The Pros and Cons of Automated Essay Grading

The term Automated essay grading (AEG) essentially means when Artificial Intelligence (AI) and Natural Language Processing (NLP) are used to evaluate and grade written essays. This technology uses algorithms that work to analyze the essay factors, including grammar, spelling, word choice, syntax, and others, to generate a grade or score for the essay content.

Since automated grading solutions have gained interest and their adoption has grown recently, education and assessments have moved increasingly to more online and digital formats. Advocates point to many potential pros, such as saving teachers’ time, getting rid of scoring bias, and offering an immediate response back to students. Critics argue that accuracy limitations, scoring integrity, and effects on students are cons, but they do not exist.

This article examines the key pros and cons of using essay AI grader today and projections for the future. We’ll analyze the capabilities and limitations of current solutions, present use cases and statistics on real-world implementation, review impacts on educators and students, and discuss the outlook for advancement as AI and NLP evolve.

The Rise of Automated Grading Solutions

Automated grading technology originated in the 1960s, but its capabilities remained extremely limited until recent breakthroughs in artificial intelligence and machine learning. In the past decade, major strides have occurred in NLP and neural networks that can analyze written text and language more accurately than ever before.

Several vendors now provide AI-based essay scoring solutions used by hundreds of universities, public school districts, and testing organizations worldwide. The largest provider, EdX, supports essay grading for tests like the SAT, GMAT, and TOEFL. Public schools in at least 21 U.S. states use automated scoring to handle growing numbers of written exams and asynchronous assignments.

Use continues to rise rapidly. Recent estimates project that the global automated essay scoring software market size was valued at approximately USD 0.25 billion in 2023 and is expected to reach USD 0.75 billion by 2032, growing at a compound annual growth rate (CAGR) of about 12% from 2023 to 2032. This represents a major shift in automated grading adoption to keep pace with remote and digital learning trends.

Pros of Automated Essay Grading

Automated essay scoring delivers several potential benefits that explain its surging usage.

Saves Teachers’ Time

Grading written essays and assignments represents one of teachers’ most labor-intensive, time-consuming tasks. Automated solutions can significantly expedite the process and alleviate this burden.

For example, estimates show teachers may spend upwards of 10-15 minutes grading a single essay. For a class of 25 students, that equates to 4-6 hours spent. Automated scoring can evaluate essays in 1 minute or less per essay, saving teachers hours of manual work and freeing up more time for lesson planning, teaching, and providing student feedback.

Provides Rapid Student Feedback

Related to saving teachers’ time, automated grading also enables students to receive scores and feedback on written assignments much faster. Rather than waiting days or weeks for teachers to grade papers, automated systems can evaluate submissions within seconds and instantly provide students with their essay scores.

Immediate performance feedback allows students to pinpoint writing areas to improve sooner. And research shows faster feedback also leads to better long-term retention and skills development.

Eliminates Subjective Scoring Biases

Unlike human graders who inherently apply subjective biases and preferences to essay scoring, automated grading solutions utilize unbiased, objective AI algorithms. Most systems are trained on millions of essay examples to develop scoring rules that grade elements like semantics, vocabulary, and topical content accuracy without favoritism.

Through machine learning advancements, leading essay scoring engines have successfully minimized algorithmic biases as well. This results in impartial scores based strictly on essay quality versus grader biases that can negatively or positively influence human-graded scores.

Facilitates Large-Scale Assessments

Automated grading provides a scalable solution to accommodate high-volume essay and short-answer scoring needs for large testing organizations. For instance, one vendor’s AI grading tool reports an ability to score 400 billion short-answer questions a year – a volume practically impossible for human graders.

Such capacity enables more frequent, large-scale assessments to better gauge student learning and refine instruction programs systemwide. A few states now administer formative assessments every 2-3 weeks and credit AI scoring for making this feasible, where manpower cannot.

Cons of Automated Essay Grading

While automated essay scoring delivers noteworthy upside, legitimate downsides and limitations exist.

Cannot Match Human Grading Accuracy

The most significant disadvantage is that algorithmic grading cannot yet match human accuracy and perceptiveness. Although AI capabilities advance annually, fully mimicking human language comprehension and cognition remains complex and challenging.

Most automated engines still struggle to analyze semantics, inference, creativity, and other higher-order skills that human graders intuitively recognize in writing. Sophisticated arguments, original ideas, humor, irony, and other subjective language qualities pose accuracy issues as well.

Risks of Formulaic and Structured Writing

Critics argue that automated essay scoring, because algorithms analyze writing style and structures versus ideas, incentivizes formulaic, uninspired writing geared to please AI models versus demonstrate true skills. For instance, long essays using complex vocabulary may receive strong scores regardless of substance.

Additionally, well-trained models can usually recognize content with high plagiarism quite well. However, students may discover “tricks” to slightly manipulate copied text to avoid plagiarism detection. This could promote cheating if applied incorrectly to high-stakes assessments.

In both cases, the concern is that automated scoring’s limitations may distort writing instruction if teachers and students fixate solely on superficial styles and structures rewarded by AI. Without balancing human scoring, writing quality may shift toward template-based versus original, creative structures, which would set back skill development.

Lacks Qualitative Feedback

Most automated scoring systems can assign grades and provide basic quantitative feedback explaining score calculations. However, algorithms struggle to deliver meaningful qualitative analysis with constructive suggestions to improve, like human graders.

Rating scale criteria are also limited, often reducing essay quality to a 1-6 numeric score. Such simplified metrics fail to capture the nuances and growth opportunities that teachers’ individualized comments can provide. Students lose out on important coaching tailored to their needs that generic AI feedback lacks presently.

Perception of Impartiality

Finally, despite aiming for unbiased objectivity, studies show students often view automated scoring as less fair and trustworthy than teacher grading. Students believe human readers better understand concepts and contexts to judge work impartially versus bots.

Negative perception erodes student confidence in scoring integrity. Further, some observers believe overdependence on algorithms to evaluate writing risks dehumanizing instruction as an impersonal, numerical process versus nurturing talent.

Outlook for Advancements in Automated Grading

The above cons reveal real downsides to curbing the more ubiquitous implementation of automated essay evaluation technologies today. However, rapid evolution continues, suggesting AI capabilities will advance markedly in the coming years to address many current limitations.

Several developments show strong promise. First, scoring accuracy continues to progress as machine learning models receive more training data. For example, leading vendors now claim scoring parity with human graders, predicting models will exceed average teacher accuracy by 2025.

Natural language generation advancements also show potential for automated feedback. New models like GPT-4 demonstrate improving capabilities, summarizing key points, and generating specific qualitative feedback superior to current template comments.

Additionally, to counter risks of formulaic writing, adaptive scoring algorithms show promise in assessing higher-order analysis like critical thinking versus writing style alone. Models in development also aim to detect sophisticated cheating attempts better.

Finally, enhanced system validation and external audits on scoring fairness may further build user confidence and acceptance if applied properly to ease perception issues.

Conclusion

Advancing artificial intelligence has the potential to lead to an automated essay scoring application of great transformational value in education. Real benefits such as teacher time savings, fast, unbiased scores to improve writing assessments are already being delivered by leading systems.

However, as with any legitimate cons, the accuracy limitations and the impact on writing quality show that there is still some evolution to come. It is conceivable in the near term that automated grading solutions will become viable alternatives to low-stakes assessment, and in the long term, partners could continue to play a role in grading high-stakes tests alongside their counterparts.

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