ai or human essay checker

ai or human essay checker

The Role of AI in Essay Checking: Enhancing Accuracy and Efficiency

1. 1. Introduction

Assistive technology has been shown to enhance the productivity, confidence, and quality of life of disabled individuals. Although there is a significant investment in the development of writing tools, much of the newly developed technology is proprietary in nature, so not much information has been distributed in open source format. This study comes to fill this gap and presents various essay checking assets. These are also part of the GrETEL online environment, which has been established to provide the writer with good quality writing, caring for text coherence and text cohesion at all text levels. Our study is unique in the sense that the essay checking elements are professionally pre-processed and processed for the detection of essay or writing issues. According to our knowledge, no other study addresses such a computer-based essay checker, and the quality of our elements is tested against assessment established by professionals and students.

Assistive technology plays a significant role in enhancing both the quality and quantity of writing assistance. Throughout the years, long before dictation took up a pronounced role in generating the text itself, special dictionaries and thesauruses were implemented to improve the quality of the text. These days, various tools assist writers throughout the writing process. Most of them are simple open source tools that help the writer with simple grammar and spell checking. There are also richer environments available online, developed by groups such as Microsoft, Google, and Grammarly, which include additional stylistic suggestions. One of the most common tools, namely Microsoft Word, can be connected through a simple interface with commercially available grammar aids like Grammarly.

2. 2. The Evolution of Essay Checking

The fourth period was characterized by widespread implementation of a fully automated scoring system. Today, many companies and often governments use these fully automated scoring systems. The fifth period is aimed at creating a computer program that actually teaches a student how to write. Upon completion of the writing, the student should be told how the written text should be graded. The development of computer programs that can offer constructive criticism is still pending. Only after a hybrid AI system that uses fuzzy logic and machine-learning tools advances to the point of applying feedback to real essays in real-time, can begin to assert that the stage of the essay became mature.

Several technologies have been used to evaluate essays. These technologies have developed from human judgment to artificial intelligence. The history of grading essays has been divided into several significant periods. The first wave of automated essay scoring systems experienced heavy usage during the 1960s until the 1980s. Second, using computers to grade essays was replaced with more fruitful processes, such as using human raters plus a computerized environment to score essays. Among the innovations during this period were knowledge engineering with natural language processing-based systems. The third period was triggered by the advent of the large-scale writing assessment along with the widespread adoption of the computer in the education setting. During this period, automated essay scoring companies used a hybrid system that incorporated both human raters and computer programs.

3. 3. Benefits of AI in Essay Checking

Another significant benefit is that AE systems reduce teachers’ grading workload. As mentioned in the AI-ED report, one significant main workload issue teachers face is repetitive grading and the cognitive load associated with the grading of monotonous tasks. The main difference between AE grading and teacher grading is the former’s ability to grade much more quickly. The accuracy of grading results is also very important. Automated essay checking can provide feedback regardless of the time of day. With AE technology, assignments can be graded while students sleep, thus allowing teachers to grade assignments more quickly without risking lower grading accuracy.

One significant benefit of automated essay checking is the reduction of a tedious task for teachers. Essay evaluation for academics is a time-consuming and generally non-work-related task. Teachers who are faced with numerous assignments from students are often overburdened at the end of the semester. By reducing the time it takes for essay evaluation using AE systems, teachers can have more time to engage in their other academic tasks.

4. 4. Ethical Considerations and Limitations of AI in Essay Checking

Automatically grading students in cases where it is not necessary can invite potential breach of privacy, especially cases where inconsistent checker links can flaw the evaluation of vulnerability. Mandatory grading programs where contrasting from a student based off of ethnicity, sexual orientation, race, or religion of a student are not ethically encouraged.

AI also cannot understand the paper context well enough to prevent false positives or negatives regarding plagiarism. Language, cultural, and subjective biases stem from low scoring margins and misunderstandings. It can also lead to repetitive examples provided that may encourage undesirable writing effects. The writing techniques necessary for the skimming of grader to recognize and draw conclusions for a different question may be ethically questionable.

As the main concern of AI-based essay checking is reducing grading discrepancies, when there is a subjective question, AI may fail to tackle it properly. In the case of criteria miscoding or misapplication, AI is not well equipped to determine human attributes of a writer. These include qualitative features like originality, uniqueness, sense of humor, unusual insights, and stylistic nature.

It is important to mention the limitations and ethical considerations of AI-based essay checking. Since most AI applications are based on what is commonly fed into the system, AI may be biased in many ways. Although AI technology is evolving rapidly, it is not possible to prevent bias from influencing the system. Limitations in paper corrections are mainly relevant to subjectivity issues in scoring that are applied to evaluate certain questions.

5. 5. Future Prospects and Recommendations

The requirement also applies to plagiarism detection. Cross-disciplinary knowledge base is helpful for enhancement, and the more data we have, the better e-learning we can provide. To sum up, this paper presents a semantic-stats method for more adaptive and accurate question checking. We also introduce a semantic parsing process that helps with human-machine instructions. The experiments on TFIDF have shown that semantic detection methods have advantages in the robustness and universality of algorithms. Our results also serve as possible solutions to abnormal word detection of NLP. This work can be used as a tool for both essays and large-scale competitive question checking, especially for those questions which are seldom updated. The semantic-stat method is lexical scaling?

Predictive typing and automatically grading mechanisms have become indispensable in large-scale examinations. Regarding the robustness and universality of algorithms, an effective machine learning system can be trained with diversified essays which contain common errors of humans, offering more accurate grading and more intelligent question checking. Therefore, training data from cross-disciplinary essays can enhance system performance, even identifying slang, jargon, spelling, or factual errors. The plausible ways to improve essay checking robustness and accuracy include word and semantics non-ambiguous binarization and dictionary spurious correction. Another challenging extension of our work consists of subjective factors. Coeducation and non-native language speakers are already here, and we are now living in a much more diversified social environment. Slang and special terminology cannot be eliminated, but dictionary management must be enhanced.