Artificial intelligence has moved from an emerging technology to an everyday consideration for colleges and universities. Students use generative AI to explain concepts, organize ideas and support research. Faculty are exploring its role in instruction and assessment, while administrators are evaluating AI-assisted tools for student services, institutional research and routine operations.
The central question is no longer whether artificial intelligence will affect higher education. Instead, institutional leaders must determine where AI can improve learning and operations, what risks require oversight and how its use should align with the institution’s mission.
How Is AI Being Used in Higher Education?
Artificial intelligence in higher education includes more than generative tools that produce text or images. Institutions may also use predictive analytics, adaptive learning platforms, virtual assistants, transcription services and workflow automation.
Common applications include:
- Providing students with supplemental explanations or practice activities
- Helping faculty develop instructional materials and organize course content
- Supporting accessibility through captioning, transcription and translation
- Responding to routine questions about enrollment or campus services
- Identifying patterns that may inform student-support efforts
- Summarizing information or automating repetitive administrative tasks
- Helping researchers review information, analyze data or generate preliminary ideas
These applications can create value, but their effectiveness depends on how they are designed, governed and evaluated.
AI Use Among College Students Is Increasing
Student adoption illustrates how quickly expectations are changing.
The United Kingdom-based Higher Education Policy Institute’s 2026 survey of 1,054 full-time undergraduate students found that 95% had used AI in at least one way and 94% had used generative AI to support assessed work. The study also found that only 36% felt encouraged by their institution to use AI and 38% said their institution provided access to AI tools. Although these results should not be treated as representative of every U.S. institution, they demonstrate the speed at which student behavior can move ahead of institutional policy. (HEPI, 2026)
This gap presents an important leadership challenge. A policy that simply permits or prohibits AI may not give students enough direction. They also need to understand:
- Which forms of AI assistance are acceptable for a particular assignment
- When AI use must be disclosed or cited
- How to evaluate AI-generated information
- Why personal, confidential or institutional information should not be entered into unapproved tools
- Which parts of the learning process students must complete independently
Clear, assignment-specific expectations can help students use AI without replacing the critical thinking, analysis and subject knowledge that higher education is intended to develop.
Potential Benefits of AI in Higher Education
More Personalized Learning Support
AI tools can provide explanations, examples and practice opportunities based on a student’s questions. Used appropriately, these capabilities may supplement faculty instruction and help students work through difficult material outside scheduled class time.
AI should not be treated as a substitute for faculty expertise, academic advising or peer interaction. Its strongest role may be as an additional resource within a learning environment that continues to emphasize human feedback and intellectual engagement.
Greater Accessibility
Transcription, captioning, text-to-speech, speech-to-text and translation tools may make course materials easier to access. Generative AI can also help explain complex material in different formats or at different levels of detail.
Leaders must still evaluate the accuracy and accessibility of AI-generated content. A tool marketed as accessible does not automatically meet every student’s needs or an institution’s legal and technical requirements.
Faculty and Staff Support
Faculty may use approved AI tools to brainstorm learning activities, create preliminary outlines, develop practice questions or revise instructional materials. Staff may use them to summarize nonconfidential information, draft routine communications or support administrative workflows.
These uses can save time, but human review remains essential. AI-generated materials may contain errors, invented references, biased language or content that does not fit the intended audience.
More Efficient Institutional Operations
Higher education institutions may apply AI to areas such as enrollment communications, scheduling, information technology support and document processing. Predictive analytics may also help leaders identify trends related to enrollment, course demand or student support.
However, an AI-generated prediction should inform, not automatically determine, a decision affecting a student or employee. Leaders should understand what data a system uses, how its output was produced and where human judgment enters the process.
Risks and Ethical Considerations
Accuracy and Fabricated Information
Generative AI produces responses based on patterns in data. It does not independently verify every claim and may provide inaccurate information or fabricate sources.
Students, faculty and staff should therefore treat AI-generated content as a starting point that requires verification. This is especially important in research, advising, policy development and other settings where an inaccurate response could affect someone’s education or employment.
Academic Integrity and Assessment
AI complicates traditional assumptions about authorship and independent work. The same tool might be permitted for brainstorming in one course but prohibited during an examination or individual writing assignment.
Institutions need policies that distinguish acceptable assistance from academic misconduct. Faculty also need support redesigning assessments so they measure the intended learning outcomes.
