AI Grading and AI-ON Assessments: Student Guide

Singapore universities are not treating AI as a simple ban-or-allow issue. The more realistic direction is mixed assessment: some tasks will allow AI-assisted work, while others will test independent understanding without AI. Students should prepare for both.

That shift matters because a polished final file is no longer enough. Lecturers may care about how the work was produced, what the student can explain, and whether the process matches the rules for that module.

Why AI-ON and AI-OFF thinking matters

NTU's College of Computing and Data Science has described a Learn with AI approach that includes both AI-ON and AI-OFF assessments. The wording is useful even beyond NTU because it captures the direction many students will experience.

AI-ON work may ask students to use tools responsibly, compare outputs, document decisions or improve productivity. AI-OFF work may ask students to solve, write, code or explain without tool assistance. Both formats reward understanding.

For a student using assignment help in Singapore, this changes what good support should look like. Support should make the student clearer and more capable, not dependent.

What can go wrong

AI-assisted study becomes risky when students skip verification. Common problems include:

• fabricated sources or weak citations
• code that runs only on the sample input
• essays that sound confident but do not answer the rubric
• charts or statistics that are not explained
• inconsistent writing style across sections
• inability to explain how the final answer was reached

These problems are not solved by better formatting. They are solved by better process.

A safer assignment workflow

Use this framework for AI-era coursework:

1. Read the brief and highlight any AI, collaboration or citation rules.
2. Create your own short plan before asking for help.
3. Use AI or tutoring only for bounded tasks such as examples, explanations, debugging or editing.
4. Keep notes on major decisions and revisions.
5. Check sources, calculations, code output and assumptions manually.
6. Prepare a short explanation of your method before the deadline.

For Python support, this may mean adding tests and comments. For data science support, it may mean explaining feature choices and metrics. For paper writing support, it may mean improving the research map rather than hiding weak sources.

What responsible help looks like

Responsible help can include tutoring, guided drafting, editing, source review, debugging, reference support and explanation. It should not encourage students to bypass learning or ignore school policy.

This is why Codingo asks for the brief, rubric and deadline before advising. The right support approach for a SUSS TMA may be different from a NUS coding lab, an NTU project demo or a Kaplan report.

If you are unsure whether your assignment is AI-ON, AI-OFF or somewhere in between, message Codingo before starting. A ten-minute clarification can prevent a much bigger problem later.

Sources

• NTU: Learning with AI in computing education
• SMU Engage: Framing the inclusion of AI in higher education
• MOE: Artificial intelligence in education
• NUS Centre for English Language Communication: AI and writing support