AI-Assisted Grading in English Language Education: A Systematic Review of Reliability, Bias, and Ethical Implications
Keywords:
AI-assisted grading, automated essay scoring, English language learners, assessment bias, rubric design, human-AI collaborationAbstract
This systematic review synthesises peer-reviewed and emerging evidence (2011–2026, N = 27 studies, including a small number of preprints, working papers, and unpublished manuscripts) on the reliability, bias, and ethical implications of AI-assisted grading in English language education, applying a PRISMA-aligned thematic narrative synthesis. In contrast to reviews that treat these themes separately, it examines reliability, bias, stakeholder perceptions, and governance together, with rubric operationalisation and prompting conditions as cross-cutting explanatory factors. Findings challenge prevailing optimism surrounding AI assessment: reliability is not a fixed property of AI technology but a construct of rubric operationalisation, with studies employing prescriptive, criterion-anchored rubrics reporting good agreement (ICC = 0.814; Koraishi, 2024) and studies using holistic or domain-inferential rubrics reporting consistently poor agreement (ICC3 = 0.385; Manning et al., 2025; QWK ≈ 0; Gaggioli et al., 2025). Studies report systematic bias disadvantaging ELL and low-SES students (−0.10 z each; Kwako & Ormerod, 2024), with racial/ethnic disparities operating in non-intuitive directions (Yamashita, 2025). Critically, comparisons across studies are confounded by uncontrolled prompting conditions, temperature settings, and rubric design, a form of methodological heterogeneity that is rarely acknowledged as a threat to validity. Preliminary evidence suggests that open-source LLMs show comparable fairness to closed models at significantly lower cost (Oketch et al., 2025), challenging resource-barrier assumptions. The review concludes that AI grading calls for rubric transparency as a precondition, bias auditing as a regulatory standard, and human oversight as a core safeguard for equitable English language assessment. These conclusions rest on a narrative synthesis of heterogeneous studies rather than on direct empirical testing.
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