Table 1_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.docx

Of these, 378 were included and 57 excluded. The individual report (publication) was the unit of analysis. Among the included reports, 99 were Chinese-language (26.2%) and 279 English-language (73.8%).

Classical machine learning/data mining, deep learning/neural models, and learning analytics/statistical modeling together accounted for 68.3% of the evidence base. Assessment-use impact was concentrated in decision-support applications: 45 reports (11.9%) were Low impact, 323 (85.4%) Medium impact, and 10 (2.6%) High impact. LLM/generative AI accounted for six reports (1.6%).

A focused technical subset of 15 High-impact and/or LLM/generative AI reports underwent task-specific performance synthesis and seven-domain technical validation appraisal. Eleven reports contained sufficient evidence for scoring (three Substantial, six Moderate, and two Limited), while four remained source-limited and unscored. Several reports documented strong task-specific performance or human–machine agreement, but no independent external validation was identified in the available evidence for the focused subset.

Overall, the literature is dominated by Medium-impact diagnostic and decision-support uses. Task-level performance, internal validation, and cross-context generalizability represent distinct layers of evidence, supporting a use-sensitive approach to assessment-use calibration.</p>