Results 221 to 230 of about 5,020 (278)

How to Imagine Educational AI: The Filling of a Pail or the Lighting of a Fire?

open access: yesEducational Theory, Volume 76, Issue 3, Page 316-338, June 2026.
Abstract Recent advances in artificial intelligence (e.g., machine learning, generative AI) have led to increased interest in its application in educational settings. AI companies hope to revolutionize teaching and learning by tailoring material to the individual needs of students, automating parts of teachers' jobs, or analyzing educational data to ...
Michał Wieczorek, Alberto Romele
wiley   +1 more source

Fairness at Risk: Where Bias Emerges in Machine Learning

open access: yesExpert Systems, Volume 43, Issue 6, June 2026.
ABSTRACT Artificial intelligence and machine learning (ML) now shape decisions in healthcare, finance and security, but they can reproduce historical prejudice and inequality. Bias in training data and in model implementation can amplify harm, especially for racial and gender minorities.
Otavio de Paula Albuquerque   +2 more
wiley   +1 more source

The Role of Aspect During Deverbal Word Processing in Greek. [PDF]

open access: yesJ Psycholinguist Res
Tsaprouni E, Manouilidou C.
europepmc   +1 more source

The “Testing Effect” in Undergraduate Endodontic Education: The Impact of Regular Low Stakes Testing on Learning Outcomes and Student Engagement

open access: yesInternational Endodontic Journal, Volume 59, Issue 6, Page 1227-1235, June 2026.
ABSTRACT Aim This study investigates the impact of regular testing on learning outcomes in undergraduate endodontic education. Specifically, it examines whether students perform better at the end of the course on previously tested material compared to non‐tested material and explores the role of students' engagement and perceptions of testing in this ...
M. Kalyva   +4 more
wiley   +1 more source

An Artificial Intelligence‐Enhanced Assessment Framework for Analyzing Middle School Science Students’ Written Responses

open access: yesJournal of Educational Measurement, Volume 63, Issue 2, Summer 2026.
Abstract This study develops and tests a Large Language Model‐based assessment framework that uses a multi‐agent system to analyze students’ written responses, generate scoring rationales, identify uncertainty levels, and assign final scores to support learning.
Namsoo Shin   +7 more
wiley   +1 more source

Evaluating Modified Early Warning Score Compliance to Minimize Unnecessary Intensive Care Unit Admissions: A Descriptive Cross‐Sectional Needs Assessment at a University Teaching Tertiary Care Centre to Inform Quality Improvement Implementation

open access: yesJournal of Evaluation in Clinical Practice, Volume 32, Issue 4, June 2026.
ABSTRACT Background Intensive Care Unit (ICU) resources are scarce in low‐ and middle‐income countries (LMICs), with a median of 0.7 beds per 100,000 population. The Modified Early Warning Score (MEWS) aids early identification of clinical deterioration and supports standardized escalation and ICU triage decisions to optimize critical‐care resource use.
Mazhar Khalil   +15 more
wiley   +1 more source

How Flexible Are Grammars Past Puberty? The Case of Relative Clauses in Turkish‐American Returnees

open access: yesLanguage Learning, Volume 76, Issue 2, Page 391-424, June 2026.
Abstract How flexible are grammars after puberty? To answer this, we test returnees: heritage speakers (HS) born in an immigration context who returned to their homeland in later years. If returnees are targetlike, then language is still malleable after puberty; in contrast, if maturational effects are in play, postpuberty returnees will show ...
Aylin Coşkun Kunduz, Silvina Montrul
wiley   +1 more source

Error Correction Learning of Second Language Verbal Morphology: Associating Imperfect Contingencies in Naturalistic Frequency Distributions

open access: yesLanguage Learning, Volume 76, Issue 2, Page 494-527, June 2026.
Abstract We investigate what is learned from exposure to usage in verbal morphology using an error correction mechanism within an associative learning framework. We computationally simulated how second language (L2) learners would respond to naturalistic input of aspectual usage, characterized by “imperfect contingencies,” given two types of ...
Justyna Mackiewicz   +2 more
wiley   +1 more source

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