McHenry County College

Artificial intelligence is presented as a technological conundrum. Institutions ask which systems to purchase, what data may be entered, how outputs should be verified and which uses should be permitted.

These questions matter, but they do not reach the center of the issue.

AI is fundamentally an anthropological conundrum. It forces us to reconsider what it means to learn, create, communicate, decide and participate in community. It raises questions about authority, trust, identity, responsibility and human judgment.

Responsible AI governance in higher education cannot be achieved through technology or contained within policy. It requires dialogue among everyone who uses AI or is affected by it.

The solution is not becoming more automated. It is becoming more intentionally human.

Policy Cannot Carry the Entire Burden

Colleges and universities have responded to generative AI with policies, guidance and academic integrity standards. These boundaries matter, but they cannot anticipate all uses.

Faculty must determine whether AI supports learning or enables students to bypass inquiry, struggle, reflection and discovery. Students must distinguish between a learning partner and a substitute for meaningful engagement. Advisors may use AI to improve communication while protecting private information. Staff may use automation without understanding its recommendations, while administrators may rely on decisions without knowing whose data or assumptions shaped them.

No policy can resolve every situation because each involves context, judgment and human consequence. Guidance that is too restrictive can discourage experimentation or drive use underground; guidance that is too broad leaves individuals to navigate privacy, bias, accuracy, accessibility, learning and accountability alone.

Policy establishes the conditions for responsible practice. Dialogue makes those principles meaningful.

Governance Must Include All Users

AI governance is often assigned to administrators, attorneys, technology leaders or policy specialists. Their expertise matters, but governance is incomplete when it excludes those closest to the work.

“Responsible AI governance in higher education cannot be achieved through technology or contained within policy. It requires dialogue among everyone who uses AI or is affected by it."

Faculty understand teaching and learning. Students understand how technology affects engagement, curiosity, agency and participation. Staff understand the systems, relationships and workflows sustaining operations. Technology professionals understand infrastructure, security, data and system limitations. Researchers and assessment professionals contribute evidence, while community and industry partners reveal expectations beyond the institution.

These perspectives are forms of institutional intelligence.

Inclusive dialogue improves decisions by surfacing questions reviews may miss: Who benefits? Who assumes the risk when a system fails? Whose knowledge is represented or missing? Can people challenge an AI-supported decision? Does the technology strengthen human agency and learning or merely accelerate output?

Institutions must create spaces where these questions are asked openly and repeatedly.

Human-in-the-Loop Must Mean Human Authority

“Human-in-the-loop” too often means a person merely approves an output. Meaningful involvement requires the authority, knowledge and time to question results, understand limitations, consider context and reach a different conclusion.

Human judgment must remain visible when AI touches admissions, financial aid, advising, employee evaluation, accessibility, assessment or student support. AI can organize information, identify patterns, summarize documents, reduce repetitive work and support decisions. It cannot assume human accountability.

Institutions remain responsible for decisions made through their systems, even when automated.

The Learning Process Must Remain Central

The central educational question is not whether AI helps students produce better answers, but whether it strengthens or diminishes learning.

Learning includes uncertainty, curiosity, experimentation, revision, productive struggle, dialogue, reflection and judgment. These are not inefficiencies to automate away; they are conditions through which understanding develops.

AI can help students explore ideas, receive feedback, compare perspectives and overcome barriers. It can also enable them to bypass the experiences education is meant to cultivate. Software cannot determine that distinction alone. It requires dialogue about the purpose of learning and AI’s proper role.

A Human-to-Human Test for AI

Institutions need a value proposition for evaluating AI use.

When AI creates opportunities for meaningful human-to-human interaction, it should be explored. It may reduce administrative burdens so advisors spend more time with students, help faculty identify where learners need support or improve access to community.

When AI decreases human interaction, it should be heavily scrutinized. Efficiency alone does not justify replacing conversation, mentorship, collaboration or care. Institutions must ask what is lost when a relationship becomes an automated response.

Not every reduction in interaction is harmful; automating repetitive tasks may create time for deeper engagement. Likewise, communication technology does not guarantee stronger relationships. Neither outcome is fixed. Consequences depend on how AI is designed, implemented and governed.

Each choice is an inflection point—an opportunity to keep people at the center of learning, work and community.

The Solution is More Human

AI does not reduce the importance of dialogue, curiosity, empathy and collective judgment. It makes them more necessary.

Higher education should treat governance as an ongoing human practice by creating forums for emerging uses, sharing successes and failures, documenting pilots and establishing channels for questioning harmful applications. Professional learning must extend beyond technical training to consider what AI means for work, relationships and education.

The central question is not simply, “What can this technology do?” It is, “What should people be able to do, decide, understand and remain responsible for when this technology is present?”

Policy is a starting point. Responsible governance emerges through the conversations that follow.

The more technological our institutions become, the more deliberately human they must remain.