Academic Integrity Risks Beyond Plagiarism

For many learners, the first rule that comes to mind when they hear about academic integrity is to avoid plagiarism.

Before submitting an assignment, many learners ask the same question.

“What similarity score is considered safe?”

That is a useful question, but it addresses only one aspect of academic integrity.

An assignment with a very low similarity score can still breach academic integrity rules if someone else completed it, if the data were fabricated, if the learner collaborated on an individual assessment or if they used artificial intelligence in a way their institution prohibits.

Academic integrity concerns whether the text overlaps with other sources as well as whether the work was produced honestly and reflects the learner’s own abilities.

1. Academic integrity goes beyond avoiding plagiarism

The International Center for Academic Integrity (ICAI) identifies six fundamental values that underpin academic integrity.

• Honesty
• Trust
• Fairness
• Respect
• Responsibility
• Courage

Under this framework, plagiarism is one of several forms of conduct that can undermine these values.

Australia’s Tertiary Education Quality and Standards Agency (TEQSA) identifies several potential breaches, including plagiarism, collusion (unauthorised collaboration), contract cheating (arranging for someone else to complete academic work, with or without payment), recycling work (reusing previously submitted work) and fabrication (inventing information).

A plagiarism check therefore addresses one category of risk within the wider issue of academic integrity.

2. A low similarity score offers limited evidence of academic integrity

A similarity report highlights passages that match text in existing sources.

Questions about how the work was produced require further evidence.

For example, someone else could write an assignment from scratch for a learner. The text might return a very low similarity score because it has never appeared elsewhere.

If the learner submits the assignment as their own, the question of authorship remains.

The Quality Assurance Agency for Higher Education (QAA) identifies contract cheating as a risk to academic standards. Work written specifically for a learner can be difficult to detect using ordinary text-matching tools.

That distinction matters when interpreting a similarity report.

Even a low similarity score leaves the question of academic integrity open.

Learners should consider how the assignment was produced alongside the score in the final report.

3. Collaboration can cross a boundary in individual assessments

Discussion with peers is a normal part of university study.

Two learners can discuss a lecture, explain a concept to each other or share resources.

When an assessment calls for individual work, jointly drafting answers, sharing assignment content or using each other’s work can cross that boundary.

This is commonly referred to as collusion, or unauthorised collaboration.

TEQSA lists collusion alongside plagiarism and contract cheating as conduct that may constitute academic misconduct.

A narrow focus on online sources can give learners a false sense of security.

“I didn’t copy anything from the internet, so I should be fine.”

Material can come from the internet, another learner, a support provider or someone else. If the assessment calls for individual work, using material produced by another person can create an academic integrity risk.

4. Reusing your own work is subject to institutional rules

Learners sometimes ask a reasonable question about work they have already completed.

“I wrote this assignment myself. Why can’t I use it again?”

Reusing all or a substantial part of work previously submitted for assessment may be treated as recycling work or a form of self-plagiarism, depending on the institution’s policy. TEQSA lists recycling work as a type of misconduct that can occur in higher education.

The concern is that the same work could receive credit in two separate assessments without the learner disclosing its reuse.

There may still be scope to build on earlier ideas or research.

In many cases, learners can develop previous work further when it fits the programme requirements and is properly cited or disclosed.

Before reusing material they have already submitted, learners should check what their institution allows and how to acknowledge the earlier work.

5. Research data and references must be verifiable

Fabricating data or falsifying it poses a serious risk in research assignments.

These problems can arise at several stages of a research project.

• Claiming to have interviewed 30 people when the interviews never took place
• Inventing survey responses
• Changing data to make the results look better
• Discarding unfavourable results without a methodological justification
• Citing a fictitious paper

TEQSA identifies the fabrication or falsification of data, the deliberate exclusion of data to manipulate results and the misrepresentation of authorship as potential breaches of research integrity.

This deserves particular attention when learners use automated tools to find references or help with writing.

