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LMS vs. LXP: what is the difference, and which one do you need?
A learning management system runs courses. A learning experience platform builds capability. Here is how they compare, and when you need one, the other or both.
AI proctoring vs. live proctoring vs. record and review: which model fits your exams?
Four ways to protect an online exam, compared on coverage, fairness, evidence, scale and cost, and when each one makes sense.
The short answer
For most high-stakes exams at scale, AI with human review gives you the coverage of automation and the judgement of people: every session is monitored, every flag is checked by a person, and the institution gets evidence it can stand behind. Live proctoring still makes sense for small, very high-stakes sittings. Record and review and AI-only tools fit practice and low-stakes tests, where no one has to defend a decision.
Side by side
| Criteria | AI + human reviewProctorly | Live human proctoring | Record and review | AI-only (automated) |
|---|---|---|---|---|
| How it works | AI monitors every session and raises flags; trained proctors review each flag; the institution makes the final call | A proctor watches a small group of candidates on webcam in real time | Sessions are recorded and reviewed afterwards, often only a sample | Software monitors the session and scores or decides on its own |
| Scales to large cohorts | Yes. Reviewers only spend time on flagged moments | Limited. Capacity depends on proctor numbers and booked slots | Partial. Recording scales; review effort grows with every hour recorded | Yes |
| Catches device-level cheating (AI assistants, overlay apps, virtual machines, remote access) | Yes. Device and screen signals sit alongside camera checks | Partial. Only what a proctor can see on camera or a shared screen | Partial. Camera and screen only, and only after the exam | Depends on the tool |
| A person reviews every flag before it counts | Yes | Yes, in the moment | Only if every recording is reviewed | No |
| Can step in during the exam | Yes, with live monitoring | Yes | No | Rarely |
| Evidence for appeals | Integrity report per candidate: clips, snapshots and the reviewer's decision | Proctor notes; recordings only if also enabled | Recordings, but decisions are not always documented | Scores and flags that can be hard to explain |
| Scheduling for candidates | On demand or scheduled | Booked time slots | On demand | On demand |
| Cost profile | Moderate. Human time goes to flagged moments only | Highest. Cost rises with every candidate hour | Low to moderate. Rises with review coverage | Lowest, with a higher risk of contested results |
Best for
AI + human review
ProctorlyHigh-stakes semester, entrance and certification exams at scale
Live human proctoring
Small cohorts where a proctor must watch every minute
Record and review
Low-stakes quizzes and practice tests
AI-only (automated)
Practice tests where no decision depends on the result
Why the model matters more than the feature list
Most proctoring comparisons start with features: face matching, browser lockdown, room scans. Those matter, but the bigger question is who decides when something looks wrong. That choice shapes how many honest candidates get flagged, how quickly results are released, and whether a decision survives an appeal.
The four models above answer it differently. AI-only tools let software decide. Record and review moves the decision to after the exam, often based on a sample. Live proctoring puts a person in the room, at a cost that grows with every candidate. AI with human review uses software to watch everything and people to judge what it finds.
When each model fits
- AI + human review: semester and end-term exams, entrance tests, certification and licensing exams, and any exam where a result may be challenged.
- Live proctoring: small cohorts, oral or practical assessments, and sittings where a regulator requires a person to watch throughout.
- Record and review: quizzes, coursework checks and practice tests where a spot check is enough.
- AI-only: mock tests and self-assessment, where no grade or admission depends on the outcome.
Questions to ask before you choose
- Which cheating methods are detected on the device, not just on camera?
- Who reviews AI flags, how quickly, and with what evidence in front of them?
- What does the institution receive for each candidate, and can it be used in an appeal?
- Who makes the final decision, and how is it recorded?
- How does cost change when the cohort doubles?
See how Proctorly combines AI monitoring with human review, or read why every AI flag needs a human.
Frequently asked questions
Is AI proctoring accurate enough to use on its own?
For practice tests, often yes. For exams that affect grades, admission or certification, no. Every automated detector produces false positives, so a trained person should review each flag before it counts against a candidate.
Is live proctoring more secure than AI proctoring?
Not necessarily. A live proctor sees what the camera shows, but many current methods, such as AI assistants, overlay apps and virtual machines, never appear on camera. Device-level checks combined with human review cover more of them.
How does AI with human review keep costs down?
Software monitors every session, so people only spend time on the moments it flags rather than watching every minute of every exam.
Who makes the final decision with Proctorly?
The institution. Proctorly flags, trained proctors review each flag with the evidence, and the institution receives an integrity report per candidate and decides.