National Training Team | Schools — FAQ Gen AI Dashboard
Description
Categorization of large volume question submissions with suggested generated answers based on past-provided information.
Detailed example
The system outputs data to a Microsoft Power BI report — the report lists the top ten categories of questions asked within a particular office hours session with a suggested question based off submissions and suggested answers to the generated question based off previously provided answers in FAQ documentation.
AI / analytics pattern
Natural Language Processing: AI that processes, interprets, and shares information in human language.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
increased efficiency. There are currently six permanent members of the NTT|S staff and approximately 40,000 SCOs. It is physically impossible for a staff this size to answer all of the questions they ask in a given session. The model reduces the count of overall questions to a reasonable amount answerable by a subject matter expert (SME). It also uses previous answers provided by SMEs in the case of duplicate or similar questions to speed preparation of FAQ documentation for publication following a session — if they’ve answered a question before, they don’t need to spend time answering it again. Currently, the project has fallen below expectations in terms of usefulness to the end users. NTT|S suffered a major operational setback earlier this year when the contract with Adobe Connect was severed unexpectedly by the new administration. Implementing Office Hours using Microsoft Teams as an alternative has had several obstacles, including a substantial decrease in questions SCOs are able to submit within a session. Because of the lower volume, SMEs can often answer most questions within a session and review remaining questions without the assistance of the model.
Controls / human review
ATO: Not reported; PIA: Not published