Assessment in the age of AI in Higher Education: A Leadership Challenge, Not a Technical One
Introduction: Why AI Is Redefining Educational Leadership
Generative artificial intelligence is rapidly transforming higher education. Students are already using AI tools to write, summarize, and solve problems, while institutions are still adapting their assessment frameworks. This gap has made one thing clear: AI assessment in higher education is not primarily a technical issue. It is a leadership challenge.
For universities and colleges, the question is no longer whether AI will affect assessment, but how institutional leaders can guide this transformation in a way that preserves academic integrity, educational quality, and equity.
AI Assessment in Higher Education: The Dual Anchoring Challenge
Effective assessment in the age of AI requires a delicate balance between two forms of expertise. On the one hand, disciplinary knowledge enables educators and students to judge the relevance, accuracy, and limits of AI-generated content. On the other hand, AI literacy is needed to understand how these systems function, where their biases lie, and how they should be used ethically.
In practice, this balance is rare. Many students have easy access to AI tools but lack the disciplinary depth needed to critically evaluate outputs. At the same time, many educators master their field but feel insufficiently prepared to integrate AI into assessment design. This “dual anchoring” challenge sits at the heart of current tensions around AI assessment in higher education.
What Current AI Use Reveals About Learning
Observed patterns of AI use among students highlight a key risk. AI is often used for efficiency—summarizing texts, generating explanations, or drafting answers—rather than for deeper learning processes such as reflection, self-assessment, or conceptual transfer.
Without strong disciplinary grounding, students may struggle to identify inaccuracies or biases in AI-generated content. In this context, assessment practices become decisive. They can either encourage surface-level performance or foster the development of critical thinking, judgment, and intellectual autonomy.
Assessment, Equity, and Institutional Responsibility
AI has the potential to widen existing educational inequalities. Students who already possess strong academic and digital skills are better positioned to benefit from AI-supported learning. Others may become dependent on AI outputs without truly developing the competencies required for long-term success.
Institutional choices matter. Assessment strategies play a central role in determining whether AI becomes a tool for inclusion or exclusion. Thoughtfully designed assessments can help all students develop both disciplinary reasoning and AI literacy, reinforcing equity rather than undermining it.
Assessment as a Strategic Lever for Change
The rapid diffusion of generative AI has pushed assessment to the forefront of institutional discussions. Increasingly, educators and leaders are questioning whether traditional formats still reflect what institutions value as learning outcomes.
At LeaderTech, we observe that this moment often triggers a broader realization: assessment is not just a measurement tool, but a strategic lever. Rethinking assessment forces institutions to clarify their educational priorities, align teaching methods with desired competencies, and articulate what graduates should be able to do in an AI-rich world.
Ban AI or Integrate It? A Leadership Decision
Two dominant approaches are currently emerging. Some institutions seek to limit or exclude AI from assessment contexts, relying on supervised exams or oral evaluations. While this can protect authenticity in the short term, it risks promoting surface learning and disconnecting assessment from professional realities.
A second approach integrates AI into assessment within a clear institutional framework. This involves defining acceptable uses, setting transparent expectations, addressing ethical considerations, and designing assessments that prioritize analysis, reasoning, and decision-making. This path requires investment in staff development and institutional coordination, but it better reflects the skills graduates will need beyond university.
Choosing between these approaches is ultimately a leadership decision, not a technological one.
Returning to Educational Fundamentals in the Age of AI
Sustainable AI assessment in higher education starts with fundamental questions. Why do we assess? Which competencies must students develop independently? Which tasks can be supported—but not replaced—by AI?
Rather than adopting tools reactively, institutions benefit from clarifying their educational mission. This includes identifying human competencies that remain essential, understanding the real capabilities and limits of AI within each discipline, and ensuring coherence between learning objectives, teaching practices, and assessment methods.
Conclusion: Leading Assessment Transformation with Purpose
Generative AI challenges established assessment practices, but it also creates an opportunity for institutional leadership. The future of AI assessment in higher education will not be shaped by tools alone, but by the strategic choices made by educational leaders.
By aligning disciplinary expertise, AI literacy, and assessment design, institutions can preserve education as a space for critical thinking, ethical judgment, and human development. At LeaderTech, we see AI not as a disruption to be managed, but as a catalyst for purposeful educational transformation—one that places leadership, coherence, and learning quality at its core.
Source: Caneva, C. (2025). Assessing in the Age of AI: The Paradox of Dual Anchoring. Revue internationale sur le numérique en éducation et communication, 22. https://doi.org/10.52358/mm.vi22.495