Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity

Abstract

Abstract Integrating Artificial Intelligence (AI) tools, particularly generative AI (GenAI), in higher education is reshaping assessment practices, presenting both challenges and opportunities. While these tools enhance learning, they also raise concerns about academic integrity and the authenticity of student work. Traditional assessments, such as essays and take-home assignments, are increasingly susceptible to AI-assisted plagiarism, necessitating a re-evaluation of assessment strategies. This systematic review, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, examines educators' challenges in assessing student learning in AI-infused environments. Using Scopus, IEEE Xplore, and ScienceDirect, we identified relevant literature highlighting concerns about originality, critical thinking evaluation, and the quality of student work. Findings underscore the need for AI-resistant, process-based assessments, such as oral exams and multi-stage evaluations, to uphold academic integrity. The study advocates for institutional AI policies and digital literacy programs to promote ethical AI use and mitigate academic misconduct. Additionally, it emphasises a balanced human-AI collaboration in assessments, ensuring that AI enhances rather than replaces student effort. Addressing these challenges can reduce academic misconduct cases, allowing educators to focus on fostering meaningful learning experiences and sustainable educational outcomes.

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