Posts by Eric Tucker
Schools Are Testing the Past: How Assessment Must Evolve for Writing, Speaking, and Listening in the AI Age
When a student completes a writing test in a locked browser, we learn what they can do alone. But the world asks something harder: can they use AI without being used by it? In this sharp and timely essay, Eric Tucker argues that assessment for writing, speaking, and listening has not kept pace with how communication actually works, and maps a practical path forward. Education leaders navigating AI policy, curriculum design, and high-stakes testing will find both a diagnostic and a blueprint here.
We’ve Been Judging Symphony Rehearsals by the Last Note: Why Multimodal AI Can Finally Measure Mathematical Practice
For decades, math assessments have measured the residue of thinking rather than thinking itself. In this sharp, research-grounded piece, Eric Tucker argues that multimodal AI can finally make the Standards for Mathematical Practice visible, capturing spoken arguments, strategic shifts, and productive struggle in real time. For district leaders and curriculum directors rethinking assessment infrastructure, this is essential reading on what it means to measure what actually matters.
A Hint is Not a Diploma: Consequential Educational Decision Making in the Multimodal AI Era
As AI accelerates into every corner of schooling, education leaders face a question that is both urgent and deeply human: who holds the gavel? In this provocative new piece, Eric Tucker argues that the real risk is not AI itself but our failure to govern it proportionally, treating a graduation algorithm with the same scrutiny as a spelling hint. This consequence-tiered framework gives leaders, policymakers, and educators a practical architecture for protecting learners while keeping innovation alive.
Responsible Inference Engines: Safeguarding Students with Learning Differences in the AI Era
The forthcoming brief Prioritizing Students with Disabilities in AI Policy (EALA/New America) highlights a critical reality: 73% of students with disabilities use AI for coursework, and 57% of special educators use it to draft IEPs. Yet, 0% of AI-based interventions in a 2025 systematic review rate as “Low…
Educational fMRIs: Dynamic Pedagogy and Pedagogical Analysis in the Multimodal AI Era
Explore how education fMRI can enhance understanding of student assessment beyond mere scores and classifications.
Measuring What Matters: From Blunt Sorting to Human Thriving Powered by Multimodal AI
Explore the potential of Advancing Multimodal AI to reshape educational metrics and enhance human potential measurement.
Innovating What We Measure: Assessment in the Service of Human Potential in an Era of AI and Uncertainty
To deliver on the promise of human potential, we must rebalance from the “assessment OF education” to “assessment FOR education,” transforming measurement into a dynamic pedagogical transaction that actively improves teaching and learning.
Pathological Explanations for Learning Differences: Untangling the Signal from the Noise to Measure Ability in the Age of AI
Hand Isaac Newton a tablet for a physics exam today, and he would bomb it—his brilliance obscured by an inability to navigate a simulation. We educators commit this malpractice against neurodivergent students daily, diagnosing their minds as deficient instead of our broken tests. Myth vs. Science Pamela Cantor,…
Useful by Design: Innovating For Whom We Measure in the Multimodal AI Era
Discover how multi-modal AI can transform educational assessment design for greater efficiency and usability.
The Architecture of Ability: How Evidence-Centered Design and UDL Can Shape AI-Era Assessment
ECD, UDL, and The Cow Path Educational measurement is at a crossroads. Multimodal artificial intelligence (AI) finally opens a pathway to move beyond the industrial-era, one-size-fits-all summative test in favor of unobtrusive, agentic, and adaptive assessment. But directing raw computing power at children without guardrails is ill-advised. Using…