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Wesley Morris; Langdon Holmes; Joon Suh Choi; Scott Crossley – International Journal of Artificial Intelligence in Education, 2025
Recent developments in the field of artificial intelligence allow for improved performance in the automated assessment of extended response items in mathematics, potentially allowing for the scoring of these items cheaply and at scale. This study details the grand prize-winning approach to developing large language models (LLMs) to automatically…
Descriptors: Automation, Computer Assisted Testing, Mathematics Tests, Scoring
Archana Praveen Kumar; Ashalatha Nayak; Manjula Shenoy K.; Chaitanya; Kaustav Ghosh – International Journal of Artificial Intelligence in Education, 2024
Multiple Choice Questions (MCQs) are a popular assessment method because they enable automated evaluation, flexible administration and use with huge groups. Despite these benefits, the manual construction of MCQs is challenging, time-consuming and error-prone. This is because each MCQ is comprised of a question called the "stem", a…
Descriptors: Multiple Choice Tests, Test Construction, Test Items, Semantics
Stefan Ruseti; Ionut Paraschiv; Mihai Dascalu; Danielle S. McNamara – International Journal of Artificial Intelligence in Education, 2024
Automated Essay Scoring (AES) is a well-studied problem in Natural Language Processing applied in education. Solutions vary from handcrafted linguistic features to large Transformer-based models, implying a significant effort in feature extraction and model implementation. We introduce a novel Automated Machine Learning (AutoML) pipeline…
Descriptors: Computer Assisted Testing, Scoring, Automation, Essays
Rebecka Weegar; Peter Idestam-Almquist – International Journal of Artificial Intelligence in Education, 2024
Machine learning methods can be used to reduce the manual workload in exam grading, making it possible for teachers to spend more time on other tasks. However, when it comes to grading exams, fully eliminating manual work is not yet possible even with very accurate automated grading, as any grading mistakes could have significant consequences for…
Descriptors: Grading, Computer Assisted Testing, Introductory Courses, Computer Science Education
Barrett, Michelle D.; Jiang, Bingnan; Feagler, Bridget E. – International Journal of Artificial Intelligence in Education, 2022
The appeal of a shorter testing time makes a computer adaptive testing approach highly desirable for use in multiple assessment and learning contexts. However, for those who have been tasked with designing, configuring, and deploying adaptive tests for operational use at scale, preparing an adaptive test is anything but simple. The process often…
Descriptors: Adaptive Testing, Computer Assisted Testing, Test Construction, Design Requirements
Ormerod, Christopher; Lottridge, Susan; Harris, Amy E.; Patel, Milan; van Wamelen, Paul; Kodeswaran, Balaji; Woolf, Sharon; Young, Mackenzie – International Journal of Artificial Intelligence in Education, 2023
We introduce a short answer scoring engine made up of an ensemble of deep neural networks and a Latent Semantic Analysis-based model to score short constructed responses for a large suite of questions from a national assessment program. We evaluate the performance of the engine and show that the engine achieves above-human-level performance on a…
Descriptors: Computer Assisted Testing, Scoring, Artificial Intelligence, Semantics
Keith Cochran; Clayton Cohn; Peter Hastings; Noriko Tomuro; Simon Hughes – International Journal of Artificial Intelligence in Education, 2024
To succeed in the information age, students need to learn to communicate their understanding of complex topics effectively. This is reflected in both educational standards and standardized tests. To improve their writing ability for highly structured domains like scientific explanations, students need feedback that accurately reflects the…
Descriptors: Science Process Skills, Scientific Literacy, Scientific Concepts, Concept Formation
Vittorini, Pierpaolo; Menini, Stefano; Tonelli, Sara – International Journal of Artificial Intelligence in Education, 2021
Massive open online courses (MOOCs) provide hundreds of students with teaching materials, assessment tools, and collaborative instruments. The assessment activity, in particular, is demanding in terms of both time and effort; thus, the use of artificial intelligence can be useful to address and reduce the time and effort required. This paper…
Descriptors: Artificial Intelligence, Formative Evaluation, Summative Evaluation, Data

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