Generative AI has intensified an existing assessment challenge because many high-stakes tasks measure polished final products more than the learning processes behind them. When students can use AI systems to draft essays, generate code, summarize texts, or solve problems quickly, traditional output-only assessment becomes less reliable as evidence of student learning.
The article argues that the main problem is not simply the availability of generative AI, but the weak alignment between intended learning outcomes and the evidence collected through assessment. If assessment tasks do not require students to show reasoning, disciplinary judgment, and ownership of decisions, then academic integrity becomes difficult to maintain whether AI is banned or allowed.
Academic integrity is presented as a system-level issue rather than only an individual moral issue. The article draws on research showing that misconduct is influenced by opportunity structures, assessment conditions, and institutional culture. Generic, under-scaffolded, final-product assessments increase the risk of plagiarism, contract cheating, and inappropriate AI use.
The article also emphasizes that integrity improves when assessment tasks are contextualized, include formative feedback, and require students to make personal judgments over time. These features align with authentic assessment, which values meaningful performance tasks embedded in disciplinary practices.
In AI-infused learning environments, authentic assessment must clarify the role of AI within disciplinary work. Students need transparent guidance about what AI may support, what must remain their own work, and how their process and decisions will be evaluated.
The article critiques detection-first institutional responses. AI detection tools may have technical limitations, may produce false positives, and may create inequities, particularly for multilingual students or students using accessibility tools. A surveillance-focused approach may also weaken trust and distract from learning.
Instead of relying primarily on detection, the article proposes redesigning assessment so that the most important evidence is difficult to outsource and easy to verify. This approach is linked to constructive alignment because assessment strongly shapes student learning behavior.
The introduction establishes the article’s purpose: to develop an evidence-centered assessment design framework and a rubric toolkit for academic integrity in AI-infused environments. The proposed package supports different institutional AI-use regimes, including prohibition, constrained permission, and encouraged transparent use, while keeping the central focus on credible evidence of student learning.