Academic integrity guide
How to Stop Students From Cheating With AI
ScreenComply supports trustworthy results by reviewing the whole session and flagging concerns for human review, with evidence attached.
Start with prevention, not punishment
The most durable deterrent is a clear, published policy that students read before the exam begins, paired with an assessment that is hard to outsource. Ambiguity is what makes academic-integrity cases painful, so state plainly which tools are permitted, what is monitored, and what happens when a session is flagged.
Assessment design carries real weight. Questions that require reasoning about course-specific material, in-class artifacts, or a student's own prior work are harder to answer from a generic model. Randomized item banks, tighter windows, and short oral follow-ups on high-stakes items all raise the cost of assistance.
Prevention alone is not sufficient, though. Once a student can run a hidden assistant on the same machine as the exam, policy and question design need enforcement behind them.
- Publish an explicit AI-use policy per course and per assessment.
- Use item banks and randomization to reduce answer sharing.
- Ask for course-specific reasoning rather than recall.
- Reserve high-stakes weight for proctored or verified components.
- Tell students what is monitored before the session, not after.
Why browser-based proctoring misses modern AI assistants
Most proctoring tools are built as browser extensions or lockdown browsers. They can see the tab, the page, and sometimes the webcam, but their visibility ends at the edge of the browser process. Modern AI assistance does not live inside the browser.
Tools such as invisible interview copilots and always-on-top answer overlays run as native desktop applications. They render above every other window, are frequently excluded from screen capture and screen share, and never touch the exam tab. A lockdown browser can be perfectly locked down while an assistant quietly reads the question and displays an answer on the same screen.
The same gap applies to remote-control software, virtual machines, injected keyboard input, and a second device sitting off camera. None of these leave a trace inside the browser, which is why a session can look clean in a browser-only tool and still be assisted end to end.
- Native overlay applications draw above the browser and are often invisible to screen sharing and screen recording.
- Remote-access tools let another person drive the machine without any browser-visible signal.
- Virtual machines and sandboxes let a candidate run the assessment in a controlled environment with helpers outside it.
- Synthesized or injected keyboard input can type answers that were never physically typed.
- A second phone, tablet, or monitor off camera leaves nothing at all in the browser.
What device-level detection actually sees
ScreenComply supports trustworthy results by reviewing the whole session and flagging concerns for human review, with evidence attached.
Prevent mode: blocking rather than only flagging
ScreenComply supports trustworthy results by reviewing the whole session and flagging concerns for human review, with evidence attached.
How audit-ready integrity reports work
ScreenComply supports trustworthy results by reviewing the whole session and flagging concerns for human review, with evidence attached.
Each entry is timestamped and tied to the specific observation that produced it, so a reviewer can see what was detected, when it happened, and how strong the signal was. Reports are written in non-accusatory, factual language, because the institution or employer makes the decision, not the software.
Reports can be exported to PDF and shared with a secure link, which is what makes them usable in academic-integrity hearings, HR reviews, and compliance audits. Retention is configurable, including tiers that minimize or eliminate stored artifacts for privacy-sensitive programs.
- Executive verdict with a graded risk level rather than a pass/fail guess.
- Chronological, timestamped evidence timeline linked to underlying signals.
- Human-review-by-design framing: factual observations, not accusations.
- PDF export and secure sharing for hearings, HR files, and audits.
- Configurable retention, up to zero-retention operating modes.
Where to go next
Frequently asked questions
Can lockdown browsers stop AI cheating?
ScreenComply supports trustworthy results by reviewing the whole session and flagging concerns for human review, with evidence attached.
How do you detect ChatGPT use during an online exam?
ScreenComply supports trustworthy results by reviewing the whole session and flagging concerns for human review, with evidence attached.
Is AI proctoring fair to students?
ScreenComply supports trustworthy results by reviewing the whole session and flagging concerns for human review, with evidence attached.
Do students have to install software?
Your team agrees a setup plan and candidate notice before the assessment. A lightweight agent and review layers support the session, alongside the tools you already use.
What happens after a session is flagged?
ScreenComply supports trustworthy results by reviewing the whole session and flagging concerns for human review, with evidence attached.
ScreenComply is under contract with the State of Montana. Security and compliance documentation is shared after an introductory call.
See a session, a detection, and a report
A short walkthrough of the evidence reports and review experience your team would receive.
