Generative Artificial Intelligence (AI)
Generative AI is a transformative technology with the potential to revolutionize workflows across Adelphi University.
While faculty and students delve into the exciting possibilities and ethical considerations of generative AI through the AI Task Force, this technology also has great potential for administrative workflows. Generative AI offers significant advantages for staff, streamlining processes and freeing up valuable time for higher-level work.
Here are a few practical ways to leverage generative AI:
- Enhanced Communication: AI can personalize emails, generate reports, and translate documents, streamlining staff communication with students, faculty, and external parties.
- Idea Generation: Brainstorming new initiatives or solutions? Generative AI can suggest innovative approaches and content, sparking creative problem-solving.
- Data Analysis and Automation: AI can analyze vast datasets, identify trends, and automate repetitive tasks like scheduling or data entry, freeing up staff time for higher-level work.
Microsoft Copilot for Education
Microsoft Copilot is an AI-powered chat assistant designed specifically for education and is available to Adelphi University faculty and staff who have Microsoft 365 accounts. It provides real-time answers to questions using the latest AI models like GPT-4 and DALL-E 3.
Google Gemini
The current version of Gemini, powered by the Gemini AI model, is a large language model capable of performing a wide range of tasks with high accuracy and fluency.
Additional AI-enabled applications used at Adelphi*
- Adele: Chat Support
- Adobe AI
- Aruba AirWave
- Google Workspace: Gemini
- Microsoft Entra ID
- Microsoft Teams: Microsoft Copilot
- Panopto: Powered by AI
- Qualtrics AI
- Scribe: Powered by AI
- Smartsheet AI tools
- Sophos AI
- SurveyMonkey: Build Surveys with AI
- Zoom AI Companion
*Please be aware that some restrictions apply.
Data Classification Standards
Not sure what information should you put into generative AI? Here’s a quick explanation:
Restricted |
Sensitive |
Internal |
Public |
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This category includes highly sensitive data that is tightly controlled due to legal, regulatory, or ethical considerations. Examples might include Protected Health Information (PHI), classified government documents, trade secrets, and confidential agreements. It’s crucial to restrict access to this information and ensure it is not used in generative AI systems without proper authorization. |
Sensitive data encompasses information that, if compromised, could cause harm to individuals or organizations. This includes personal data— de-identified or personally identifiable information (PII)—such as financial information, and health records. Handling this data requires robust security measures to safeguard against unauthorized access or disclosure. |
Internal data refers to information used exclusively within an organization for its operations or decision-making processes. This may include proprietary algorithms, employee records, and strategic plans. Access to internal data should be restricted to authorized personnel, and its use in generative AI systems should be carefully controlled to prevent leaks or misuse. |
Publicly available data is information that is freely accessible to anyone and does not require special authorization to view or use. This includes data from public websites, government reports, and publicly disclosed research findings. It’s still important to exercise caution and ensure compliance with any applicable usage terms or copyrights when incorporating public information into generative AI systems. |