3.5. Ethics in Higher Education Contexts
Jace Hargis
AI is reshaping higher education at every level, from teaching and learning to admissions and administration. But with these opportunities come ethical dilemmas: how should faculty balance innovation with integrity, how can institutions protect student privacy, and what happens when AI challenges traditional academic values? This chapter examines the unique ethical questions AI raises in higher education, drawing on real-world policies and resources.
Academic Integrity
Perhaps the most visible concern in universities is academic integrity. Tools like ChatGPT can generate essays, solve problem sets, or write code. Should students be allowed to use them? If so, under what conditions? Institutions are taking varied approaches:
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- Harvard Generative AI Guidelines: emphasize transparency with students, academic honesty, and clear expectations in syllabi.
- Cornell AI in Research Guidelines: recommends clarifying acceptable uses while safeguarding fairness.
- TWU AI Policies for Syllabi: language for including AI policies on syllabi
Ethical questions of plagiarism, authorship, and fairness are not new, but AI tools make them more complex.
Equity and Access
AI can widen or narrow the equity gap in higher education. For example:
- Accessibility: AI-driven transcription and translation tools can improve access for students with disabilities or non-native speakers.
- Affordability: Not all students can access paid AI tools, raising fairness concerns.
- Bias: AI detectors may unfairly flag non-native speakers’ writing as “AI-generated,” risking inequity and harm.
Privacy and Data Use
Universities collect vast amounts of student data, from grades to learning analytics. When paired with AI, this raises new risks:
- How much student data should be shared with third-party AI platforms?
- Should faculty use AI tools that store student writing on external servers?
- How do FERPA and other privacy laws apply in the AI era (U.S. Department of Education on Student Privacy)?
Privacy is not just a legal issue but a trust issue: students need assurance their personal data is respected and protected.
Faculty Responsibilities
Faculty occupy a dual role as teachers and role models. How they use AI shapes student expectations. Responsible practice includes:
- Modeling critical, ethical use of AI in teaching.
- Clarifying policies in course syllabi.
- Encouraging creativity, reasoning, and empathy as distinctly human capacities that AI cannot replace.
📖 Analogy: The Honor Code (click to expand)
Most universities have an honor code—an agreement that students and faculty will uphold shared values of honesty and integrity. AI is like a new student arriving on campus: brilliant, fast, but also unpredictable. Without guidance, it may unintentionally disrupt the community’s trust. With clear expectations, however, it can become a valuable collaborator. Just as honor codes balance freedom with accountability, AI policies in higher education must balance innovation with integrity.
📚 Weekly Reflection Journal
If you were writing a syllabus for next semester, how would you address AI use? Would you allow it, limit it, or prohibit it? Why?
Quick Self-Check
Flip the cards to explore common higher education dilemmas and the ethical principles they raise.
Looking Ahead
Higher education presents unique ethical challenges, but also opportunities for leadership. In the next chapter, we will broaden the lens to society-wide issues of AI, ethics, and governance.
