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AI in the Assessment Ecosystem: A Human–Centered AI Perspective
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AI in the Assessment Ecosystem: A Human–Centered AI Perspective (2024)AI in the Assessment Ecosystem: A Human–C…Six Human-Centered Artificial Intelligence Grand Challenges (2023)Six Human-Centered Artificial…Artificial Intelligence Risk Management Framework (AI RMF 1.0) (2023)Artificial Intelligence Risk …A Unified Framework of Five Principles for AI in Society (2022)A Unified Framework of Five P…Ethics-Based Auditing to Develop Trustworthy AI (2021)Ethics-Based Auditing to Deve…Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-Based Approaches to Principles for AI (2020)Principled Artificial Intelli…Human-Centered Artificial Intelligence: Three Fresh Ideas (2020)Human-Centered Artificial Int…Closing the AI accountability gap (2020)Closing the AI accountability…The global landscape of AI ethics guidelines (2019)The global landscape of AI et…Actionable Auditing (2019)Actionable AuditingSociocognitive Foundations of Educational Measurement (2018)Sociocognitive Foundations of…Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (2018)Gender Shades: Intersectional…Validating the Interpretations and Uses of Test Scores (2013)Validating the Interpretation…A Framework for Evaluation and Use of Automated Scoring (2012)A Framework for Evaluation an…Building a Validity Argument for the Test of English as a Foreign Language™ (2011)Building a Validity Argument …Language Testing and Validation (2005)Language Testing and Validati…An argument-based approach to validity. (1992)An argument-based approach to…Construct validity in psychological tests. (1955)Reviews: Lord, Frederic M., and Novick, Melvin R. (with contributions by Allan Birnbaum). Statistical Theories of Mental Test Scores. Reading, Mass.: Addison-Wesley, 1968. xii + 568 pp. $14.95. (1969)Statistical Theories of Mental Test Scores (1969)Beyond Dichotomies (2015)TWO CRITERIA FOR GOOD MEASUREMENTS IN RESEARCH: VALIDITY AND RELIABILITY (2017)The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education (2018)The New Rhetoric: A Treatise on Argumentation (1971)Validity: on the meaningful interpretation of assessment data (2003)Current Concepts in Validity and Reliability for Psychometric Instruments: Theory and Application (2006)Applications of Item Response Theory To Practical Testing Problems (2012)Qualitative research and evaluation methods (1980)Research Methods in Education (2007)THE PSYCHOLOGY OF PERSONAL CONSTRUCTS (2010)Rethinking the role of automated writing evaluation (AWE) feedback in ESL writing instruction (2014)The Advantages and Disadvantages of Using Qualitative and Quantitative Approaches and Methods in Language “Testing and Assessment” Research: A Literature Review (2016)Task-Based Language Teaching (2019)Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement (2009)Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement (2009)Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement. (2009)Principles alone cannot guarantee ethical AI (2019)The Ethics of AI Ethics: An Evaluation of Guidelines (2020)Ethics of AI in Education: Towards a Community-Wide Framework (2021)Accountability in algorithmic decision making (2016)How the machine ‘thinks’: Understanding opacity in machine learning algorithms (2016)Demarginalizing the Intersection of Race and Sex: A Black Feminist Critique of Antidiscrimination Doctrine, Feminist Theory, and Antiracist Politics [1989] (2018)On the Opportunities and Risks of Foundation Models (2021)PRODUCT DEVELOPMENT: PAST RESEARCH, PRESENT FINDINGS, AND FUTURE DIRECTIONS (1995)Taxonomy of Risks posed by Language Models (2022)Trustworthy Artificial Intelligence: A Review (2022)The weirdest people in the world? (2010)Self-Determination Theory: Basic Psychological Needs in Motivation, Development, and Wellness (2017)A Survey on Bias and Fairness in Machine Learning (2021)ChatGPT: More Than a “Weapon of Mass Deception” Ethical Challenges and Responses from the Human-Centered Artificial Intelligence (HCAI) Perspective (2023)The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT (2023)Understanding Human-Centred AI: a review of its defining elements and a research agenda (2025)Model Cards for Model Reporting (2019)What is AI Literacy? Competencies and Design Considerations (2020)Bridging the Gap Between Ethics and Practice (2020)Education for AI, not AI for Education: The Role of Education and Ethics in National AI Policy Strategies (2021)Responsible artificial intelligence governance: A review and research framework (2025)AI-powered marketing: What, where, and how? (2024)Trust in AI: progress, challenges, and future directions (2024)AI Ethics: Integrating Transparency, Fairness, and Privacy in AI Development (2025)
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References

  • 2019The global landscape of AI ethics guidelines7,131 cited
  • 2018Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification3,544 cited
  • 2023Artificial Intelligence Risk Management Framework (AI RMF 1.0)2,375 cited
  • 2013Validating the Interpretations and Uses of Test Scores2,273 cited
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  • 2011Building a Validity Argument for the Test of English as a Foreign Language™305 cited
  • 2021Ethics-Based Auditing to Develop Trustworthy AI267 cited
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Sociocognitive Foundations of Educational Measurement
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