Artificial intelligence is no longer a prospect for higher education or a specialized research topic discussed only in computer science labs and seminars. It has arrived in our classrooms, advising offices and administrative suites. AI platforms can function as study companions, individualized tutors, advisors and even graders. Institutions are piloting predictive analytics to anticipate student success, immersive platforms to transform engagement and back-office applications to streamline everything from scheduling to compliance and purchasing, as well as enhancing interactions with students, local communities, industry and government.
The history of higher education shows that moments of technological disruption are also moments of institutional choice. The printing press democratized access to knowledge but also destabilized established authority. The internet enabled universal access to information but created unprecedented challenges around information integrity and trust. AI represents the next great inflection point. Unlike prior technologies that primarily distributed or stored information, AI touches knowledge, the very heart of our educational system. We can program AI platforms to interpret, predict and even simulate judgment. That shift raises questions that cut to the very core of the academic enterprise: Who controls knowledge? Who makes decisions? And most importantly, who bears the responsibility when those decisions shape lives?
The attraction of AI for institutions of higher education (IHEs) is clear. Appropriately enabled, it can individualize learning at scale, removing constraints of prerequisite knowledge, time and place. It provides students with the continuous support that even the most dedicated faculty cannot provide to hundreds of learners at once and can simultaneously create test ecosystems to assess and evaluate pedagogical interventions. It can reduce institutional inefficiencies, enhance effective use of resources and, if implemented appropriately, lower the cost and increase the value of a credential and its relevance to the workplace.
However, the same features that make AI attractive also create risks. Predictive analytics used to identify at-risk students may perpetuate biases in the data and diminish human motivation and drive. Automated grading can strip away the nuance of faculty judgment. Heavy reliance on generative platforms may corrode the critical thinking and creativity that underpin higher education. Still, AI may give students who lack access to good faculty new opportunities for success. The challenge for IHEs is not whether to use AI but how to use it responsibly and ensure an ethical basis for its use.
Academic Integrity: Reframing Learning Output
The fear of students using generative AI to draft essays or solve assignments has triggered increased alarms about cheating and loss of critical thinking. Beyond the parallels to calculators, the reality is more complex. Cheating existed long before AI, and AI merely exposed the weaknesses we’ve known of for decades. Our assessments often measure memorization rather than learning. The deeper question is whether the way we assess students invites over-reliance on such tools. Thus, a more suitable response is not to ban AI but to redesign evaluations emphasizing authentic assessment, problem solving, reflection and creativity—areas where AI may assist and even strengthen student effort but not substitute genuine learning. AI could assist faculty deepen assessment and design tasks more aligned with determining competencies, even simulating application of knowledge to workplace contexts.
Courses must therefore emphasize critical thinking, creativity, discovery and the ability to interrogate AI outputs. Assignments should invite multiple approaches, encourage reflection and assess not only correctness but reasoning. Faculty and administrators must also model consistency. Declaring that student use of AI is unethical while quietly relying on it to prepare lectures, draft communications, create strategic plans, grade assignments and make decisions sends the wrong message. Integrity must begin with transparency and alignment to the institutional mission at all levels.
The AI Divide
The most profound ethical concern relates to a widening of existing divides. AI threatens to magnify deficiencies into chasms difficult to bridge. Students who grow up experimenting with AI platforms will enter institutions of higher education with advantages, not only completing tasks more efficiently but also understanding how to frame questions, analyze results, synthesize information and challenge algorithmic output. Those without access risk exclusion from the very literacy and fluency with technological tools that the future workforce will demand. The divide also extends across institutions. Where access to digital infrastructure is weak, we must create connectivity and share (appropriately managed) resources and computing power to avoid the very gaps seen during the COVID pandemic. IHEs must therefore see AI not as an optional add-on but as a new dimension of access and assurance of career success. Assuring device affordability and connectivity, providing institutionally supported platforms and embedding AI literacy into curricula are obligations higher education must fulfill.
We must remember that the ethical implications of AI look different based on perspective. For students, the issues revolve around fairness, personalization and privacy. They want assurances that predictive tools will not unfairly label them or restrict their opportunities. They want access to AI as an enabler, not as a gatekeeper, and deserve to know how data is stored, used and protected, and how faculty and the administration use it to make decisions. For faculty, challenges include professional autonomy and trust. AI tools can provide powerful assistance but, when grading or instructional design becomes too automated, faculty risk losing the very agency that defines their expertise. Professional development and support must be provided to ensure faculty are not reduced to machine supervisors but remain the central architects of learning. For administrators, the concerns are systemic—adoption and maintenance costs, the risk of vendor dependency and the responsibility of crafting policies that balance innovation with accountability. Without careful planning, adoption could deepen inequities, erode trust and expose institutions to legal and ethical vulnerabilities. While each group sees different risks and benefits, the institution must find common ground guided by values rather than expediency.
Beyond Black Boxes: Transparency and Accountability
A recurring theme across all perspectives is the problem of the black box. AI systems are often opaque, drawing conclusions based on algorithms and patterns invisible to users. This lack of traceability and explainability undermines trust. If a student is flagged as unlikely to succeed, on what basis was that judgment made? If administrators use AI for admissions and funding-related decisions for different disciplines, what factors drive the decisions? Transparency is non-negotiable. IHEs must insist on tools that provide explainability, traceability and accountability, which requires not only means understanding the algorithms themselves but clarifying the chain of responsibility.