The 2026 HEPI survey found that 65% of participating students believed assessment had changed significantly in response to AI. That finding underscores the need for institutions to examine not only how they detect improper use, but also how assignments can promote analysis, reflection and authentic application. (HEPI, 2026)
Privacy and Data Security
Users may unintentionally share protected student records, employee information, unpublished research or other confidential material with an AI system. Institutions should make it clear which tools have been approved and what types of information users may enter.
The National Institute of Standards and Technology’s 2024 Generative AI Profile identifies risks associated with generative systems and offers actions organizations can consider when managing them. The framework emphasizes ongoing risk management across the AI lifecycle rather than treating review as a one-time technology decision. (NIST, 2024)
Bias and Fairness
AI systems may reproduce or amplify patterns of bias found in their training data or implementation. This is particularly concerning when AI influences decisions involving admission, financial aid, hiring, advising or student intervention.
Before adopting an AI-supported system, leaders should ask whether its performance has been evaluated across relevant populations. Institutions also need a way for people to question or appeal decisions influenced by automated systems.
Equity of Access
Some AI platforms require paid subscriptions, newer devices or reliable broadband access. Requiring students to use a tool without providing equitable access may place certain learners at a disadvantage.
Equity also includes differences in AI literacy. Students and employees who have received training may be better positioned to evaluate AI-generated information and use tools effectively. Institutions should therefore consider access, instruction and support together.
Overreliance and Skill Development
AI can help users complete tasks more quickly, but efficiency is not always the same as learning. If students routinely outsource analysis, writing or problem-solving, they may miss opportunities to develop the skills an assignment was designed to measure.
UNESCO’s 2024 AI Competency Framework for Students emphasizes a human-centered mindset, AI ethics, technical understanding and responsible system design. Its companion framework for teachers similarly emphasizes human agency and appropriate pedagogical use. (UNESCO student framework, 2024) (UNESCO teacher framework, 2024)
These frameworks reinforce an important principle: AI literacy includes knowing when and why to use artificial intelligence—not merely knowing how to generate a response.
What Higher Education Leaders Should Consider
The impact of AI will depend heavily on institutional leadership. Colleges and universities need coordinated decisions that account for academic quality, technology, security, accessibility, ethics and workforce readiness.
A responsible AI strategy should address several questions:
What problem are we trying to solve?
Institutions should begin with a clearly defined educational or operational need. Adopting a tool simply because it includes AI can result in unnecessary costs, fragmented systems and limited value.
Who will be affected?
Students, faculty, staff, technology teams, accessibility professionals and institutional leaders may experience the same system differently. Their perspectives should be included before significant implementation decisions are made.
What data will the system use?
Leaders should understand what information the tool collects, where that information is stored, whether it is used to train a model and how long it is retained.
How will the institution evaluate results?
AI initiatives should have defined measures of success. Depending on the project, these may include learning quality, accessibility, employee workload, student satisfaction, error rates or service response times.
Where is human oversight required?
Institutions should identify decisions that cannot be delegated to an automated system. High-impact decisions affecting academic standing, access to services or employment should include meaningful human review.
How will policies and training stay current?
AI systems and their capabilities change rapidly. Policies should be reviewed regularly, and training should be tailored to the responsibilities of students, faculty, staff and administrators.
This work requires leaders who can evaluate evidence, bring together different stakeholders and guide change without losing sight of institutional values. Those interested in this broader organizational perspective can explore Concordia University Chicago’s PhD/EdD in Leadership with an Organizational Leadership specialization.
A Human-Centered Approach to AI Adoption
Responsible AI adoption is not only a technology initiative. It is an organizational change effort.
A human-centered process asks institutions to understand users’ needs, define the right problem, test potential solutions and revise them based on evidence. These principles are closely related to design thinking, an approach that can help leaders explore new systems without committing to large-scale implementation before their value and risks are understood.
Institutions can begin with limited pilot programs, clear safeguards and defined evaluation criteria. Feedback from students, faculty and staff can then inform whether a tool should be revised, expanded or discontinued.
Preparing Higher Education for What Comes Next
Artificial intelligence will continue to reshape teaching, learning and institutional operations, but its presence does not guarantee better outcomes. The results will depend on the decisions leaders make about policy, training, access, assessment and accountability.
Effective leaders will need to balance innovation with careful oversight. They must be willing to test new ideas while protecting privacy, promoting equity and preserving the human relationships at the center of education.
Learn more about how Concordia University Chicago is adapting to the age of AI and technology and preparing students to lead through continued technological change.
