Direct answer
The chatbot landscape in 2026 is dominated by a small set of general-purpose assistants — ChatGPT, Gemini, Claude, Copilot, Perplexity and Meta AI — each built on a distinct large language model, plus a long tail of task-specific bots for support, tutoring and search. Wikipedia maintains a dated, sourced inventory of conversational agents that runs from early rule-based experiments such as ELIZA and ALICE through to today's model-backed assistants [1]. For students, the practical question is not only which bot to use but what happens to chatbot-written prose once it passes through an academic AI detector [1].
What Are the Main AI Chatbots Available Today, and Who Makes Them?
The list splits cleanly into general-purpose assistants and narrow task bots. General-purpose assistants map one-to-one onto model families: ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), Copilot (Microsoft, built on OpenAI models), Perplexity (Perplexity AI, retrieval-first) and Meta AI (Meta). Wikipedia's inventory keeps these entries tied to named makers and release dates, which is why it is more useful as a reference than a vendor roundup [1]. Task-specific bots — customer service agents, tutoring systems, search assistants — make up the rest of the list and are usually built on one of the same underlying models [1].
That shared foundation matters for academic work. Because the major assistants are all large language models, their output carries broadly similar statistical fingerprints: fluent, evenly paced, low-variance sentence structure. Turnitin's own guidance notes that its AI writing detection was developed to flag text likely produced by large language models such as ChatGPT, Claude and Gemini, and that the result is expressed as a percentage of qualifying text rather than a judgement about who wrote it [2]. In other words, the name on the chatbot matters far less than the fact that an LLM generated the prose [2].
A third category has emerged that students should recognise: writing assistants embedded inside tools they already use — Google Docs suggestions, Grammarly's generative features, Notion AI. These rarely appear on a "list of chatbots" because they are not standalone conversational products, yet they produce LLM text that detectors can flag. Turnitin also cautions that its detector can produce false positives, particularly on formulaic or heavily templated writing, so a high score is a signal to investigate rather than a verdict [2].
How Do Turnitin-Style AI Detectors Decide Whether Chatbot-Written Text Is AI-Generated?
Detectors do not read for meaning; they read for statistical regularity. Turnitin's AI writing detection divides a submitted document into segments of qualifying text and reports what share of that text it believes was AI-generated, excluding segments below a minimum length so that short quotations and reference entries do not distort the percentage [3]. This is why a paper can score 0% on one draft and a much higher figure after a light paraphrase: the segmentation and the underlying signal both shift with the wording [3].
The output is deliberately hedged. When the signal sits below Turnitin's confidence threshold, the report displays a *% style low-confidence indicator instead of an exact figure, signalling that the evidence is weak rather than that the text is clean [3]. Detection also only runs within supported assignment and file-type parameters, so a document that falls outside those limits simply returns no AI indicator at all [3].
Two practical consequences follow. First, detector scores are not portable: a score from one tool does not predict what Turnitin will report, because the models, thresholds and segmentation rules differ [3]. Second, because the detector responds to surface-level regularity, heavy editing that removes predictable phrasing tends to lower the score, while paraphrasing that preserves the same rhythm often does not [3].
Can a Student Check a Chatbot-Assisted Draft With a Real Turnitin AI Report Before Submitting It?
Sometimes, but not reliably. Turnitin's student-facing documentation explains that students in supported courses can see an AI writing indicator on their own submission only when the instructor has enabled student visibility; where that setting is off, the student cannot see the AI percentage before the instructor does [4]. Because instructor settings vary by course and by institution, in-course visibility is not something a student can count on [4].
Even when visibility is on, the report keeps AI detection and similarity separate — AI-generated segments are displayed distinctly from matched sources, so the two numbers answer different questions and should be read independently [4]. A low similarity score says nothing about AI generation, and a high AI score says nothing about plagiarism [4].
The dependable pre-submission route is therefore a non-repository check: submit the draft to a service that returns the same style of AI and similarity reports without adding the file to a student paper database. Turnitin0.com runs exactly this kind of check — a .docx, .pdf or .txt upload (300–30,000 words, under 20 MB) returns a Turnitin AI detection report and a similarity report together, typically in under 15 minutes, with the file kept out of the student paper repository. That gives a student the same two signals an instructor would see [4], before the submission that actually counts.
If you have just finished a chatbot-assisted draft and you would rather not discover the AI score in your instructor's gradebook, turnitin0 can show you the report first — the same AI percentage and similarity matches, on your own file, before you submit.
※ Turnitin0.com - Actual Turnitin AI Report Cover, Score, Flag And Similarity Summary
FAQ
Which chatbots appear on most current lists?
The general-purpose assistants dominate: ChatGPT, Gemini, Claude, Copilot, Perplexity and Meta AI, each tied to a named maker and model family [1]. Historical entries such as ELIZA and ALICE still appear because the list is organised by era as well as purpose [1].
Does Turnitin flag text from every chatbot equally?
No. Detection responds to statistical regularity in the text rather than to the brand of chatbot, so output from ChatGPT, Claude or Gemini is treated on the same basis [2]. Turnitin also notes that false positives are possible on formulaic writing [2].
Why does my AI score show an asterisk instead of a number?
That display appears when the signal falls below Turnitin's confidence threshold, meaning the evidence is weak rather than absent [3]. It is a low-confidence indicator, not a clean bill of health [3].
Can I see my AI score before submitting an assignment?
Only if your instructor has enabled student visibility on that assignment; otherwise the indicator stays hidden from you until after submission [4]. Because that setting varies, a non-repository pre-submission check is the more dependable option [4].
Does a low similarity score mean my chatbot draft is safe?
No. AI detection and similarity are reported separately and measure different things, so a low similarity percentage tells you nothing about AI generation [4]. Read the two signals independently [4].