In an educational setting, an estimate of the difficulty of Multiple-Choice Questions (MCQs), a commonly used strategy to assess learning progress, constitutes very useful information for both teachers and students. Since human assessment is costly from multiple points of view, automatic approaches to MCQ item difficulty estimation are…

There are a lot of very interesting scientific concepts to learn in natural and social science. The initial concepts that the student possesses may contradict the actual concepts, which is what causes misconceptions. Misconceptions are identified using misconception detection test tools. In fact, the development of the use of diagnostic test…

Diagnostic tests are typically administered in a multiple-choice (MC) format due to their advantages of objectivity and time efficiency. The MC-deterministic input, noisy "and" gate (DINA) family of models, a representative class of cognitive diagnostic models for MC items, efficiently and parsimoniously estimates the mastery profiles of…

Exams are designed to rank students objectively by their abilities, including elements such as time limits, the number and difficulty of questions, and negative marking policies. Using data from a lab-in-field experiment, we develop and estimate a model of student behavior in multiple-choice exams that incorporates the effects of time constraints…

Background/purpose: This study aimed to reveal the accuracy of estimation of multiple-choice test items parameters following the models of the item-response theory in measurement. Materials/methods: The researchers depended on the measurement accuracy indicators, which express the absolute difference between the estimated and actual values of the…

Rasch modelling is a powerful tool for evaluating item performance, measuring drift in difficulty over time, and comparing students who sat assessments at different times or at different sites. Here, we use data from thirty UK medical schools to describe the benefits of Rasch modelling in quality assurance and the barriers to using it. Sixty…

This study evaluates the performance of three leading AI chatbots--OpenAI's ChatGPT, Google's Gemini, and Microsoft Bing Copilot--in answering multiple choice questions (MCQs) from the UGC-NET Education paper. Using 150 randomly selected questions from examination cycles between June 2019 and December 2023, the chatbots' accuracy was assessed…

Enemy items are two test items that should not be presented to a candidate on the same test. Identifying enemies is essential for personnel assessment, as they weaken the measurement precision and validity of a test. In this research, we examined the effectiveness of lexical and semantic natural language processing techniques for identifying enemy…

Inclusive Postsecondary Education programs are designed to provide students with disabilities the opportunity to seek postsecondary education while receiving explicit instruction and support in social and emotional, independent living, and vocational skills. While students enroll in college classes, there is little research available for how to…

This research set out to enhance our understanding of the exam techniques and types of written annotations or markings that learners may wish to use to support their thinking when taking digital multiple-choice exams. Additionally, we aimed to further explore issues around the factors that contribute to learners writing less rough work and…

Textbook anatomy depiction of the hepatobiliary tree is present in 55%-62% of the population. Misidentification of hepatobiliary variants can lead to bile duct injuries in cholecystectomies. A better understanding of variants has been cited as a key area for improvement in anatomy education. The aim of this study was to compare the effectiveness…

Multiple-choice questions (MCQs) are widely used for assessment in medical education. While human-generated MCQs benefit from pedagogical insight, creating high-quality items is time intensive. With the advent of artificial intelligence (AI), tools like DeepSeek R1 offer potential for automated MCQ generation, though their educational validity…

Automated multiple-choice question (MCQ) generation is valuable for scalable assessment and enhanced learning experiences. How-ever, existing MCQ generation methods face challenges in ensuring plausible distractors and maintaining answer consistency. This paper intro-duces a method for MCQ generation that integrates reasoning-based explanations…

Student-centered teaching techniques and equitable assessments are crucial in high-quality education. Asking students to write multiple-choice questions (MCQs) over course content and including them on exams may address both concurrently. Much of the work surveying students' responses to this practice involved graduate level education in…

The urgent need to train military and civilian responders in combat casualty care during large-scale operations presents challenges due to the variability of learner preparedness and the resource demands of traditional curriculum development. This study examines the application of generative artificial intelligence (AI) in authoring and evaluating…