Productivity AutomationVerified
Reviewed and published by trentmaziarz, July 7, 2026. Discovered and drafted by our automated research pipeline.
MassDOT integrated GitHub Copilot into its internal IT department in summer 2024, giving software developers an AI tool that suggests and completes code as they write it.
Details
GitHub Copilot is an AI-powered coding assistant developed by GitHub and OpenAI that generates code suggestions in real time within developers' code editors. MassDOT's deputy CIO confirmed the rollout during a May 2025 presentation, describing it as one of the agency's early AI deployments within its IT department. The practice is limited to MassDOT IT staff who write software for the agency's systems.
Products affected
MassDOT IT internal software development
Sources & Evidence
Cite this record
Trace Foundation. (2026). Massachusetts Department of Transportation (MassDOT): MassDOT integrated GitHub Copilot into its internal IT department in summer 2024, giving software developers an AI tool that suggests and completes code as they write it (data as of 2026-07-07) [Data set record]. AI Trace. https://www.aitrace.org/r/practice/fe734232-cf78-4941-b7b5-a8b1937c4d20. Accessed October 5, 2026.
- Stable link
- https://www.aitrace.org/r/practice/fe734232-cf78-4941-b7b5-a8b1937c4d20
- Data as of
- July 7, 2026
- Last verified
- Not recorded
Other practices by Massachusetts Department of Transportation (MassDOT)
Data AnalysisMassDOT tested an AI framework developed with UMass Lowell that analyzes aerial photographs to automatically detect and classify crosswalks across the entire state of Massachusetts, identifying approximately 88,000 crosswalks in 2021 imagery.ProductivityMassDOT deployed HEKA (Highway Engineer Knowledge Assistant), an internal AI chatbot that helps highway engineers quickly find answers to questions about project rules, regulations, and design specifications. The tool went live in November 2024 and is used daily by engineers across the Highway Division.Data AnalysisMassDOT tested machine learning combined with GIS mapping to build a model that identifies roads with the highest concentrations of serious crashes, known as a High-Injury Network (HIN).
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