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DTSTART;TZID=America/New_York:20261005T000000
DTEND;TZID=America/New_York:20261007T235959
DTSTAMP:20260212T211856Z
CREATED:20260212T211850Z
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SUMMARY:2026 AIME-Con
DESCRIPTION:We are thrilled to announce the return of the NCME Artificial Intelligence in Measurement and Education Conference (AIME–Con)! Following last year’s sold-out success\, AIME-Con 2026 will be held in Pittsburgh at the Wyndham Grand Pittsburgh Downtown on October 5–7. \n\n\n\nLearn More
URL:https://ncme.org/event/2026-aime-con/
LOCATION:Wyndham Grand Pittsburgh Downtown\, 600 Commonwealth Pl\, Pittsburgh\, Pennsylvania\, 15222\, United States
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261009T103000
DTEND;TZID=America/New_York:20261009T113000
DTSTAMP:20260901T215022Z
CREATED:20260901T213624Z
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SUMMARY:Why Educational Measurement Matters in the Age of AI
DESCRIPTION:This is presented by the NCME Communications Committee \n\n\n\nThis public-facing NCME webinar brings together leaders in educational measurement\, assessment\, learning analytics\, and AI in education to discuss what our field can contribute in the age of modern AI. Panelists Andrew Ho from Harvard University\, Kristen DiCerbo from Khan Academy\, and Alyssa Wise from Vanderbilt University will explore why concepts such as validity\, fairness\, evidence\, interpretation\, and responsible use are essential for designing and evaluating AI-enabled educational systems. The session will be moderated by Seyedahmad (Ahmad) Rahimi\, chair of the NCME Communications Committee\, and aims to build bridges between NCME and adjacent communities\, including AI in education\, learning analytics\, EDM\, EdTech\, and the learning sciences. \n\n\n\nRegister Now\n\n\n\nAbout the Presenters\n\n\n\n\n\nAndrew Ho\, Charles William Eliot Professor of Education\, Harvard Graduate School of EducationAndrew Ho is the Charles William Eliot Professor of Education at the Harvard Graduate School of Education. His work focuses on educational measurement\, testing\, accountability\, and the interpretation and use of assessment results. He is a past president of NCME and delivered the 2025 NCME Presidential Address\, “Metaphors\, Mantras\, and Mnemonics: Communication Competencies in Educational Measurement.” \n\n\n\n\n\n\n\n\n\n\n\n\n\nKristen DiCerbo\, Chief Learning Officer\, Khan AcademyKristen DiCerbo\, Ph.D.\, is Chief Learning Officer at Khan Academy\, where she helps guide teaching and learning strategy and research-based approaches across Khan Academy’s offerings. Her work connects assessment\, learning science\, educational technology\, and AI-supported learning. Before joining Khan Academy\, she was Vice President of Learning Research and Design at Pearson. \n\n\n\n\n\n\n\n\n\n\n\n\n\nAlyssa Wise\, Professor of Technology and Education\, Department of Teaching and Learning\, Vanderbilt University; Director\, LIVE Learning Innovation IncubatorAlyssa Wise is Professor of Technology and Education in the Department of Teaching and Learning at Vanderbilt University and Director of the LIVE Learning Innovation Incubator. Her research combines data science\, learning sciences\, and human-centered design to build and study learning analytics and AI systems that promote equitable and effective learning.
