AI evidence extraction

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

Prince Daughin Ngqabutho Ncube, Godwin Pedzisai Dzvapatsva, Courage Matobobo, Memory M Ranga · AI and Ethics · 2025

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

7/10

Relevance

Evidence

Citations

9.64

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1007/s43681-025-00871-w

Methodology & findings

Study design

Systematic review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, utilizing literature search across Scopus, IEEE Xplore, and ScienceDirect databases to examine educators' challenges in assessing student learning in AI-infused environments..

Primary method

Systematic review methodology following PRISMA framework; qualitative synthesis of identified literature on assessment challenges and strategies in AI-infused environments.

Main result

The systematic review identified that "Findings underscore the need for AI-resistant, process-based assessments, such as oral exams and multi-stage evaluations, to uphold academic integrity." The study found that traditional assessments like essays and take-home assignments are increasingly vulnerable to AI-assisted plagiarism, and that educators face significant challenges in assessing the authenticity of student work in AI-infused learning environments.

Reports effect sizes.

Research paradigm

Interpretivist/qualitative synthesis

Author conclusions

The authors conclude that "Addressing these challenges can reduce academic misconduct cases, allowing educators to focus on fostering meaningful learning experiences and sustainable educational outcomes." They emphasize "a balanced human-AI collaboration in assessments, ensuring that AI enhances rather than replaces student effort" and advocate for "institutional AI policies and digital literacy programs to promote ethical AI use and mitigate academic misconduct."

Risk of bias

Database selection bias (only three databases searched may exclude relevant grey literature); Publication bias (peer-reviewed sources only, no grey literature included); Study selection bias (no explicit mention of dual review or inter-rater reliability measures); Selection bias: Potentially limited to papers in specific databases (Scopus, IEEE Xplore, ScienceDirect) and English-language publications; Publication bias: Systematic reviews may be biased toward published studies with significant findings; Quality assessment bias: No explicit quality assessment tool mentioned in the abstract for evaluating included studies; heterogeneity_of_study_designs

Open questions raised

The review identifies needs for:
AI-resistant assessment strategies
institutional AI policies
digital literacy programs
process-based evaluations (oral exams, multi-stage assessments)
and (5) frameworks for ethical AI use in higher education assessment.

Data: not_statedCode: not_statedExtracted from: pdf

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