{"id":44594,"date":"2026-01-16T15:24:47","date_gmt":"2026-01-16T09:54:47","guid":{"rendered":"https:\/\/www.editage.com\/insights\/?p=44594"},"modified":"2026-02-09T18:38:42","modified_gmt":"2026-02-09T13:08:42","slug":"decoding-ai-detection-in-academic-writing","status":"publish","type":"post","link":"https:\/\/www.editage.com\/insights\/decoding-ai-detection-in-academic-writing","title":{"rendered":"Decoding AI Detection: How to Avoid AI Detection in Academic Writing"},"content":{"rendered":"

One thing\u00a0authors\u00a0detest\u00a0is running their piece of content, written without the help of any artificial intelligence (AI) tool,\u00a0and\u00a0receiving a high AI detection score!\u00a0And these high\u00a0AI detection\u00a0scores can be\u00a0extremely damaging to a writer\u2019s reputation, especially in the case of students, academics, and researchers.<\/span>\u00a0<\/span><\/p>\n

This blog will help you understand how AI detectors actually work,\u00a0explain their limitations, and highlight how you can\u00a0avoid AI detection.<\/span>\u00a0<\/span><\/p>\n

How AI Detectors Work<\/span><\/a>\u00a0<\/span><\/p>\n

Limitations of AI Detectors<\/span><\/a>\u00a0<\/span><\/p>\n

How is AI Usage Detected in Academic Writing<\/span><\/a>\u00a0<\/span><\/p>\n

AI Text Detectors vs. Plagiarism Checkers<\/span>\u00a0<\/span><\/a><\/p>\n

Tips to Avoid AI Detection in Academic Writing<\/span><\/a>\u00a0<\/span><\/p>\n

How AI Detectors Work<\/h2>\n

With the rise of AI writing tools,\u00a0it should be no surprise that\u00a0there came a demand for\u00a0AI detection software and tools.\u00a0AI detectors are\u00a0typically\u00a0used by professors, institutions, educators, publishing houses,\u00a0and content creators\u00a0to keep AI-generated content in check<\/span>1<\/sup><\/span>.\u00a0These tools detect AI writing primarily through two criteria:\u00a0<\/span>Perplexity<\/span><\/b>\u00a0and\u00a0<\/span>Burstiness<\/span><\/b>.<\/span>\u00a0<\/span><\/p>\n

Perplexity<\/h3>\n

Like the word\u00a0suggests, \u201cperplexity\u201d is a measure of surprise or unexpectedness\u00a0in the flow of text.\u00a0AI detectors\u00a0look for highly predictable\u00a0text and\u00a0identify\u00a0them as AI-generated content,\u00a0whereas\u00a0more natural sounding language is\u00a0considered\u00a0a sign of human-like writing.\u00a0In other words, the lower the perplexity, the higher the AI detection score.<\/span>\u00a0<\/span><\/p>\n

Let us understand this with an example. Consider the following sentence: \u201c<\/span>It was raining heavily last_____<\/span><\/b>.\u201d\u00a0There are multiple ways to complete the sentence, and the table below explains\u00a0how each of the possible continuations are evaluated by AI\u00a0detectors.<\/span>\u00a0<\/span><\/p>\n\n\n\n\n\n\n\n
\u00a0<\/span><\/td>\nPossible continuations\u00a0for the sentence<\/span><\/b>\u00a0<\/span><\/td>\nPerplexity<\/span><\/b>\u00a0<\/span><\/td>\n<\/tr>\n
1<\/span>\u00a0<\/span><\/td>\nIt\u00a0was raining\u00a0heavily last\u00a0<\/span>night.<\/span><\/span>\u00a0<\/span><\/td>\nLow:<\/span><\/b>\u00a0The\u00a0most likely word\u00a0to be used to complete the sentence.<\/span>\u00a0<\/span><\/td>\n<\/tr>\n
2<\/span>\u00a0<\/span><\/td>\nIt was raining heavily last\u00a0<\/span>time we visited the beach.<\/span><\/span><\/td>\nLow-to-Medium:<\/span><\/b>\u00a0Not so likely continuation, but it is grammatically correct and makes sense.<\/span>\u00a0<\/span><\/td>\n<\/tr>\n
3<\/span>\u00a0<\/span><\/td>\nIt was raining heavily last\u00a0<\/span>summer, eerily confirming that climate change is real!<\/span><\/span>\u00a0<\/span><\/td>\nMedium:<\/span><\/b>\u00a0The sentence uses unexpected phrases and is slightly poetic,\u00a0indicating\u00a0human-like writing.<\/span>\u00a0<\/span><\/td>\n<\/tr>\n
4<\/span>\u00a0<\/span><\/td>\nIt was raining heavily\u00a0last\u00a0<\/span>when the fox jumped over the moon.<\/span><\/span><\/td>\nHigh:<\/span><\/b>\u00a0The sentence makes no logical sense, using surprising wording that no AI tool would use.<\/span>\u00a0<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

