The advice on preventing cheating in online classes hasn’t changed much in years: mix up your question types, verify identities, keep things timed. That advice still matters, but it was written for a world where a student’s worst move was Googling an answer mid-quiz. That’s not the world instructors are teaching in anymore. As online learning continues to evolve, instructors and coaches need to think beyond traditional course formats and create more engaging, personalized learning experiences. Understanding the different types of personal coaching can also help educators explore more specialized approaches to delivering knowledge and supporting learners.
By some measures, the share of students using an AI tool like ChatGPT during exams jumped from 66% in 2024 to 92% in 2025. A generative AI model doesn’t just supply a memorized answer, it can write an entire original-sounding essay, solve a novel problem, or explain a concept in the exact tone a rubric is looking for, in seconds. The old cheating toolkit (search engines, shared answer keys) hasn’t gone away, but it’s now the smaller problem.
This guide covers both layers: the assessment-design and identity-verification basics that still work, and the newer guardrails built specifically for an AI-assisted world, so what you put in place actually holds up in 2026.
Quick Answer: What should you do to avoid cheating in online classes?
No single tactic stops cheating completely, but layering these consistently makes it impractical:
Why This Looks Different in 2026: The AI Problem
Traditional cheating deterrents assumed a student needed to find an existing answer, from a classmate, a textbook, or a search result. Generative AI breaks that assumption: it can produce a plausible, original-sounding answer to a question it has never seen before, which means plagiarism checkers built to catch copied text often miss it entirely. Specialized browser extensions now exist specifically to detect exam questions from text or screenshots and generate answers within seconds, built for this exact purpose.
This doesn’t mean every anti-cheating measure needs to change, it means the ones aimed only at copied text or shared answer keys now cover a shrinking share of the actual problem. The strategies below are ordered by how directly they address that shift.
Design Assessments So Cheating Doesn’t Help Much
The most durable defense isn’t a tool, it’s how the assessment itself is built.
Verify Who’s Actually Taking the Test
Identity verification matters most for high-stakes assessments, certifications, final exams, anything tied to a credential, and matters far less for routine practice quizzes, where it mostly adds friction without much benefit.
For the assessments that do warrant it, ID verification tools like ProctorExam compare a test-taker’s ID document against a live biometric scan, and some proctoring services add a room scan before the exam starts. These tools are worth the setup cost for a certification exam; they’re usually overkill for a weekly comprehension check.
Use Technical Guardrails Proportionally
A handful of technical controls address the AI-specific risk directly, without requiring a full institutional proctoring rollout:
AI-text detectors, like Turnitin’s AI writing detection or GPTZero, flag content patterns consistent with AI generation. These aren’t perfect and shouldn’t be the sole basis for an accusation, but they’re a useful signal on written assignments where AI-generated text is the main risk.
The point isn’t to stack every available tool onto every assessment. Match the guardrail to the stakes: a certification exam earns the full set, a weekly practice quiz needs a fraction of it.
Build the Culture Piece, Not Just the Rules
Technical measures catch some cheating; a stated culture of integrity prevents a meaningful share of it before it happens. Naming the academic integrity policy explicitly in the first lesson, explaining why it exists rather than just what it forbids, and giving students a channel to ask questions when they’re unsure what counts as help, all measurably reduce casual, opportunistic cheating.
This matters more, not less, in an AI-saturated environment: students genuinely unsure where the line sits between “using AI to understand a concept” and “using AI to produce the answer” benefit from an instructor who states that line clearly, rather than leaving it to be discovered through a violation.
How Academy LMS Helps You Structure This
None of the strategies above require a specific plugin, but a WordPress LMS built for this makes them far easier to actually implement. Academy LMS‘s quiz builder includes the settings that back several of the design choices above directly: a configurable time limit (in seconds, minutes, or hours), a passing grade percentage, and answer randomization, so the same quiz doesn’t present choices in the same order to every student.
For sequencing, Prerequisites let you require one course or assessment before a student can access the next, which keeps a cohort roughly synchronized and reduces the chance of later students seeing earlier material out of order. Content drip releases lessons on a schedule instead of all at once, narrowing the window in which material can be copied wholesale before an instructor has reviewed engagement. And assignments support file-attachment submissions with point values and their own time limits, useful for the applied, personal-response questions that are hardest for AI to fake convincingly.
None of this makes cheating impossible. Combined with the assessment design and culture pieces above, it makes cheating enough work that most students don’t bother, which is the realistic goal.
A Simple Checklist Before Your Next Assessment
Frequently Asked Questions
What should you do to avoid cheating in online classes?
Layer several approaches rather than relying on one: design assessments around personal application instead of pure recall, verify identity for high-stakes exams, use proportional technical guardrails like time limits and answer randomization, and state your academic integrity policy explicitly rather than assuming students already know it.
How has AI changed cheating in online classes?
Generative AI tools can produce original, plausible answers to questions they’ve never seen, which means traditional plagiarism checkers built to catch copied text often miss AI-generated responses entirely. Reported AI tool use during exams has grown sharply in recent years, making assessment redesign, not just detection, the more durable response.
Do I need proctoring software for every online assessment?
No. Proctoring and identity verification are worth the setup cost for high-stakes, certificate-granting exams, but they add unnecessary friction for routine practice quizzes, where a time limit and answer randomization usually cover the real risk.
Can AI-text detectors reliably catch cheating?
They’re a useful signal, not a verdict. Tools like Turnitin’s AI writing detection or GPTZero flag patterns consistent with AI-generated text, but they can produce false positives and shouldn’t be the sole basis for an academic integrity accusation.
Does breaking a big exam into smaller quizzes actually reduce cheating?
Yes, generally. A single high-stakes exam creates strong pressure to cheat when a lot rides on one attempt. Frequent, lower-stakes quizzes reduce that pressure and give students less incentive to risk cheating on any single assessment.
What’s the most overlooked way to reduce cheating in online classes?
Stating the academic integrity policy explicitly and explaining why it exists, rather than assuming students already understand the boundaries. This is especially relevant now that many students are genuinely uncertain where “using AI to learn” ends and “using AI to answer” begins.