Publication Date
| In 2026 | 0 |
| Since 2025 | 2 |
Descriptor
| Artificial Intelligence | 2 |
| Fidelity | 2 |
| Models | 2 |
| Open Source Technology | 2 |
| Algorithms | 1 |
| Benchmarking | 1 |
| Classification | 1 |
| Computational Linguistics | 1 |
| Computer Simulation | 1 |
| Computer Software | 1 |
| Error Patterns | 1 |
| More ▼ | |
Author
| Alex Lyman | 1 |
| Bryce Hepner | 1 |
| Conrad Borchers | 1 |
| David Wingate | 1 |
| Ethan C. Busby | 1 |
| Joshua R. Gubler | 1 |
| Lisa P. Argyle | 1 |
| Tianze Shou | 1 |
Publication Type
| Reports - Research | 2 |
| Journal Articles | 1 |
| Speeches/Meeting Papers | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Alex Lyman; Bryce Hepner; Lisa P. Argyle; Ethan C. Busby; Joshua R. Gubler; David Wingate – Sociological Methods & Research, 2025
Generative artificial intelligence (AI) has the potential to revolutionize social science research. However, researchers face the difficult challenge of choosing a specific AI model, often without social science-specific guidance. To demonstrate the importance of this choice, we present an evaluation of the effect of alignment, or human-driven…
Descriptors: Artificial Intelligence, Computer Simulation, Open Source Technology, Social Science Research
Conrad Borchers; Tianze Shou – Grantee Submission, 2025
Large Language Models (LLMs) hold promise as dynamic instructional aids. Yet, it remains unclear whether LLMs can replicate the adaptivity of intelligent tutoring systems (ITS)--where student knowledge and pedagogical strategies are explicitly modeled. We propose a prompt variation framework to assess LLM-generated instructional moves' adaptivity…
Descriptors: Benchmarking, Computational Linguistics, Artificial Intelligence, Computer Software

Peer reviewed
Direct link
