Publication Date
| In 2026 | 0 |
| Since 2025 | 2 |
| Since 2022 (last 5 years) | 5 |
| Since 2017 (last 10 years) | 6 |
| Since 2007 (last 20 years) | 6 |
Descriptor
| Automation | 6 |
| Natural Language Processing | 6 |
| Artificial Intelligence | 4 |
| Computer Assisted Testing | 3 |
| Algorithms | 2 |
| Computer Interfaces | 2 |
| English | 2 |
| Essays | 2 |
| French | 2 |
| Multiple Choice Tests | 2 |
| Portuguese | 2 |
| More ▼ | |
Author
| Mihai Dascalu | 6 |
| Danielle S. McNamara | 5 |
| Stefan Ruseti | 4 |
| Andreea Dutulescu | 2 |
| Denis Iorga | 2 |
| Ionut Paraschiv | 2 |
| Cecile A. Perret | 1 |
| Chaohua Ou | 1 |
| Danielle McNamara | 1 |
| Langdon Holmes | 1 |
| Laura K. Allen | 1 |
| More ▼ | |
Publication Type
| Reports - Research | 6 |
| Journal Articles | 2 |
| Speeches/Meeting Papers | 2 |
Education Level
| High Schools | 1 |
| Higher Education | 1 |
| Postsecondary Education | 1 |
| Secondary Education | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Andreea Dutulescu; Stefan Ruseti; Denis Iorga; Mihai Dascalu; Danielle S. McNamara – Grantee Submission, 2024
The process of generating challenging and appropriate distractors for multiple-choice questions is a complex and time-consuming task. Existing methods for an automated generation have limitations in proposing challenging distractors, or they fail to effectively filter out incorrect choices that closely resemble the correct answer, share synonymous…
Descriptors: Multiple Choice Tests, Artificial Intelligence, Attention, Natural Language Processing
Andreea Dutulescu; Stefan Ruseti; Denis Iorga; Mihai Dascalu; Danielle S. McNamara – Grantee Submission, 2025
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…
Descriptors: Automation, Computer Assisted Testing, Multiple Choice Tests, Natural Language Processing
Stefan Ruseti; Ionut Paraschiv; Mihai Dascalu; Danielle S. McNamara – Grantee Submission, 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
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
Wesley Morris; Scott Crossley; Langdon Holmes; Chaohua Ou; Mihai Dascalu; Danielle McNamara – International Journal of Artificial Intelligence in Education, 2025
As intelligent textbooks become more ubiquitous in classrooms and educational settings, the need to make them more interactive arises. An alternative is to ask students to generate knowledge in response to textbook content and provide feedback about the produced knowledge. This study develops Natural Language Processing models to automatically…
Descriptors: Formative Evaluation, Feedback (Response), Textbooks, Artificial Intelligence
Danielle S. McNamara; Laura K. Allen; Scott A. Crossley; Mihai Dascalu; Cecile A. Perret – Grantee Submission, 2017
Language is of central importance to the field of education because it is a conduit for communicating and understanding information. Therefore, researchers in the field of learning analytics can benefit from methods developed to analyze language both accurately and efficiently. Natural language processing (NLP) techniques can provide such an…
Descriptors: Natural Language Processing, Learning Analytics, Educational Technology, Automation

Peer reviewed
Direct link
