Higher education guide
College Cheating and AI: What Institutions Can Actually Do
AI changed college cheating by moving assistance from shared answer keys to a private assistant available to every student at any moment. Text-based AI detectors are unreliable and lockdown browsers cannot see desktop assistants, so institutions are left without evidence at exactly the point they need it. The workable response combines assessment design, clear policy, and device-level exam integrity that produces reviewable evidence.
Why text-based AI detectors are not the answer
Detecting AI in a finished document means judging text that no longer carries reliable authorship signals. These tools produce both false accusations and easy evasions, and a student can defeat them by rephrasing. Basing an academic-integrity case on a probability score puts the institution in a weak position at appeal.
Session-level evidence is qualitatively different. Instead of arguing about whether prose looks generated, the institution can point to what was running on the machine during the assessment and when.
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.
Fitting exam integrity into the LMS you already run
ScreenComply supports trustworthy results by reviewing the whole session and flagging concerns for human review, with evidence attached.
Fairness, accessibility, and student privacy
Integrity tooling has to work for every student or it should not be deployed. Major screen readers and magnifiers are whitelisted by default and are never treated as restricted tools. Accommodations such as extended time, breaks, dictation, and alternative input devices are configured by the institution per session, without the student disclosing medical information to the platform.
Privacy is handled through configurable retention tiers, including modes that minimize or eliminate stored artifacts. Access to reports is role-scoped, and the institution retains control over how long session data lives.
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
Has AI made college cheating worse?
It has made assistance continuously available and effectively private, which changes the problem from access to detection. Rather than quantifying it, institutions should assume any unproctored, unverified assessment can be completed with AI help.
Are AI writing detectors reliable enough for academic integrity cases?
They are generally not reliable enough to carry a case on their own. They produce false positives on legitimate work and are defeated by rephrasing, which is why session-level evidence is a stronger foundation.
Does ScreenComply work inside Canvas?
ScreenComply supports trustworthy results by reviewing the whole session and flagging concerns for human review, with evidence attached.
What about students who need accommodations?
ScreenComply supports trustworthy results by reviewing the whole session and flagging concerns for human review, with evidence attached.
How long is student data retained?
Retention is configurable by the institution, including privacy tiers that minimize or eliminate stored artifacts and automated deletion on a defined schedule.
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.