Please use this identifier to cite or link to this item:
https://dair.nps.edu/handle/123456789/5029
Title: | Improving Clarity and Accessibility in Public Procurement Documents: An AI–Powered Approach to Plain Writing Compliance |
Authors: | Mayra Hernandez, Chace Morris |
Keywords: | Public Procurement Artificial Intelligence ChatGPT Plain Writing Principles Large Language Models |
Issue Date: | 27-Dec-2023 |
Publisher: | Acquisition Research Program |
Citation: | Published--Unlimited Distribution |
Series/Report no.: | Acquisition Management;NPS-AM-24-010 |
Abstract: | This research addressed the pervasive issue of complex and unclear communication in Department of Defense procurement processes, which hinders transparency and poses a barrier to entry in public sector markets. Utilizing a two-phase approach, this research sought to enhance public procurement communications and align them with the Plain Writing Act. Phase 1 utilized text analysis software and artificial intelligence (AI) tools to refine procurement documents, focusing on clarity and adherence to Plain Writing Principles. The analysis revealed substantial variations in complexity and readability levels, with most Original documents not easily understood by the general public. AI-Refined versions effectively improved readability and comprehension, demonstrating AI’s potential to simplify complex language and enhance document accessibility. Phase 2 assessed stakeholder perceptions of Original, AI-Refined, and AI/Human-Refined documents through surveys. A key finding was the universal preference for AI-enhanced versions over Original documents. Purely AI-driven revisions were perceived as more effective and better aligned with plain writing standards than those involving human collaboration. Overall, the research highlights AI’s potential as an innovative approach to improve the clarity and effectiveness of public procurement communications, making complex information more understandable and accessible to a broader audience. |
Description: | Acquisition Management / Graduate Student Research |
URI: | https://dair.nps.edu/handle/123456789/5029 |
Appears in Collections: | NPS Graduate Student Theses & Reports |
Files in This Item:
File | Description | Size | Format | |
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NPS-AM-24-010.pdf | Student Thesis | 2.57 MB | Adobe PDF | View/Open |
Student Poster.pdf | Student Poster | 392.89 kB | Adobe PDF | View/Open |
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