AI evidence extraction

A Semi-Systematic Review of Structural Relations in Teachers’ Use of Generative AI for Assessment Purposes

Kwai Ming Albert Chan, See Ki Ada Tse, Fei Yin Dawn Lo · Journal of Education and Learning · 2026

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

6/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.5539/jel.v15n4p1

Methodology & findings

Study design

Semi-systematic literature review using targeted search strategy across Web of Science, Scopus, and ERIC databases from 2023-2025.

Sample

< 30

Primary method

Qualitative synthesis of literature; specific statistical methods not described in abstract for a scoping review methodology.

Main result

The review found that "none of the reviewed models or frameworks focus specifically on assessment or evaluation; instead, they adopt a holistic approach to GenAI usage and acceptance." Additionally, "the findings reveal a lack of targeted research on assessment-related applications of GenAI, with most studies addressing broader themes such as general adoption," highlighting a significant gap in literature regarding assessment-specific applications of generative AI in higher education.

Reports effect sizes.

Research paradigm

Interpretive/qualitative synthesis of literature

Author conclusions

The authors conclude that "the review underscores the need for future research to develop and evaluate models that explicitly address assessment and evaluation. Closing these gaps is crucial for understanding and maximizing the potential of teachers' use of GenAI in assessment and for determining if a redesign of existing approaches is urgently needed."

Risk of bias

Selection bias: Only 5 studies retained from 53 initial articles suggests restrictive inclusion criteria; Publication bias: Limited to indexed databases (Web of Science, Scopus, ERIC); Potential language bias: No mention of language restrictions in search strategy; Date range limitation: 2023-2025 only, excluding earlier foundational work; Selection bias potential due to strict inclusion criteria reducing 53 articles to 5; publication bias possible given restriction to indexed databases (Web of Science, Scopus, ERIC); language bias not addressed; potential database coverage limitations.; Selection bias from filtering 53 articles to 5 studies; Publication bias inherent in database searches (Web of Science, Scopus, ERIC); Language/geographic bias from database-dependent searches; Time period constraint (2023-2025) may exclude relevant earlier work

Limitations

The review's limitations include that "the review originally aimed to explore structural relations of GenAI adoption in teacher assessment practices" but found that "none of the reviewed models or frameworks focus specifically on assessment or evaluation," which constrains the ability to draw conclusions about assessment-specific structural relations
Additionally, the small final sample of five studies limits the comprehensiveness of findings.

Open questions raised

Lack of targeted research on assessment-related applications of GenAI
Absence of models or frameworks specifically focused on assessment or evaluation
Missing literature on specific relational mechanisms and outcomes of GenAI-focused assessment practices
Need for development and evaluation of models that explicitly address assessment and evaluation
Requirement to determine if redesign of existing approaches is needed
The authors identify the need for: (1) targeted research on assessment-related applications of GenAI; (2) development and evaluation of models explicitly addressing assessment and evaluation; (3) research examining specific relational mechanisms and outcomes of GenAI-focused assessment practices; (4) exploration of whether redesign of existing approaches is urgently needed.

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