Best Practices for Building AI-Supported Marking Schemes

Grading is one of the most time-consuming and high-stakes components of academic work. In 2025, with student enrolments rising, faculty workloads intensifying, and growing demand for personalised feedback, higher education institutions are increasingly turning to AI-supported marking schemes to streamline assessment and ensure consistent quality.

Yet, the integration of AI into marking raises a critical question: How can we build AI-supported marking systems that are reliable, ethical, and pedagogically aligned?

This blog post outlines best practices for designing AI-assisted marking schemes that preserve academic integrity while leveraging the efficiencies and insights that artificial intelligence offers.

What Are AI-Supported Marking Schemes?

AI-supported marking schemes refer to assessment structures where AI tools assist in evaluating student work, either through automated scoring, rubric alignment, pattern recognition, or feedback generation.

These systems can be:

Fully automated (e.g., auto-grading multiple choice or coding exercises)

Semi-automated (e.g., AI suggests a grade or feedback, but a human confirms)

Human-in-the-loop systems (AI highlights areas of concern or excellence to help the instructor)

While already common in standardised testing, these schemes are now gaining traction in formative and summative higher education assessments — from essay evaluation to peer review moderation.

Why Use AI in Marking?

The benefits of AI-supported marking schemes include:

Efficiency – Save time on repetitive grading tasks

Consistency – Reduce variation across graders and sections

Scalability – Manage large cohorts with minimal delay

Personalisation – Generate targeted feedback at scale

Data Insights – Identify trends, common errors, or at-risk students

Used correctly, these tools don’t replace human judgment—they amplify educator capacity and enhance the student experience.

Best Practices for Designing AI-Supported Marking Schemes

1. Start with Robust Rubric Design

AI marking tools are only as good as the rubrics they’re trained on or guided by. A well-designed rubric ensures that AI:

Recognises key competencies

Applies levels of performance consistently

Avoids over-focusing on surface features (e.g., word count or syntax)

Tip: Use AI to help build the rubric, but finalise it through human peer review.

2. Align Rubrics with Learning Outcomes (LOs)

For AI to assess meaningfully, the rubric criteria must be explicitly mapped to intended learning outcomes. This ensures:

Pedagogical alignment

Accurate grading guidance

Better analytics on LO attainment

Consider using AI models like GPT-4o or TheCaseHQ’s rubric alignment tool to auto-map rubric rows to CLOs or NQF descriptors.

3. Define the AI's Role: Autograder, Assistant, or Auditor?

Before deployment, clarify what role AI will play in the marking workflow:

Role	Description	Examples
Autograder	Fully automates scoring	Quizzes, coding tasks
Assistant	Suggests scores or feedback	Essays, reflections
Auditor	Flags anomalies for review	Peer assessments

Best practice: Use assistant or auditor roles for open-ended tasks, retaining human oversight.

4. Train the AI on Diverse, Annotated Examples

For supervised models (or fine-tuned LLMs), it’s crucial to train on:

Varied student submissions

Clear annotations of grading decisions

Edge cases (e.g., excellent but unconventional answers)

This helps the AI avoid bias and better generalise across student styles.

5. Pilot Before Full Implementation

Before deploying AI grading at scale:

Run a parallel trial: AI and human mark the same batch

Analyse discrepancies

Refine rubrics or model prompts based on feedback

This ensures quality control and builds faculty confidence.

6. Ensure Transparency and Explainability

One of the most significant concerns about AI marking is the “black box” effect. Students and faculty must understand:

How the AI works

What it looks for

What the final grade is based on

Solutions include:

Feedback reports generated by AI

Annotated rubrics

Optional human appeal pathways

7. Include Human Oversight for High-Stakes Assessments

AI can misinterpret nuance, sarcasm, or cultural context. For major assignments:

Combine AI-generated suggestions with human moderation

Use a “dual marking” model (AI + human) for final grade determination

Flag “uncertain” scores for mandatory human review

This balances efficiency with fairness.

8. Audit for Bias and Equity

Check if the AI disproportionately mis-scores certain groups (e.g., EAL students, neurodiverse learners). Include diverse data in training and test for:

Lexical bias

Format dependency

Cultural misunderstandings

AI that fails inclusivity can deepen existing educational inequalities.

9. Provide Feedback, Not Just Scores

AI can quickly generate tailored feedback like:

“Your argument is well-structured but lacks critical depth.”

“Try integrating more peer-reviewed evidence.”

“Excellent clarity and originality in your opening.”

This not only helps students improve but also meets quality assurance standards.

10. Integrate With Your LMS or e-Assessment System

For seamless use:

Choose tools compatible with Canvas, Moodle, Blackboard, etc.

Ensure secure data handling (especially GDPR compliance)

Track rubric-to-grade mappings for audit purposes

Cloud-based AI feedback widgets (e.g., ChatGPT plugins or LMS add-ons) make this easier than ever in 2025.

Tools to Explore

Tool	Key Feature
Gradescope	AI-assisted rubric-based grading
ChatGPT (GPT-4o)	Rubric generation, feedback suggestions
TheCaseHQ Templates	AI-powered LO-linked rubrics
Magicschool.ai	Customisable feedback & assessment tools
FeedbackFruits	LMS-integrated feedback assistant
Turnitin Draft Coach	AI-supported writing improvement (not grading)