The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Automatically Enriching Content for a Behavioral Health Learning Management System: a First Look
Open accessAbstract
Deep generative AI models have been evolving rapidly and are being applied to assist users in many domains.We consider an initial case study of applying this technology to semi-automated content enrichment for an online learning management system used by behavioral health professionals.Our goal is to optimize the use of domain expertise while also scaling the production efficiency of learning assets for users.We identify the possible opportunities for its use, discuss potential challenges and concerns.Finally, we provide prompt engineering strategies and initial quantitative results towards semi-automating one type of rime consuming editorial task, to gauge the feasibility of our approach.Results show that there is significant promise in using such an approach, and suggest that a larger, more rigorous study is required.
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“finding that using ChatGPT improved provider efficiency by 37.5%”
From The Opportunities and Risks of Large Language Models in Mental Health (2024) · cites this paper as [13]
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Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115
Ijadi‐Maghsoodi, R., Harrison, D., Kelman, A. R., Kataoka, S., Langley, A. K., Ramos, N., Cugley, G. M., Alquijay, M. A., Tate, K., Lester, P. E., et al. (2020) · Psychological Trauma Theory Research Practice and Policy · doi:10.1037/tra0000880 · Cited by 13
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The Opportunities and Risks of Large Language Models in Mental Health
Lawrence, H. R., Schneider, R. A., Rubin, S. B., Matarić, M. J., McDuff, D. J., & Jones Bell, M. (2024) · JMIR Mental Health · doi:10.2196/59479 · Cited by 235
The opportunities and risks of large language models in mental health
Lawrence, H. R., Schneider, R. A., Rubin, S. B., Mataric, M. J., McDuff, D. J., & Bell, M. J. (2024) · arXiv · doi:10.48550/arxiv.2403.14814
Metadata
- Authors
- Barish, G., Marlotte, L., Drayton, M., Mogil, C., & Lester, P.
- Affiliations
- University of London (GB)
- University of California, Los Angeles (US)
- Venue
- World Congress on Electrical Engineering and Computer Systems and Science
- Publisher
- Avestia Publishing
- Published
- 2023-08
- ISSN
- 2369-811X (ISSN-L)
- OpenAlex
- W4386074514
- Cited by
- 2
- References
- 2
- Citations by year
- 2026: 1 · 2024: 1
- FWCI
- 0.51
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science (publishedVersion)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- generative AI, content enrichment, virtual learning environments, prompt engineering, online learning, large language models
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