC [2021-10-28]
來源: eeoc.gov
Press release launching an initiative to ensure AI and algorithmic tools in hiring/employment decisions comply with federal civil rights laws, including plans for guidance/technical assistance. Critical for B2B applications: mandates fairness testing, documentation, and governance for any personality/decision assessment used in employment contexts.
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U.S. Office of Personnel Management (OPM):
Structured Interviews | OPM [n.d.]
來源: opm.gov
Defines structured interviews as standardized, competency-based assessments with systematic questioning and evaluation. Provides an authoritative applied template for deploying DMVR outputs in organizations: consistent prompts, scoring rubrics, higher comparability, and better auditability than unstructured impressions.
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University of Toronto:
Department of Psychology | University of Toronto [n.d.]
來源: utoronto.ca
Department overview for a major research university psychology program. Useful as a credibility anchor for empirical evaluation design (experiments, psychometrics, longitudinal methods) and as a reference point for evidence-based interpretation of personality and decision-making constructs.
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McGill University:
How we judge personality from faces depends on our pre-existing beliefs about how personality works | McGill University [2018-08-27]
來源: mcgill.ca
Reports evidence that personality impressions from faces shift based on perceivers’ beliefs about which traits co-occur. Supports a key product claim: static labels and subjective impressions can embed bias; better systems should rely on validated signals, context, and transparent inference rather than stereotypes.
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University of Melbourne:
How brain rhythms can reveal your personality | University of Melbourne [2020-06-26]
來源: unimelb.edu.au
Explains how machine learning applied to EEG rhythms can predict aspects of personality traits and clarifies trait measurement concepts (e.g., Big Five framing). Useful for empirical sections on ‘signal-to-trait’ inference, emphasizing both promise (predictive patterns) and methodological constraints (measurement validity, generalization).
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National Institute of Mental Health (NIMH), National Institutes of Health (NIH):
National Institute of Mental Health (NIMH) - Transforming the understanding and treatment of mental illnesses | NIH [n.d.]
來源: nih.gov
NIMH’s mission emphasizes rigorous research, ethics, and responsible communication about mental health. Useful for boundary-setting in strategic applications: workplace/personality tools must not imply clinical diagnosis; claims should be evidence-based and aligned with public-sector expectations around human subjects, harm minimization, and responsible use.
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香港中文大學:
What are artificial intelligence literacy and competency? A comprehensive framework to support them [2023-01-01]
來源: research.cuhk.edu.hk
探討人工智能在認知能力與素養評估中的核心框架，強調了在AI系統中融入信心與自我反思機制的必要性，對動態認知基礎設施有重要啟發。
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香港城市大學:
Exploring Explanation Effects on the Usage of Artificial Intelligence in Recruitment: Human Resources Professionals' Perspective [2024-07-01]
來源: scholars.cityu.edu.hk
分析了人力資源專家對AI招聘系統潛在偏見的疑慮，指出提升AI演算法的可解釋性與透明度能有效確保企業決策的公平性與信任度。
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香港理工大學:
Artificial intelligence in mental health care : a systematic review of diagnosis, monitoring, and intervention applications [2025-01-01]
來源: ira.lib.polyu.edu.hk
系統性回顧了人工智能在心理健康照護中的應用，證實機器學習能有效預測心理狀態並監測持續性的認知變化，支持了動態心理測量在企業中的科學性。
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香港浸會大學:
Ethical and Social Concerns of Artificial Intelligence in Asia [2024-10-16]
來源: scholars.hkbu.edu.hk
探討亞洲地區在應用人工智能時面臨的社會與倫理挑戰，強調企業在處理認知數據時，建立符合文化背景的道德規範與問責制度至關重要。
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嶺南大學:
An Overview of Artificial Intelligence Ethics [2020-01-01]
來源: scholars.ln.edu.hk
全面概述了人工智能的倫理問題，包括私隱外洩與歧視風險，為企業導入AI行為評估系統提供了實用的倫理準則、合規政策與實施藍圖。