As you can see in the first sentence, \u201cnight\u201d\u00a0is\u00a0the most\u00a0logical\u00a0option\u00a0to complete the sentence, and AI text detectors give this a low perplexity score.\u00a0The second and third sentences are likely to receive higher perplexity scores,\u00a0indicating\u00a0human-like writing because of the\u00a0less predictable wording and language. The fourth sentence\u00a0will receive the highest perplexity score considering that it makes the least logical sense to\u00a0the\u00a0AI detector!<\/span>\u00a0<\/span><\/p>\n

Does this mean that you should\u00a0intentionally\u00a0make mistakes\u00a0in your writing? Definitely not! More on this later.<\/span>\u00a0<\/span><\/p>\n

Burstiness<\/h3>\n

Now let us understand the second factor\u00a0used for measuring AI-generated text: burstiness.\u00a0Burstiness is a measure of the rhythm in the flow of text.\u00a0This is\u00a0similar to\u00a0perplexity, except that it applies at a sentence level check.\u00a0<\/span>\u00a0<\/span><\/p>\n

Humans tend to write using a mix of short and long sentences,\u00a0involving\u00a0both simple and complex phrases. AI writing tools, on the other hand, tend to repeat certain phrases and\u00a0maintain\u00a0the\u00a0sentence length\u00a0quite uniform\u00a0throughout the text.\u00a0This means that\u00a0a monotonous tone in the text is likely to\u00a0receive a\u00a0lower score for burstiness,\u00a0indicating\u00a0AI-generated writing.<\/span>\u00a0<\/span><\/p>\n

Overall, AI\u00a0detectors calculate the balance between perplexity and burstiness to\u00a0determine\u00a0whether the writing is human-\u00a0or AI-generated.\u00a0But these tools are constantly\u00a0evolving,\u00a0and more layers of evaluation metrics are being added to enhance their\u00a0accuracy of distinguishing between AI-generated content\u00a0and human writing.\u00a0Yet, at present,\u00a0know\u00a0that AI detectors are not foolproof!<\/span>\u00a0<\/span><\/p>\n

Limitations of AI Detectors<\/h2>\n

This brings us to the limitations\u00a0of AI detection tools.\u00a0We have all heard that AI detectors are biased<\/span> and may not be entirely reliable\u00a0because of how the same\u00a0piece\u00a0of text are evaluated\u00a0differently\u00a0by\u00a0various tools.\u00a0A study<\/span>2<\/sup><\/span>\u00a0published in\u00a02023 also\u00a0observed\u00a0that AI detectors were biased against\u00a0non-native English authors. They\u00a0reported\u00a0that\u00a0AI detectors accurately classified\u00a0essays written by US students,\u00a0whereas\u00a019.8% of the human-written TOEFL essays were\u00a0incorrectly flagged as AI-generated.\u00a0One\u00a0detector ended up flagging over 97% of TOEFL essays as AI-generated!<\/span>\u00a0<\/span><\/p>\n