Create courses, upload rubrics, and receive consistent, performance-based assessments — saving time while upholding academic standards.
Supporting organisations committed to responsible AI
How it looks
Detailed performance band analysis with evidence-based feedback
Auto-Marking Review
Rubric-aligned evaluation workspace
Evaluation Results
Research Methods Report
SOC204 · Applied Research Methods
Final score
82.5%
Distinction
Your Submission
This study used a mixed-methods approach to examine how peer feedback affects student confidence during group projects. Survey responses were compared with interview themes to identify patterns across both data sets.
Overall Feedback
A well-structured report with a clear methodology and a strong connection between the research question and the findings. The analysis is persuasive and supported by relevant sources.
Strengths
- Clear research question
- Relevant supporting evidence
- Logical report structure
Areas for Improvement
- Explain the sampling limitations in more detail
- Connect the final finding to the wider literature
Feedback by criterion
3 criteria reviewedAnalysis & Argument
DistinctionThe argument is coherent and the findings are interpreted rather than simply described.
Evidence & Referencing
MeritSources are relevant and used well, with a little more critical comparison needed.
Structure & Clarity
MeritThe report is easy to follow and would benefit from a sharper limitations section.
Getting Started
How to Use the Auto-Marking System
Four simple steps to evaluate assignments accurately and consistently.
Courses / New Course
Create New Course
Live previewSet up a course and upload its outline.
Course Details
Organization
Course Code
Course Title
Level
Credits
Course Outline
PDF or DOCX · optional
Create Course
Create your course and upload the course outline. Learning outcomes are automatically extracted and mapped.
Courses / MAMC01805 / New Assignment
Create New Assignment
Live previewProvide assignment information and upload a rubric.
Assignment Details & Rubric
Creating an assignment for MAMC01805
Assignment Title
Weighting
Word range
Rubric Source
Rubric PDF/DOCX
Performance bands detected · 100 total points
- Analysis & Argument
- Evidence & Referencing
- Structure & Clarity
Upload Rubric
Upload a rubric containing criteria and performance bands. The system understands expectations at each grade level.
Courses / MAMC01805 / Submit
Submit Assignment
Live previewResearch Methods Report · 30% weighting
Student ID
Assignment Content
RequiredSubmit Work
Students or lecturers submit assignments for AI-powered evaluation against the rubric criteria.
Feedback by criterion
3 criteria reviewedAnalysis & Argument
DistinctionThe argument is coherent and the findings are interpreted rather than simply described.
Evidence & Referencing
MeritSources are relevant and used well, with a little more critical comparison needed.
Structure & Clarity
MeritThe report is easy to follow and would benefit from a sharper limitations section.
Review Results
Receive detailed results with performance band scores, evidence-based feedback, and clear improvement areas.
Capabilities
Key Features
Designed for accurate, fair, and scalable assessment
Performance Band Assessment
Evaluate submissions across predefined performance bands using consistent rubric-aligned scoring.
PDF Rubric Extraction
Automatically extract criteria, performance bands, and requirements from rubric PDFs — no manual setup needed.
Evidence-Based Feedback
Every score is supported with clear evidence from the submission, including structure, citations, and content quality.
Learning Outcomes Integration
Course learning outcomes are automatically mapped to assessment criteria to ensure fair, outcome-driven evaluation.
Scalable for Institutions
Mark large cohorts consistently while reducing lecturer workload and turnaround time.
Audience
Who Is This For?
Built for the entire academic ecosystem
For Students
Receive clear, evidence-backed feedback and fair, transparent grading.
For Lecturers
Save hours of marking time while maintaining consistency across every submission.
For Universities
Deploy scalable, audit-ready assessment across entire cohorts and programmes.
Ready to simplify and standardize assignment marking?
Consistent, rubric-aligned grading at any scale — so you can focus on teaching, not marking.
Common questions
- Can AI grade essays reliably?
- AI marking is most reliable when it scores against an explicit rubric rather than judging quality in the abstract. AI Sentinels applies your rubric criterion by criterion, so every submission is measured the same way and the marks come with the reasoning behind them. An educator reviews and can override any mark, which keeps accountability with the teacher and makes the tool a first pass rather than the final word.
- How does automated marking handle a rubric?
- You upload the rubric with your assignment, and each criterion becomes a scoring dimension. The engine assesses every submission against those dimensions, returns a score per criterion with structured feedback, and highlights the strengths and gaps it based each mark on. Applying the same rubric to every script can improve consistency across a cohort and gives markers a common first pass to work from.
- Does auto-marking replace the teacher?
- No. It produces a first-pass mark and structured feedback that a teacher reviews, adjusts, and approves before anything is released. The value is in removing the repetitive part of marking a large cohort, not in removing judgement. Every mark stays editable and the reasoning is visible, so the educator can see why a score was given before accepting it.
- Does it work with Moodle, Canvas, or Google Classroom?
- Moodle and our developer API are available today, and Canvas and Google Classroom are on the way. Integration options vary by plan and deployment, so the practical answer depends on which LMS you run and how you want submissions to flow. Contact us to confirm support for your setup before committing to a rollout.