How to Stop Students From Cheating With AI

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

Detecting AI assistance reliably requires observation at the layer where the assistance runs: the operating system. ScreenComply pairs a browser-based API with an optional desktop agent that inspects the environment around the assessment rather than only the page inside it.

The agent enumerates running processes and browser extensions against a continuously updated list of AI assistants, answer overlays, and interview-copilot tools. It looks for always-on-top and transparent windows, remote-access sessions, virtualization, screen-capture drivers, additional connected displays, and USB storage events. It checks whether keyboard input originates from local physical hardware, and it records focus changes, clipboard events, and typing cadence patterns that distinguish composition from paste-and-modify behavior.

No detection system is perfect, and ScreenComply does not claim to catch every form of assistance. The goal is different and more useful: gather corroborating, timestamped signals so a human reviewer can make an informed, defensible decision instead of guessing from a webcam thumbnail.

• Process and extension enumeration against a maintained AI-assistant list.
• Overlay and always-on-top window detection, including windows excluded from screen share.
• Remote access, virtual machine, and screen-capture detection.
• Multi-monitor enumeration queried from the operating system, not inferred from gaze alone.
• Input-source integrity, clipboard and paste events, and typing-cadence analysis.
• Network and secondary-device signals on the local network.

Prevent mode: blocking rather than only flagging

Detection tells you what happened. For high-stakes assessments, many institutions want the assistance to never start. ScreenComply supports two operating modes: Detect mode observes quietly and flags anomalies for review, while Prevent mode blocks restricted applications from launching during the assessment window.

In Canvas, this becomes a workflow rather than a separate platform. The assignment or quiz stays sealed until the student is genuinely being proctored, then unlocks for that student the moment the session goes live. Students launch from a single link inside the course they already use.

How audit-ready integrity reports work

A detection is only useful if it can be explained to someone who was not in the room. Every ScreenComply session produces a structured integrity report: an executive verdict with a risk level, a chronological evidence timeline, and the underlying signal detail behind each entry.

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.