Research Summary: AI-supported assessment in online learning: a Chinese- and English-language systematic scoping review and evidence map of computational approaches, assessment-use impact, and technical validation

A systematic scoping review and evidence map examined the application of Artificial Intelligence (AI) in online learning assessments across both Chinese- and English-language publications. The review focused on computational approaches, the impact of AI on assessment use, and technical validation. It highlights that while AI can support feedback, diagnosis, prediction, intervention, and scoring, its technical performance does not inherently define how AI-generated outputs should be interpreted or utilized as assessment evidence.

Why it matters

This review underscores the increasing integration of AI into educational assessment systems, particularly in online learning environments. It is critical for organizations to understand the current state of AI application in this domain, acknowledging that technical performance alone is insufficient for establishing the evidentiary value or interpretability of AI-generated assessments.

Key insights

• AI is actively employed in online learning to facilitate various assessment functions, including providing feedback, diagnosing learning gaps, predicting performance, intervening with support, and automated scoring.
• The review analyzed computational approaches, the impact of AI on assessment utilization, and the evidence base for technical validation.
• The scope of the review included both Chinese- and English-language reports published between January 1, 2020, and May 31, 2026.
• A comprehensive search strategy was employed, covering 13 structured information sources and supplemented by additional retrieval methods, yielding a substantial number of records before filtering.

Source

Frontiers in Education — https://www.frontiersin.org/articles/10.3389/feduc.2026.1921095