A fabricated reference can look like a genuine citation to an academic paper.

Learners should verify each source before using it.

6. Responsible AI use depends on the assessment rules

Generative AI has made the boundaries of academic support more complex.

Learners may use AI to clarify a concept, get language support or explore ways to organise their work, among other tasks.

Institutions can differ in where and to what extent they permit AI use, and in whether learners must disclose it.

In 2026, TEQSA updated its Academic Integrity Toolkit to address AI-related risks specifically. It emphasised the need to clarify how learners may use AI in each type of assessment.

TEQSA also notes that copying AI-generated material into an assessment without disclosure can fall within the traditional definition of plagiarism in some circumstances.

What matters is the policy that applies to the institution and to the specific assessment.

One module may allow AI for language support.

Another may permit its use provided the learner discloses it.

A particular assessment may prohibit it altogether.

Before using AI, a learner might focus on whether their use can be detected.

“Will my use of AI be detected?”

The more useful question is about what the assessment rules permit.

“How am I allowed to use AI for this assessment?”

7. Small mistakes can create academic integrity risks

Academic integrity problems sometimes begin with an oversight or a misunderstanding.

Several common mistakes can put a learner at risk.

• Forgetting to acknowledge a source
• Paraphrasing too closely to the original
• Misunderstanding how to cite a source
• Submitting the same assignment for two different modules
• Sharing an assignment with a classmate without expecting them to use it
• Using an AI tool before reading the institution’s rules

Even an unintentional breach can have academic consequences.

The QAA therefore encourages institutions to address academic integrity through education, clear information and student support as well as through procedures for handling misconduct.

Learners can reduce the risk by checking the rules early, well before submission.

At the start of an assignment, learners should be able to answer these questions.

How much help am I allowed to receive? Which parts must I complete myself? How should I acknowledge my sources? May I use AI? If so, how should I disclose my use of it?

8. How SAS helps learners identify risks before submission

Swiss Academic Support (SAS) treats academic integrity as part of the learning process, with support that can begin well before a final similarity check.

Depending on the learner’s needs, SAS can provide support in several areas.

• Explain citation requirements
• Review the learner’s use of sources
• Identify wording that may be too close to the original source
• Check academic formatting
• Help learners interpret their similarity reports
• Flag risks associated with reusing earlier work
• Clarify the scope of third-party support
• Remind learners to check their institution’s rules on AI and other support tools

SAS can flag risks involving authorship. A review by SAS leaves the learner responsible for submitting their own work.

SAS provides guidance. Learners are responsible for writing their own assignments, using genuine research data and citing verifiable references. Any assurance about the outcome of an academic integrity review is outside the scope of SAS support.

Final decisions about academic misconduct rest with the institution under its own policies and procedures.

SAS aims to help learners identify problems early enough to prevent an avoidable breach.

Conclusion

Plagiarism is a serious risk. Academic integrity also concerns how the work was produced.

An academic submission should accurately represent who completed it, use genuine data and verifiable sources, respect the permitted scope of collaboration and comply with the rules governing support tools.

A question about similarity covers one part of the review.

“What is the similarity score for my assignment?”

A broader question considers the whole process.

“Does the way I produced this assignment accurately reflect my own work and abilities?”

That question gets to the heart of academic integrity.

Academic integrity goes beyond avoiding plagiarism. SAS helps learners identify risks involving sources, authorship, data, AI and how the assignment was produced before they submit it.

References

International Center for Academic Integrity. (2021). The fundamental values of academic integrity (3rd ed.). ICAI.

Quality Assurance Agency for Higher Education. (2022). Contracting to cheat in higher education: How to address essay mills and contract cheating (3rd ed.). QAA.

Tertiary Education Quality and Standards Agency. (2026). Academic integrity toolkit. TEQSA.

Tertiary Education Quality and Standards Agency. (2023). Guidance note: Academic and research integrity. TEQSA.