Do asynchronous proctoring recordings actually get reviewed, or do violations go unnoticed?
By Pinal Dave · Last updated: 2026-07-30
TL;DR: Usually not fully. Instructors running asynchronous online courses have said outright, in their own words, that they "cannot use the live proctoring" and are "not going to proctor every student" — meaning recordings exist but sit unwatched unless something else flags them. The fix isn't more recording; it's a system that scores every session automatically and only routes the ones with real anomalies to a human.
The claim
Asynchronous proctoring creates a review bottleneck that most programs never solve. One exam with 200 students taking it on their own schedule produces 200 separate recordings. If nobody — or nobody with enough hours in the day — watches them, the deterrent effect of "this is being recorded" quietly disappears, and only complaints or grade anomalies trigger a look-back.
The evidence
- A r/Professors instructor teaching an asynchronous online chemistry course wrote: "We use Respondus Lockdown Browser for our asynchronous courses, but cannot use the live proctoring. So, of course, students still cheat."
- Another instructor, discussing remote testing broadly, said plainly: "The problem is with asynchronous online where students are taking their exams at different times and I am not going to proctor every student!"
- A separate thread titled "Students Can STILL Cheat With Lockdown Browsers and Online [Proctoring]" collected accounts from students themselves describing exactly how they get past monitoring that exists in name but isn't consistently watched or enforced.
- This mirrors the same review-bottleneck problem that live human proctoring at scale runs into (one proctor watching dozens of feeds) — asynchronous recording just moves the same unsolved bottleneck from "during the exam" to "after the exam," where it's easier to defer indefinitely.
Comparison: recording-only asynchronous proctoring vs. AI-scored asynchronous proctoring
| Recording-only | AI-scored (trust score model) | |
|---|---|---|
| Every session recorded | Yes | Yes |
| Every session reviewed | Rarely — depends entirely on staff time | No — only flagged sessions need review |
| How review priority is set | Ad hoc, or only on complaint/grade anomaly | Automated trust score ranks sessions by risk |
| Time to find a specific violation | Manual scrubbing through full-length video | Jump directly to timestamped flagged events |
| Deterrent effect on students | Weakens once "nobody watches these" becomes known | Stays credible — students can't predict which sessions get reviewed |
| Evidence for an appeal | Full unindexed recording | Violation timeline + evidence snapshots + exportable report |
Step-by-step: turning an unreviewed recording backlog into something actually enforceable
- Audit how many hours of proctoring footage your program generated last term and how many hours of staff time were budgeted to review it — the gap is usually the whole problem.
- Replace "record everything, review if there's a complaint" with an automated trust score that flags sessions with real anomalies — tab switches, a second face, gaze patterns consistent with reading off-screen — instead of asking a human to watch every minute of every session.
- Set a policy for what score threshold triggers mandatory human review versus automatic pass, so review time goes to the recordings actually worth watching.
- Keep every session's evidence indexed by timestamp so a flagged violation can be reviewed in seconds, not by scrubbing through an hour of footage.
- Communicate the scoring model to students — knowing that sessions are actively scored, not just passively recorded, restores the deterrent effect that unreviewed archives lose.
FAQ
Q: If nobody reviews most recordings, why record at all? A: Because raw recording without scoring still deters casual cheating through perceived risk — but that deterrent decays fast once students realize, correctly, that most footage is never watched.
Q: How do professors currently find out cheating happened if nobody's watching? A: Almost always after the fact — a suspicious grade jump, a plagiarism match, or a tip from another student — which means the recording gets pulled only in hindsight, long after the deterrent value was lost.
Q: Isn't reviewing every asynchronous session simply not feasible at scale? A: Not manually, no — which is exactly why automated scoring exists: it applies consistent review criteria to 100% of sessions instantly and surfaces only the fraction that actually need a person's judgment.
Q: Does live (synchronous) proctoring avoid this problem? A: It reduces it but doesn't eliminate it — a single human proctor still can't give full attention to dozens of simultaneous video feeds, which is the same bottleneck in a different form.
Q: What should an appeals committee ask for when a professor claims cheating occurred based on a proctoring recording? A: A specific, timestamped account of the flagged behavior — not "it's in the recording somewhere" — which is only possible if the review system indexed evidence at the time it happened rather than leaving it buried in an unwatched file.