This paper argues that the core problem extends beyond academic dishonesty to a deeper misalignment between assessment practices and the learning outcomes they are intended to measure, and highlights the need for alternative assessment models that emphasize process over product.
Abstract
The rapid adoption of artificial intelligence (AI), particularly large language models (LLMs), has fundamentally disrupted how learning is demonstrated and evaluated in higher education. Tasks that once served as proxies for understanding-such as writing essays, solving problem sets, or producing computer code-can now be generated superficially by AI systems with minimal human effort. This paradigm shift raises a critical ethical question: how should learning be evaluated when traditional indicators of competence are easily outsourced? This paper examines the ethical challenges of educational evaluation in the age of AI from a university-level perspective. We argue that the core problem extends beyond academic dishonesty to a deeper misalignment between assessment practices and the learning outcomes they are intended to measure. Evaluation regimes that rely on artificial constraints risk measuring compliance, access, or concealment rather than genuine understanding, reasoning, or judgment. By analyzing institutional responses and presenting empirical survey data, we highlight the need for alternative assessment models that emphasize process over product. The goal is to establish ethically informed assessment strategies that preserve student agency and accountability in an automated age.
This article re-examines the role of assessment within the rapidly evolving landscape of artificial intelligence (AI), focusing specifically on differentiated instruction, and highlights the potential of AI to not only streamline the assessment process but also cultivate a more equitable and student-centered learning environment.
H. Trinh· VNU Journal of Foreign Studi...· 0 citations
Qualitative content analysis on a purposive corpus of 46 documents shows that AI is strongest in codifiable and routine tasks such as basic educational content generation, objective grading, literature processing, drafting, and administrative support.
Amir Ghorbani, M. Blankesteijn· Foresight and STI Governance· 0 citations
Online assessment offers flexibility, scalability and rapid feedback, yet generative artificial intelligence (GenAI) has weakened the assumption that a submitted product reliably represents a student’s independent learning. This critical essay argues that higher education should shift from product-centred assessment to a process-plus-performance model that makes reasoning, evidence selection, revision, feedback use, ethical judgement and intellectual ownership visible. Drawing on process-writing theory, constructivism, constructive alignment, assessment for learning, self-regulated learning, the Community of Inquiry, authentic assessment and Universal Design for Learning, it examines the limitations of take-home essays, remote multiple-choice tests, AI detection and surveillance. It proposes staged assignments, critical evaluation and transparent disclosure of AI use, authentic and localised tasks, reasoned multiple-choice questions, brief oral verification and targeted secure assessment at key progression points. Because process records can also be fabricated, trustworthy judgement should triangulate written development with live explanation or practical performance. This layered approach supports academic integrity without treating surveillance as the default, while strengthening critical thinking, AI literacy, inclusion and professional accountability.
S. Munjita, Phebby Mwangala Kasimba· Journal of Digital Pedagogy· 0 citations
The study proposes the incorporation of “epistemic hygiene,” understood as the systematic development of competencies in critical judgment, comparison, and validation of knowledge generated by algorithmic systems.
A new measure of curricular exposure to large language models is constructed by combining task-level estimates of LLM capabilities with course descriptions from more than 1,000 U.S. colleges and universities, showing that colleges have recognized the instructional challenge posed by generative AI but have made limited observable changes to how student learning is assessed.
JacobLight, David Autor, Nick Bloom et al.· 0 citations
Abstract: Education has not been left behind in the face of change; as new technologies have significantly transformed the educational landscape. The integration of digital technology into teaching practices has opened the door to more diverse learning methods, greater access to resources, and more flexible and personalized instruction, creating a forum for discussing how to leverage artificial intelligence as a catalyst to enrich the educational experience. Assessment, as an essential component of the teaching-learning process, often remains on the sidelines of these developments. It is our responsibility to carefully plan its design, administration, grading, and analysis in order to fully leverage its central role in language learning and minimize its well-documented biases. This article analyzes whether automated assessment-and more specifically, assessment using AI-can correct these biases or whether, on the contrary, it risks introducing new ones. The study is based on an experiment conducted with 90 Higher Education Cycle (Semester 1), whose exams were graded by two teachers and an AI grader. Significant discrepancies emerged. While the AI reduces some of these biases and brings standardization and consistency to grading, algorithmic biases and a lack of consideration for qualitative dimensions also emerged. Consequently, a hybrid approach combining AI and human teachers is recommended as a balanced solution.
Keywords : Educational assessment; Artificial intelligence ; Assessment bias ; ChatGPT5
