Top 10 Best Automated Essay Scoring Software of 2026

Turnitin supports automated essay scoring workflows that connect writing analytics with rubric-aligned feedback and annotation tools for instructors managing repeated grading cycles. It is also built around text-based submission handling and similarity reporting so the same workflow can address both scoreable writing outcomes and originality checks for submitted documents. The submission-to-rubric setup helps standardize evaluation criteria across sections and terms.

A key tradeoff is that writing feedback and scoring depend on the assignment setup and the kinds of rubric criteria defined for the activity. Schools that need scoring across highly diverse formats or workflows outside text submission may find that the automated feedback experience is less consistent than manual grading practices. Turnitin fits best when assessments require repeatable grading support paired with document comparison in a classroom or institution-wide setting.

iThenticate stands out as a dedicated academic similarity and originality checking tool with essay-focused workflows rather than a general-purpose scoring engine. It identifies text overlap and can generate similarity reports that instructors and academic reviewers use as a proxy for originality and writing integrity.

Core capabilities include source matching, exclusion controls, and report outputs meant for submission review in education settings. It does not provide automated essay scoring with rubric-based grades as a primary function.

Gradescope supports automated essay scoring only when essay prompts and scoring criteria can be mapped into rubric elements, which then drive structured scoring rather than fully free-form judgments. Rubric-based workflows can route grading into consistent, repeatable decisions by requiring graders to evaluate the same criteria across responses. This fits institutions that already use rubrics for written communication assessment and need faster scoring across large enrollments.

A key tradeoff is that prompts and rubrics must be designed and maintained so the rubric criteria align with how graders interpret writing quality. When prompt wording changes or rubric criteria do not capture the main differences in student responses, automated scoring output can require additional grader review. A common usage situation is end-of-unit writing assessments where large numbers of essays must be scored quickly for timely feedback, while graders still need annotations and question-level evidence tied to rubric criteria.

Elicit stands out for turning writing assessment workflows into query-driven research and rubric generation using model-assisted prompts. It supports structured extraction and workflow planning around writing prompts, evaluation criteria, and evidence selection.

Teams can operationalize repeated scoring tasks by combining saved queries, consistent criteria, and exportable outputs for review. It is strongest when assessment needs benefit from evidence-grounded reasoning rather than purely local scoring models.

Grammarly for Education stands out with AI writing feedback that maps directly to grammar, clarity, and style improvement goals. It supports automated essay assessment through rubric-aligned suggestions, plus writing-quality scoring signals embedded in editor feedback.

For instructors, it streamlines review workflows by highlighting issues and guiding revisions rather than only returning a numeric grade. Its automated scoring is strongest for writing mechanics and communicative effectiveness, not for deep content understanding.

Knewton’s approach to automated assessment centers on learning analytics scoring from adaptive learning data rather than essay-only rubric engines. The system uses item-level interactions to estimate learner mastery and feed scoring and recommendations for educational content. Essay scoring is supported through analytics workflows that connect student writing performance to underlying skill models and learning objectives.

Criterion’s distinct advantage is essay scoring paired with writing feedback that targets student revisions rather than only assigning a grade. Core capabilities include automated rubric-based scoring, detailed diagnostic feedback, and educator workflows for reviewing and calibrating assessments. The tool also supports instructional use cases such as formative writing checks and writing analytics across assignments and classes.

WriteToLearn stands out by turning essay writing assignments into guided, criteria-aligned scoring feedback. It focuses on automated rubric-style evaluation and actionable comments tied to student writing needs.

The workflow emphasizes revision cycles, with feedback intended to help students improve their next submission. Core capabilities center on evaluating writing quality against set expectations rather than providing a full learning management suite.

EssayGrader stands out for its focus on automated essay scoring with feedback aligned to writing criteria. It generates numeric evaluations plus written comments intended to guide revision. The core workflow supports assignment-style grading for educators or learning programs that need consistent rubric-like judgments.

Scribbr distinguishes itself with writing-centric feedback built around academic writing support rather than standalone automated essay scoring. The platform provides tools for checking structure, clarity, and citation quality, and it supports teacher-style feedback workflows. Automated scoring outputs are not the core product, so rubric-based grading accuracy and consistency depend on how educators frame criteria in Scribbr’s writing guidance features.