Publication Date
| In 2026 | 0 |
| Since 2025 | 1 |
| Since 2022 (last 5 years) | 1 |
| Since 2017 (last 10 years) | 1 |
| Since 2007 (last 20 years) | 2 |
Descriptor
| Computational Linguistics | 2 |
| Correlation | 2 |
| Individual Differences | 2 |
| Natural Language Processing | 2 |
| Reading Comprehension | 2 |
| Accuracy | 1 |
| Artificial Intelligence | 1 |
| Computer Software | 1 |
| Connected Discourse | 1 |
| Cues | 1 |
| Data Analysis | 1 |
| More ▼ | |
Author
| Allen, Laura K. | 1 |
| Crossley, Scott A. | 1 |
| Danielle S. McNamara | 1 |
| Linh Huynh | 1 |
| McNamara, Danielle S. | 1 |
| Snow, Erica L. | 1 |
Publication Type
| Reports - Research | 2 |
| Journal Articles | 1 |
Education Level
| High Schools | 1 |
| Secondary Education | 1 |
Audience
Location
| Arizona | 1 |
Laws, Policies, & Programs
Assessments and Surveys
| Gates MacGinitie Reading Tests | 1 |
What Works Clearinghouse Rating
Linh Huynh; Danielle S. McNamara – Grantee Submission, 2025
We conducted two experiments to assess the alignment between Generative AI (GenAI) text personalization and hypothetical readers' profiles. In Experiment 1, four LLMs (i.e., Claude 3.5 Sonnet; Llama; Gemini Pro 1.5; ChatGPT 4) were prompted to tailor 10 science texts (i.e., biology, chemistry, physics) to accommodate four different profiles…
Descriptors: Natural Language Processing, Profiles, Individual Differences, Semantics
Crossley, Scott A.; Allen, Laura K.; Snow, Erica L.; McNamara, Danielle S. – Journal of Educational Data Mining, 2016
This study investigates a novel approach to automatically assessing essay quality that combines natural language processing approaches that assess text features with approaches that assess individual differences in writers such as demographic information, standardized test scores, and survey results. The results demonstrate that combining text…
Descriptors: Essays, Scoring, Writing Evaluation, Natural Language Processing

Peer reviewed
Direct link
