A Systematic Literature Review on AI-Based Quiz and
Assessment Systems for Adaptive Learning
Islombek Abdurakhmanov1,*, Eugene Q. Castro1
1Department of Computer Science, Central Asian University, Tashkent, Uzbekistan
Emails: 221236@centralasian.uz; e.castro@centralasian.uz
Abstract
AI-based quiz and assessment tools are widely studied for supporting adaptive learning, yet existing work
is distributed across different tasks (e.g., question generation, automatic evaluation, feedback, and conversational
assessment) and often uses inconsistent datasets and metrics, making comparisons difficult. This paper
reports a Systematic Literature Review (SLR) conducted under PRISMA 2020 to summarize approaches and
evaluation practices for AI-based quiz and assessment systems. Searches were performed in IEEE Xplore,
ACM Digital Library, and Google Scholar using keyword combinations related to automated question generation,
assessment, evaluation, and large language models. The search returned Nidentified=57 records; after
duplicate removal, Ndedup=55 records remained for screening. Following title/abstract screening and full-text
eligibility assessment, Nincluded=9 studies were included for qualitative synthesis and structured data extraction.
The reviewed studies show strong attention to transformer/LLM-based question generation, automatic scoring
and evaluation frameworks, and formative feedback generation for learning. However, recurring
limitations include reliability of automated judging, lack of standardized benchmarks, domain transfer
issues, and risks impacting fairness and academic integrity. We conclude with practical recommendations for
stronger evalua-tion design (e.g., shared benchmarks, transparent rubrics, and human-in-the-loop validation)
to improve trust and real-world adoption.