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Harold Doran; Testsuhiro Yamada; Ted Diaz; Emre Gonulates; Vanessa Culver – Journal of Educational Measurement, 2025
Computer adaptive testing (CAT) is an increasingly common mode of test administration offering improved test security, better measurement precision, and the potential for shorter testing experiences. This article presents a new item selection algorithm based on a generalized objective function to support multiple types of testing conditions and…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Algorithms
Bryan R. Drost; Char Shryock – Phi Delta Kappan, 2025
Creating assessment questions aligned to standards is a time-consuming task for teachers, but large language models such as ChatGPT can help. Bryan Drost & Char Shryock describe a three-step process for using ChatGPT to create assessments: 1) Ask ChatGPT to break standards into measurable targets. 2) Determine how much time to spend on each…
Descriptors: Artificial Intelligence, Computer Software, Technology Integration, Teaching Methods
Jila Niknejad; Margaret Bayer – International Journal of Mathematical Education in Science and Technology, 2025
In Spring 2020, the need for redesigning online assessments to preserve integrity became a priority to many educators. Many of us found methods to proctor examinations using Zoom and proctoring software. Such examinations pose their own issues. To reduce the technical difficulties and cost, many Zoom proctored examination sessions were shortened;…
Descriptors: Mathematics Instruction, Mathematics Tests, Computer Assisted Testing, Computer Software
Valentina Albano; Donatella Firmani; Luigi Laura; Jerin George Mathew; Anna Lucia Paoletti; Irene Torrente – Journal of Learning Analytics, 2023
Multiple-choice questions (MCQs) are widely used in educational assessments and professional certification exams. Managing large repositories of MCQs, however, poses several challenges due to the high volume of questions and the need to maintain their quality and relevance over time. One of these challenges is the presence of questions that…
Descriptors: Natural Language Processing, Multiple Choice Tests, Test Items, Item Analysis
Kyung-Mi O. – Language Testing in Asia, 2024
This study examines the efficacy of artificial intelligence (AI) in creating parallel test items compared to human-made ones. Two test forms were developed: one consisting of 20 existing human-made items and another with 20 new items generated with ChatGPT assistance. Expert reviews confirmed the content parallelism of the two test forms.…
Descriptors: Comparative Analysis, Artificial Intelligence, Computer Software, Test Items
Roger Young; Emily Courtney; Alexander Kah; Mariah Wilkerson; Yi-Hsin Chen – Teaching of Psychology, 2025
Background: Multiple-choice item (MCI) assessments are burdensome for instructors to develop. Artificial intelligence (AI, e.g., ChatGPT) can streamline the process without sacrificing quality. The quality of AI-generated MCIs and human experts is comparable. However, whether the quality of AI-generated MCIs is equally good across various domain-…
Descriptors: Item Response Theory, Multiple Choice Tests, Psychology, Textbooks
Tatiana Chaiban; Zeinab Nahle; Ghaith Assi; Michelle Cherfane – Discover Education, 2024
Background: Since it was first launched, ChatGPT, a Large Language Model (LLM), has been widely used across different disciplines, particularly the medical field. Objective: The main aim of this review is to thoroughly assess the performance of the distinct version of ChatGPT in subspecialty written medical proficiency exams and the factors that…
Descriptors: Medical Education, Accuracy, Artificial Intelligence, Computer Software
Kyeng Gea Lee; Mark J. Lee; Soo Jung Lee – International Journal of Technology in Education and Science, 2024
Online assessment is an essential part of online education, and if conducted properly, has been found to effectively gauge student learning. Generally, textbased questions have been the cornerstone of online assessment. Recently, however, the emergence of generative artificial intelligence has added a significant challenge to the integrity of…
Descriptors: Artificial Intelligence, Computer Software, Biology, Science Instruction
Harun Bayer; Fazilet Gül Ince Araci; Gülsah Gürkan – International Journal of Technology in Education and Science, 2024
The rapid advancement of artificial intelligence technologies, their pervasive use in every field, and the growing understanding of the benefits they bring have led actors in the education sector to pursue research in this field. In particular, the use of artificial intelligence tools has become more prevalent in the education sector due to the…
Descriptors: Artificial Intelligence, Computer Software, Computational Linguistics, Technology Uses in Education
Rao, Dhawaleswar; Saha, Sujan Kumar – IEEE Transactions on Learning Technologies, 2020
Automatic multiple choice question (MCQ) generation from a text is a popular research area. MCQs are widely accepted for large-scale assessment in various domains and applications. However, manual generation of MCQs is expensive and time-consuming. Therefore, researchers have been attracted toward automatic MCQ generation since the late 90's.…
Descriptors: Multiple Choice Tests, Test Construction, Automation, Computer Software
Selcuk Acar; Denis Dumas; Peter Organisciak; Kelly Berthiaume – Grantee Submission, 2024
Creativity is highly valued in both education and the workforce, but assessing and developing creativity can be difficult without psychometrically robust and affordable tools. The open-ended nature of creativity assessments has made them difficult to score, expensive, often imprecise, and therefore impractical for school- or district-wide use. To…
Descriptors: Thinking Skills, Elementary School Students, Artificial Intelligence, Measurement Techniques
Blake Edward Morris – ProQuest LLC, 2024
Aim: This study aimed to describe the practices and perceptions of nurse educators when developing NGN-style test items. Methods: This study used a qualitative descriptive methodology to explore the practices and perceptions of nursing faculty when developing NGN-style test items from nursing faculty in accredited prelicensure programs in the…
Descriptors: Nursing Education, Test Items, Test Construction, Facilitators (Individuals)
Zhang, Lishan; VanLehn, Kurt – Interactive Learning Environments, 2021
Despite their drawback, multiple-choice questions are an enduring feature in instruction because they can be answered more rapidly than open response questions and they are easily scored. However, it can be difficult to generate good incorrect choices (called "distractors"). We designed an algorithm to generate distractors from a…
Descriptors: Semantics, Networks, Multiple Choice Tests, Teaching Methods
Ravand, Hamdollah; Baghaei, Purya – International Journal of Testing, 2020
More than three decades after their introduction, diagnostic classification models (DCM) do not seem to have been implemented in educational systems for the purposes they were devised. Most DCM research is either methodological for model development and refinement or retrofitting to existing nondiagnostic tests and, in the latter case, basically…
Descriptors: Classification, Models, Diagnostic Tests, Test Construction
Mohammed, Aisha; Dawood, Abdul Kareem Shareef; Alghazali, Tawfeeq; Kadhim, Qasim Khlaif; Sabti, Ahmed Abdulateef; Sabit, Shaker Holh – International Journal of Language Testing, 2023
Cognitive diagnostic models (CDMs) have received much interest within the field of language testing over the last decade due to their great potential to provide diagnostic feedback to all stakeholders and ultimately improve language teaching and learning. A large number of studies have demonstrated the application of CDMs on advanced large-scale…
Descriptors: Reading Comprehension, Reading Tests, Language Tests, English (Second Language)