Can algorithm-based feedback help students to write better? A meta-analysis exploring surface- and deep-level outcomes

Against the backdrop of rapid developments of algorithm-based feedback tools — from older tools mainly providing feedback on grammar and spelling to advanced tools based on generative artificial intelligence offering more comprehensive writing support — our meta-analysis examines to what extent algorithm-based feedback improves not only surface- (e.g., grammar and spelling) but also deep-level (e.g., structure, content, coherence) writing outcomes for different learners at secondary school and university. We reviewed experimental and quasi-experimental studies published between 2011 and the end of 2024, covering five European languages. Results from the 33 included studies indicated that algorithm-based feedback was beneficial for improving writing in general ( g = 0.36). Specifically, positive effects were observed for surface-level outcomes at posttest ( g = 0.31), though no lasting effects were found at maintenance ( g = −0.02). In contrast, deep-level writing outcomes showed sustained improvement, with positive effects both at posttest ( g = 0.31) and maintenance ( g = 0.54). No significant differences between secondary and university students were observed. However, L2 learners, in general, seemed to profit most from algorithm-based feedback, showing gains in surface- ( g = 0.77, bordering on significance), and deep-level outcomes ( g = 0.46). While no significant differences were found between the effects of specific types of algorithm-based feedback tools, feedback from Grammarly and Pigai statistically enhanced students’ writing, but effects of ChatGPT feedback were non-significant. We discuss implications for future research and educational practice, also in light of the small transfer of learning to new writing tasks. • Small effects for surface- and deep-level outcomes at posttest. • L2 learners particularly benefit. • No effect for surface-level outcomes at maintenance. • No significant difference between tools. • Small transfer effect to new writing tasks.

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Publication Details

Journal
Assessing Writing
Published
2026-04-01
DOI
https://doi.org/10.1016/j.asw.2026.101034
Primary Topic
Writing and Handwriting Education
Type
article
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article

Can algorithm-based feedback help students to write better? A meta-analysis exploring surface- and deep-level outcomes

Sina Scherer, Vera Busse, Steve Graham
Assessing Writing
Writing and Handwriting Education
article

Can algorithm-based feedback help students to write better? A meta-analysis exploring surface- and deep-level outcomes

Sina Scherer, Vera Busse, Steve Graham
article en

Abstract

Against the backdrop of rapid developments of algorithm-based feedback tools — from older tools mainly providing feedback on grammar and spelling to advanced tools based on generative artificial intelligence offering more comprehensive writing support — our meta-analysis examines to what extent algorithm-based feedback improves not only surface- (e.g., grammar and spelling) but also deep-level (e.g., structure, content, coherence) writing outcomes for different learners at secondary school and university. We reviewed experimental and quasi-experimental studies published between 2011 and the end of 2024, covering five European languages. Results from the 33 included studies indicated that algorithm-based feedback was beneficial for improving writing in general ( g = 0.36). Specifically, positive effects were observed for surface-level outcomes at posttest ( g = 0.31), though no lasting effects were found at maintenance ( g = −0.02). In contrast, deep-level writing outcomes showed sustained improvement, with positive effects both at posttest ( g = 0.31) and maintenance ( g = 0.54). No significant differences between secondary and university students were observed. However, L2 learners, in general, seemed to profit most from algorithm-based feedback, showing gains in surface- ( g = 0.77, bordering on significance), and deep-level outcomes ( g = 0.46). While no significant differences were found between the effects of specific types of algorithm-based feedback tools, feedback from Grammarly and Pigai statistically enhanced students’ writing, but effects of ChatGPT feedback were non-significant. We discuss implications for future research and educational practice, also in light of the small transfer of learning to new writing tasks. • Small effects for surface- and deep-level outcomes at posttest. • L2 learners particularly benefit. • No effect for surface-level outcomes at maintenance. • No significant difference between tools. • Small transfer effect to new writing tasks.

Assessing WritingVol. 68
University of Münster (DE), Arizona State University (US), FH Münster University of Applied Sciences (DE)
Quality Education
Openalex Percentile: Top 3%
Writing and Handwriting Education
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