To integrate AI responsibly, IHEs must establish policies that provide coherence while allowing for flexibility and innovation. At the broadest level, institutions need overarching guidelines that articulate the mission, values and principles that frame AI use. These guidelines act as guardrails, ensuring innovation remains aligned with commitment to fairness, transparency, privacy and accountability. Without such clarity, adoption risks fragmentation, inconsistent implementation and uneven enforcement that could undermine trust and create confusion about acceptable use. At the next level, disciplinary context must shape practices around AI. Each field has unique norms, pedagogy and vulnerabilities, and we must adapt policies accordingly. This middle layer ensures the nuances of individual disciplines are respected while remaining tethered to institutional principles. Finally, policies must ensure faculty retain flexibility to adapt, experiment and innovate, tailoring assignments and approaches to their pedagogical goals and the needs of the professions for which students are preparing. Overly prescriptive rules risk stifling creativity and discouraging faculty from exploring AI’s potential. In contrast, policies that offer guardrails while affirming professional judgment enable faculty to remain central to the learning process.
Taken together, these layers—institutional, disciplinary and individual—provide balance, avoiding extremes of unchecked decentralization while promoting consistency, accountability and innovation in a rapidly changing educational landscape.
The Ethical Imperative
At the heart of AI adoption in higher education lies an ethical imperative that cannot be ignored. Every algorithm, predictive tool and automated decision carry consequences for students, faculty and society that reach beyond efficiency gains or cost savings.
Fairness requires that AI tools not replicate or deepen bias. Personal and academic student, staff and faculty data are as sensitive as medical or financial information, and institutions must safeguard them with equal rigor. The preservation of human agency is equally, if not more, important. AI can be of tremendous assistance in decision making, allowing us to simultaneously analyze vast amounts of data and consider multiple scenarios, but responsibility must remain with people, not machines. When errors occur, and they inevitably will, accountability cannot be outsourced to a black box. Students, faculty and administrators deserve to not only know that AI is being used but how it operates, what data it draws upon and how it reaches decisions.
While issues related to hallucinations are being addressed, what is not as easily recognized are the hidden nuances of indeterminism, or the tendency of AI platforms to provide slightly or vastly different answers to identical questions based on how the system groups and processes user requests in batches and even based on changes in token weights based on recent use. The potential for different grades for identical answers or almost random selection of one student application over another are risks that cannot be cast aside. Finally, universities must demand robust systems that are reliable, resilient and resistant to manipulation. These principles are not roadblocks to innovation but the foundation on which sustainable and trustworthy adoption must rest.
Critical Success Factors
To integrate AI ethically and effectively, institutions must look beyond the technology itself. Leadership is central. Presidents, provosts, vice presidents and deans must do more than authorize AI adoption. They must model transparency and ensure institutional values guide practice. Cultural readiness is equally vital, requiring willingness to challenge long-held assumptions about teaching, operations and administration. Professional development, always a critical aspect in change management and successful transformations, takes on a new level of importance with AI. Absent the investment of time and resources to enable faculty and staff training and support, as well as the space to experiment, adoption will remain fragmented, uneven and inequitable.
Moreover, AI adoption must be subject to constant reflection, assessment and feedback, balancing fairness, privacy, agency and trust—aspects that often do not exist even with current non-AI-based systems. IHEs cannot treat implementation as a one-time event. Rather, it must be considered as an ongoing evaluation, adjustment and improvement process. Such a process includes examining not just efficiency and effectiveness of use and the validity of decisions reached through AI use but also fairness and the lived experiences of students and faculty. Iterative goals and regular assessment must confirm that AI truly enhances engagement, support and opportunity. Otherwise, it fails to meet the purpose of higher education. In the end, the critical success factors are less about technology than about people. It is leadership, culture, collaboration and an unwavering commitment to learners that will determine whether AI transforms IHEs for the better or compounds the challenges that they already face.
Conclusion: Choosing the Path
We must remember that AI is a tool—powerful, complex and still evolving. It reflects the choices we make about where and how to deploy it. If used carelessly, it could entrench inequities, erode skills and further undermine trust. If used wisely and with purpose, it could expand opportunity and individualized learning, decrease costs while increasing scale and impact of effort, and enhance the human dimensions of education. The future of higher education will not be determined by the capabilities of AI itself but by the values we embed in its use. Ethics must therefore not be an afterthought but the foundation. We must build systems that are transparent, traceable and accountable. The question is not whether AI will begin to shape higher education. It already has! The real question is whether we will shape AI in ways that align with our mission, our values and our responsibility to society. That path is ours to choose.
__________________________________________________________________________
Vistasp M. Karbhari is a Professor in the Departments of Civil Engineering, and Mechanical & Aerospace Engineering at the University of Texas at Arlington, where he served as president from 2013–2020. He is a Fellow and Board Member of Complete College America and Co-Chair of CCA’s AI Council and can be followed on LinkedIn.
Karen Vignare is Vice President for Digital Transformation for Student Success at the Association of Public and Land Grant Universities. Her role is to work with large public institutions to leverage technology to improve effectiveness, efficiency and improvement in student success efforts. She co-chairs CCA’s AI Council. Connect with Karen on LinkedIn.
You Might Also Like
