Artificial Intelligence

Using Artificial Intelligence in a University Setting

Using artificial intelligence (AI) in a university setting can enhance various aspects of education, research, and administrative processes. This page includes some usage recommendations for implementing AI in a university setting, in addition to the USNH Artificial Intelligence Standard.

The successful integration of AI into a university setting requires thoughtful planning, ethical considerations, transparency, and a commitment to ensuring that AI serves the best interests of students, faculty, and the entire university community.

AI can bring numerous benefits to a university, but it should be used carefully and ethically to ensure it strengthens the overall learning experience and administrative efficiency while maintaining a focus on student success and well-being.

 

Example Artificial Intelligence Use Cases in a University Setting

rESEARCH AND DATA ANALYSIS
  • AI tools may be used for data analysis, research efforts, and optimizing processes to assist faculty and researchers in generating insights and interpreting data sets across disciplines. AI tools can be leveraged to scale research capabilities.
AUTOMATION & Optimization
  • AI can be applied to automate administrative tasks, increasing efficiency and decreasing the overall manual workload. 
Chatbots for support services
  • AI chatbots may be used to provide support and resources for customers and students. Chatbots are able to respond to questions quickly and escalate issues if needed.
virtual labs and simulations
  • AI labs and simulations can enhance students' learning experiences. Virtual environments may allow students to explore complex concepts interactively, obtain hands on learning experiences, and conduct experiments safely.
teaching & Learning
  • AI tools may be used to create presentations that strengthen engagement, such as interactive tools or simulations.
CRITICAL THINKING
  • Students can use AI to generate text, and then examine the results to check for accuracy or search for additional sources to support the information provided. 

Dos and Don’ts of using Artificial Intelligence in a University Setting

Do...

Understand the Objectives:
  • DO clearly define the goals and objectives of using AI in the university. Understand what problems you're trying to solve and what benefits you aim to achieve.
Data Quality:
  • DO ensure that the data used for AI applications is of high quality, accurate, and up to date. Clean, relevant data is essential for effective AI.
Privacy and Data Security:
  • DO prioritize the privacy and data security of students, faculty, and staff. Implement robust security measures to protect sensitive information.
Ethical Considerations:
  • DO consider the ethical implications of AI usage, including issues related to bias, fairness, transparency, and accountability. Develop clear ethical guidelines.
Transparency:
  • DO maintain transparency in AI-driven decision-making processes. Ensure that the university community understands how AI is being used.
Testing and Validation:
  • DO thoroughly test and validate AI algorithms and models to ensure they are accurate and reliable, especially in areas like admissions and grading.
User Training:
  • DO provide training and support for faculty, staff, and students to effectively use AI tools and platforms.
Continuous Evaluation:
  • DO regularly evaluate the performance and impact of AI applications and be prepared to make adjustments based on feedback and data.
  • DO stay compliant with all relevant laws and regulations related to AI usage in education.
Collaboration and Partnerships:
  • DO collaborate with AI experts, other universities, and relevant organizations to stay informed about the latest AI developments and best practices.

Don't...

Bias and Discrimination:
  • DON'T allow AI systems to perpetuate bias or discrimination. Regularly audit and correct AI systems for fairness.
Overreliance:
  • DON'T over-rely on AI to the detriment of human judgment and expertise, especially in sensitive areas like student evaluations and academic counseling.
Lack of Transparency:
  • DON'T implement AI systems that operate in a "black box" manner, where their decisions cannot be explained or understood.
Ignoring Ethical Concerns:
  • DON'T ignore ethical concerns in AI use. Address ethical dilemmas and concerns promptly, and involve relevant stakeholders in decision-making.
Inadequate Data Protection:
  • DON'T compromise data protection and cybersecurity. Neglecting these areas can lead to data breaches and privacy violations.
Ineffective Communication:
  • DON'T assume that the university community understands AI applications. Communicate their purpose, benefits, and potential impact clearly.
Uncontrolled Automation:
  • DON'T fully automate critical processes without human oversight, particularly when it comes to academic decisions, as this can undermine the human element of education.
Ignoring Feedback:
  • DON'T disregard feedback from students, faculty, and staff regarding AI systems. Continuous improvement requires listening to those affected by AI usage.
Lack of Accountability:
  • DON'T overlook accountability. Clearly define roles and responsibilities in the use of AI and establish mechanisms for redress in case of errors.
  • DON'T implement AI without a clear legal and ethical framework. Universities should establish guidelines and policies to govern AI use.
QUESTIONS?

For any questions or to learn more about AI, contact Cybersecurity GRC at cybersecurity.grc@usnh.edu.