URL:https://ncme.org/event/why-educational-measurement-matters-in-the-age-of-ai/
CATEGORIES:Webinars
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DTSTART;TZID=America/New_York:20261013T120000
DTEND;TZID=America/New_York:20261013T130000
DTSTAMP:20260910T145124Z
CREATED:20260910T145114Z
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SUMMARY:A Computational Psychometrics Ecosystem for Large-Scale Language Assessment: Integrating Measurement\, Artificial Intelligence\, and Governance
DESCRIPTION:This is presented by the Large-Scale Assessments SIGIMIE. \n\n\n\nLarge-scale language assessments increasingly rely on digital platforms that generate extensive linguistic\, behavioural\, and operational data. These data create opportunities to improve assessment design\, scoring\, delivery\, security\, and reporting\, but they also introduce significant measurement challenges. Computational applications are often implemented as separate technical solutions\, making it difficult to determine whether improvements in efficiency or predictive accuracy strengthen the validity of score interpretations and uses—or introduce construct-irrelevant variance\, subgroup differences\, model drift\, and new threats to fairness and comparability. \n\n\n\nThis webinar presents the Computational Psychometrics Ecosystem (CPE)\, an integrative framework for embedding computational methods within established psychometric principles across the lifecycle of large-scale language assessment. The framework comprises six interconnected layers: (1) Measurement Foundations\, including construct definition\, validity\, reliability\, fairness\, and psychometric modelling; (2) Multimodal Data\, encompassing item responses\, response times\, speech\, writing\, and process data; (3) Computational Methods\, including artificial intelligence\, machine learning\, natural language processing\, Bayesian modelling\, and simulation; (4) Assessment Applications\, including automated item generation\, adaptive and multistage testing\, automated scoring\, cognitive diagnosis\, and test-security analysis; (5) Operational Intelligence\, including quality assurance\, anomaly detection\, model monitoring\, predictive analytics\, and decision support; and (6) Governance\, encompassing transparency\, explainability\, privacy\, accountability\, auditability\, and human oversight. \n\n\n\nWithin the CPE\, computational performance is not treated as sufficient evidence of measurement quality. Instead\, each application is evaluated in relation to its intended contribution to the assessment’s validity argument. Operational illustrations from large-scale language assessment are used to identify relevant evidence requirements\, including construct and content representation\, item calibration\, linking and equating\, subgroup performance\, measurement invariance\, human–machine agreement\, classification consistency\, model drift\, uncertainty\, and the consequences of automated decisions. \n\n\n\nThe framework demonstrates how computational psychometrics can extend—rather than replace—Classical Test Theory\, Item Response Theory\, Generalizability Theory\, and Many-Facet Rasch Measurement. It also provides a structure for investigating how innovations affect score comparability\, fairness\, security\, and the appropriate interpretation and use of assessment results. The CPE offers researchers and assessment organisations a common basis for designing\, evaluating\, monitoring\, and governing AI-enabled language assessments while preserving the scientific and ethical foundations of large-scale educational measurement. \n\n\n\nRegister Now\n\n\n\nAbout the Presenter\n\n\n\n\n\nDr Ardeshir Geranpayeh is an assessment and educational technology specialist with more than three decades of experience in psychometrics\, high-stakes testing\, and automated assessment. Before establishing his computational psychometrics consultancy in 2022\, he held several senior positions at Cambridge University Press & Assessment\, including Head of Psychometrics and Data Services and Head of Automated Assessment and Learning. His work integrates established psychometric principles with advances in artificial intelligence and machine learning to support the development of valid\, reliable\, fair\, and innovative assessment systems. \n\n\n\nDr Geranpayeh has delivered more than 80 academic papers and professional presentations internationally. He is also an experienced workshop leader in assessment and test security and has contributed to major conferences organised by the National Council on Measurement in Education\, the Association of Test Publishers\, the Association of Language Testers in Europe\, and the International Test Commission. He is particularly skilled at making complex assessment concepts accessible to audiences from diverse professional backgrounds.
URL:https://ncme.org/event/a-computational-psychometrics-ecosystem-for-large-scale-language-assessment-integrating-measurement-artificial-intelligence-and-governance/
CATEGORIES:Webinars
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261016T130000
DTEND;TZID=America/New_York:20261016T143000
DTSTAMP:20260429T162808Z
CREATED:20260429T162756Z
LAST-MODIFIED:20260429T162808Z
UID:10000034-1792155600-1792161000@ncme.org
SUMMARY:Classroom Assessment Committee Meeting – October
DESCRIPTION:Regular monthly committee meeting. \n\n\n\nRegister Now
URL:https://ncme.org/event/classroom-assessment-committee-meeting-october/
CATEGORIES:Webinars
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