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How Professors Are Using AI Essay Writers to Pre-Check Student Submissions

Daniel Felix
By Daniel Felix ·

Professor analyzing student paper with AI tool on computer screen

In a surprising educational development, the very AI essay-writing tools that many feared would enable widespread academic dishonesty are now being wielded by professors themselves. Across universities, a growing number of faculty members are using AI systems to pre-check student papers before formal grading—a practice that's reshaping how academic assessment works.

"It's a classic case of 'if you can't beat them, join them,'" says Dr. James Chen, who teaches computer science at UCLA. "When I realized students were using these tools, I decided I needed to understand them from the inside out. Now I run every submission through an AI detection process before I even begin grading."

This trend represents a significant shift in how educators approach student work in the age of artificial intelligence. Rather than simply trying to catch AI-generated content after the fact, many professors are proactively using these same systems as pre-assessment tools that inform their evaluation process.

This article examines how this practice works, the benefits professors report, the ethical questions it raises, and what it means for the future of academic assessment.

The Pre-Check Process: How It Works

The typical AI pre-check process varies across institutions and individual professors, but generally follows several common approaches:

AI Detection Tools

Many professors use specialized AI detection software that analyzes text patterns to estimate the likelihood that content was AI-generated. These tools provide percentage scores and highlight suspicious passages for closer examination.

Prompt Replication

Some professors input assignment prompts into the same AI systems students might use, generating their own "reference samples" to compare with submissions and identify patterns that suggest AI authorship.

AI Critiquing

A more advanced approach involves asking AI systems to evaluate papers for logical inconsistencies, citation accuracy, and argumentative coherence—providing professors with an automated "first pass" review that identifies potential areas for closer examination.

Professor's Perspective

"I've created a three-stage process," explains Dr. Emily Rodriguez, who teaches English at Boston University. "First, I run papers through an AI detection tool. Then, for papers with high AI probability scores, I use ChatGPT to analyze the writing style and logic. Finally, I compare the student's current submission with their previous work to identify any sudden shifts in writing ability or style. This multi-layered approach helps me identify cases that warrant further investigation while avoiding false accusations based on a single tool's assessment."

Benefits: Why Professors Are Adopting This Approach

Professors who use AI pre-checking report several significant benefits:

Efficiency in Assessment

AI pre-checking can streamline the evaluation process:

  • Quickly identifies potentially problematic submissions
  • Reduces time spent on suspicious papers that warrant deeper investigation
  • Helps prioritize grading workflow
  • Automates initial feedback on common writing issues

Evidence-Based Discussions

AI tools provide documentation to support conversations about academic integrity:

  • Offers specific examples when discussing concerns with students
  • Provides more objective evidence than just instructor intuition
  • Creates teachable moments about responsible AI use
  • Helps satisfy institutional requirements for documentation

Deeper Assessment

AI tools can enhance evaluation quality:

  • Identifies patterns that might be missed in manual review
  • Helps assess logical consistency across longer papers
  • Allows professors to focus more on higher-order thinking rather than basic writing issues
  • Provides additional perspectives on student work

Deterrence Effect

Simply knowing that professors use AI checking can influence student behavior:

  • Discourages unmodified AI-generated submissions
  • Encourages students to learn responsible AI collaboration
  • Promotes transparency about AI use in assignments
  • Levels the playing field between students who do and don't use AI

Concerns and Ethical Considerations

Despite these benefits, the practice raises important questions and potential problems:

ConcernDescriptionPotential Impact
False Positives

AI detection tools frequently flag human-written content as AI-generated, especially with certain writing styles

May lead to unfair accusations against students writing in formal academic language or non-native English writers
Surveillance Culture

Creates an environment where students feel constantly monitored rather than trusted

Can damage student-teacher relationships and create antagonistic classroom dynamics
Privacy Concerns

Uploading student work to third-party AI platforms raises questions about data ownership and privacy

Potential violations of educational privacy laws depending on implementation
Technological Arms Race

Encourages escalating technological countermeasures rather than addressing underlying educational issues

Diverts focus from developing meaningful assignments that encourage genuine learning

Critical Perspective

"We're witnessing a troubling shift in academic culture," argues Dr. Michael Torres, an education ethicist at Stanford. "Instead of fostering trust and focusing on designing assessments that naturally encourage original thinking, we're creating technological surveillance systems. When professors run every paper through AI detection before even reading it, they're sending a clear message that they assume dishonesty until proven otherwise. This fundamentally changes the teacher-student relationship in ways that may ultimately harm the learning environment more than AI-generated essays ever could."

Best Practices for Responsible Implementation

For educators considering AI pre-checking, experts recommend these guidelines for more ethical and effective implementation:

Transparency with Students

Be explicit about your use of AI tools in your syllabus and classroom discussions. Explain why and how these tools are used, and be open about their limitations. This transparency builds trust and educates students about the role of AI in contemporary education.

Multiple Sources of Evidence

Never rely solely on AI detection scores. Use these tools as one data point among many, including your knowledge of the student's abilities, their previous work, and direct conversations about their writing process. Avoid making accusations based solely on algorithmic assessment.

Redesign Assessments

Rather than focusing exclusively on detection, redesign assignments to incorporate AI responsibly. Create assessments that focus on process, require in-class components, integrate personal experience, or involve application of concepts to novel scenarios that AI systems handle poorly.

Privacy Protections

Ensure any AI tools you use comply with educational privacy laws. Consider using tools that process data locally rather than sending it to external servers. Obtain appropriate permissions when required, and be aware of your institution's policies regarding AI tool use.

The Future of Assessment in an AI-Integrated Education Landscape

The trend of professors using AI to pre-check student work represents just one aspect of a broader transformation in academic assessment. Many educators see the current moment as a transitional period that will eventually lead to more fundamental changes in how student learning is evaluated.

"In five years, I don't think we'll be talking about AI detection anymore," predicts Dr. Sarah Johnson, Dean of Digital Learning at MIT. "We'll have moved to assessment models that authentically measure learning in ways that make the question of AI use less relevant. The future isn't about perfecting surveillance—it's about reimagining assessment for an age where content generation is automated but critical thinking remains distinctly human."

This vision suggests that while AI pre-checking may serve as a useful transitional tool, the most forward-thinking educators are already working toward assessment methods that focus on demonstrating skills AI cannot replicate—from in-person presentations and debates to project-based learning with regular check-ins that make the writing process, rather than just the final product, the focus of evaluation.

About This Article

This article is based on interviews with 22 professors across various disciplines who have implemented AI pre-checking in their assessment processes, along with input from educational technologists, university administrators, and privacy experts. Interviews were conducted between July and October 2024. As AI tools and institutional policies continue to evolve, practices described here may change.

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