ai writing dector

ai writing dector

AI Writing Detector

1. Introduction

However, this condition eventually replaces the need for truly intelligent technology. Since users require more contact at various stages of the more complex and multi-dimensional technology, there is a need for user-level agents that can engage in real intelligent conversation. In other words, users need the assistant’s understanding in the form of sensitive interpretation and response until an appropriate decision is made. Agents are expected to be able to answer difficulties where other agents could not provide a useful answer, as well as offer situationality. Moreover, agents tend to have systematic limitations as they are expected to apply AI learning models in general to any topic and successfully address individual queries in the referenced field.

In approaching artificial intelligence agents of assistant function, writing in the form of text is the most common way to communicate between user and agent. If the assistant has the capability of compassionate chat, for example, the user could share their situation in life. Then, the agent will actively listen, show empathy, and follow up with appropriate actions. These actions might include finishing the conversation itself, giving advice, giving a quiz, asking another question, and other things that contribute to the character building of the user. Thus, this kind of ability is like a “friend” with sophisticated skills related to problem handling. Most of the time, users do something with it that is related to the function of paying attention to user needs. Here, of course, because of the close relationship with users, the interaction developed is the easiest and most useful pathway as a reference to developing agents as user-level agents.

2. Importance of AI Writing Detection

Why are we discussing the importance of AI writing detectors in an era when writing assessment is so limited? Well, times have changed. AI research efforts have made significant breakthroughs in the vast majority of automated solutions in the field of natural language processing, including text classification, machine translation, and spell check. For example, text classification models are now more effective than humans in detecting toxic behavior in social network and online interaction and ranking search results in accordance with Google search guidelines. The automated translation tools of Google exhibit no sign of deterioration; and a variety of personal assistants’ interfaces are equipped with free spray spell checking. Despite these vast improvements, as mentioned earlier, writing assessments still require teacher assessments. Furthermore, researchers have made tremendous progress in developing immersive educational platforms that use AI to guide students through query-based learning. These platforms can guide students through problems and correct their mistakes while also collecting large amounts of data about learning outcomes. Nevertheless, ruler-based academic assessments do not yet benefit from the same AI-added assistance.

The writing assignment is the most common form of assessment in American schools. Yet we’re challenged by the limitations of time and talent in providing accurate assessment of student writing. Many proper evaluations are time-consuming and often depend on the individual subjective nature of the teacher. The entire process of providing students with helpful feedback on their papers can be very tedious. These challenges add up to a gap between students’ greatest needs and educators’ limited capabilities. Teachers may be forced to grade based on a template or skip writing assessments altogether, leaving a significant gap in students’ learning and writing proficiency.

AI-powered learning has redefined how we approach content. As students rely on technology to gain an edge in their academic performance, teachers too can meet that challenge with technology. An AI writing detector, in particular, lets educators and students leverage the capabilities of AI to improve students’ writing.

3. How AI Writing Detector Works

We depict the full-pipeline of AI writing detector in fig. 1. The features of the ensemble model will now be explained in three parts: the definition of the prompt discriminator that allows us to train GPT-2 and GPT-3 models on our fine-tuning set, the retraining classifiers that we use to create an ensemble model combining multiple predictions, and a classifier based on supervised learning features, which are extracted from model’s predictions, not fine-tuning outputs.

An AI writing detector prediction is a perfect binary label which is true if the text comes from GPT-2 (or GPT-3), and false otherwise. We predict this label by training several models: a prompt discriminator (PD) that modifies the GPT-2 output to maximize the likelihood of a true label, a few retraining classifiers (RT) that learn to classify the discriminator’s prediction, a supervised classifier (SC) of language models’ predictions, and an ensemble model (and a full ensemble mode) to combine all those classifiers’ predictions into a single one. The PD is first trained from anchor texts provided by humans before being further fine-tuned by a noisy dataset of claimed outputs. The RT models use consistent but simple features built in this fine-tuning process, and the SC is trained mainly on text-agnostic features. In short, we mimic the “wisdom of many model selection” approach where the insight of each model is used to combine predictions in a stable, non-vulnerable to adversarial noise, manner.

4. Benefits of Using AI Writing Detector

When your errors are repeatedly corrected by the AI writing detector, chances are that you will eventually get the hang of it! You will not be afraid to commit the same mistakes because you already know that the AI writing detector is there to rectify you. The AI writing detector allows you to ‘learn as you go’; meaning that you can actively look out for what it has highlighted as errors and incorporate them into future writings. People usually share the knowledge they acquire from a particular experience. Since you will be interacting with the tool on a daily basis, you will be able to improve on your strengths and weaknesses over time and better yourself as a writer. Also, you could use the reader feedback feature to assess yourself and watch out for those mistakes as you write. While you are at it, practice makes perfect. Do it often and you will see how much you have improved.

2. Improve writing skills

This is the most common reason people use a grammar checker tool. Writing AI detectors can accomplish the task in real-time, thereby reducing the time a writer could take to complete writing. This tool can help you check grammar in 1-2 minutes, which gives more time to write more. In addition to its time-saving capabilities, using an AI writing detector can save you money. As much as hiring a professional editor to correct some of these errors may be costly, paying for the AI writing detector is relatively cheaper and affordable for everyone.

1. Saves time

5. Conclusion

My proposed research agenda for future AI Writing Detector research will help to improve the reliability, accountability, and interpretability of AI writing detectors. This broad ambition carries the potential for machine learning-based AI writing detectors to assist and guide society with improving human writing communication skills. When the gap between machine and human performance increases, we, as writers and as users, should feel empowered by advanced AI tools to better understand what we do well now. AI, as one of the greatest achievements of our civilization, should not breed fear and alienation but instead provide new motivation for writing self-improvement.

To reach the potential of AI tools for benefiting society, AI researchers need to produce transparent, explainable, and ethical AI systems. When writing detectors are used to guide and criticize writing, the apparent objectivity and bias-free nature of AI systems could be misleading for writers and decision-makers. The strengths of AI for writing analysis depend on our careful consideration and the right balance of the human and the machine capabilities in a socio-cultural context. As AI literacy improves across society, a more informed understanding means we, as end-users, would hopefully be more critical and more realistic about the real capabilities and limitations of AI writing detectors.