Building reliable and scalable AI grading systems for student success
AI-assisted grading can speed up marking, improve consistency and give students faster feedback. But without transparency and human oversight, the risk to fairness and trust is high.
Educators are asking clear questions: Where does AI make grading better, and where does it add risk? A recent webinar, held in partnership with Graide, looked at how to streamline assessment and feedback, the limits of large language models (LLMs) for fair grading, and the support students and staff need to understand AI's role.
What educators actually need from AI-assisted grading
Assessment must stay authentic to the discipline and the profession students are preparing for, while also checking core digital literacies. There's no one-size-fits-all tool-context matters.
Michelle Picard, pro vice-chancellor for learning and teaching innovation at Flinders University, put it plainly: "We need to be sure that when we're gaining those efficiencies, we are modelling the appropriate and ethical use of AI and students' data."
The non-negotiables
- Fairness - outcomes should not disadvantage any group.
- Validity - align with learning outcomes and professional standards.
- Reliability - consistent results across markers, cohorts and time.
- Unbiased - identify and limit unwanted patterns in data and models.
- Explainable - decisions that educators can inspect and justify.
Manjinder Kainth, CEO of Graide, argued for systems that make decision-making reviewable. He cautioned against relying on LLMs alone for grading because of bias, inconsistency and variable accuracy. "I recommend supervised machine learning systems because they're much easier to interrogate... which allows you to have fine-grained control of bias propagation."
Keep a human in the loop
"The fundamental thing we always say about AI is having a human in the loop - that's going to be your first line of defence against any bias," said Daniel Searson, curriculum and education developer at the University of Adelaide. He also stressed that informed consent from students and academics is essential for any pilot or rollout.
Policy, privacy and ethics
Transparency isn't optional. Be explicit about what the AI does, what data it sees and how results are used. Build your approach around recognised guidance, such as the NIST AI Risk Management Framework (NIST AI RMF), and ensure your data governance covers collection, storage, retention and deletion.
Picard's reminder stands: efficiency gains are meaningless if student data is mishandled or the process can't be explained.
AI as a second reader, not the grader
Rhodora Abadia, associate dean of UniSA Online, said her institution treats AI as a "secondary reader." Academic staff remain the primary markers. She also noted a common pitfall: some AI systems prefer conventionally structured arguments, which can penalise unconventional but strong answers. This is a training problem as much as a tooling issue-another reason to improve AI literacy among staff.
Faster feedback, more time for teaching
Used well, AI can deliver timely, formative feedback and pinpoint areas for improvement. That gives educators back time for higher-value work-coaching, personalisation and deeper conversations with students who need it most.
Implementation checklist for universities and faculties
- Define scope: What tasks will AI support (e.g., rubric suggestion, draft feedback, similarity checks), and what stays with humans?
- Choose the right technique: Prefer explainable, supervised models for grading tasks; use LLMs for feedback drafting with human review.
- Govern data: Limit training data, strip identifiers, set retention windows and document who can access what.
- Obtain consent: Inform students and staff; provide opt-out where feasible and document lawful bases for processing.
- Design rubrics tightly: Clear criteria, exemplars and thresholds. Calibrate with anchor scripts and norming sessions.
- Human oversight: Require spot checks, second marking for edge cases and escalation paths for disputes.
- Bias checks: Regularly audit outcomes across demographic groups and task types; adjust models and rubrics accordingly.
- Make it transparent: Tell students what the AI does and how their mark is determined. Share feedback logic where possible.
- Pilot first: Start with low-stakes assessments. Measure agreement with expert markers, student satisfaction and time saved.
- Monitor and iterate: Track drift, error types and false positives/negatives. Update models and guidance on a set schedule.
- Accessibility: Ensure feedback is clear, inclusive and compatible with assistive tech.
- Upskill teams: Provide targeted training on AI literacy, prompt quality, bias awareness and data protection.
The panel
- Rhodora Abadia, associate dean, UniSA Online, University of South Australia
- Manjinder Kainth, CEO, Graide
- Michelle Picard, pro vice-chancellor for learning and teaching innovation, Flinders University
- Sreethu Sajeev, branded content deputy editor (chair)
- Daniel Searson, curriculum and education developer, University of Adelaide
Further resources and next steps
- Build your institutional playbook using the NIST AI Risk Management Framework.
- Want structured training to raise AI literacy across staff? Browse AI courses by job to plan targeted upskilling.
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