AI Driven Workflow for Detecting Academic Integrity Violations

AI-Powered Academic Integrity Violation Detection

1. Identification of Potential Violations

1.1 Data Collection

Gather academic submissions from students, including essays, research papers, and assignments. Utilize platforms such as Turnitin or Grammarly to collect data.

1.2 Initial Screening

Employ AI-driven plagiarism detection tools to perform an initial screening of submissions for potential integrity violations. Tools like Copyscape and Unicheck can be integrated for this purpose.

2. Analysis of Submissions

2.1 AI Algorithm Implementation

Utilize machine learning algorithms to analyze submission patterns and identify anomalies. Implement tools like IBM Watson or Google Cloud AI for advanced data analysis.

2.2 Contextual Understanding

Incorporate natural language processing (NLP) to assess the context of the submissions. Tools such as OpenAI’s GPT can be used to understand the writing style and detect inconsistencies.

3. Review Process

3.1 Automated Alerts

Set up an automated alert system to notify educators of potential violations based on AI analysis. This can be facilitated through platforms like Canvas or Blackboard.

3.2 Educator Review

Allow educators to review flagged submissions. They can use AI-assisted tools for enhanced analysis, such as Turnitin’s Feedback Studio, to provide context and feedback.

4. Decision Making

4.1 Violation Assessment

Educators assess the severity of the violation using a standardized rubric. AI tools can provide insights into past cases for comparative analysis.

4.2 Final Decision

Based on the assessment, educators make a final decision regarding the violation. This can be documented through institutional systems for accountability.

5. Reporting and Feedback

5.1 Documentation of Findings

Document the findings and decisions made during the review process. Use AI tools to generate reports automatically, ensuring accuracy and consistency.

5.2 Providing Feedback to Students

Communicate the outcomes to students, providing constructive feedback. Tools like Google Classroom can be used to facilitate this communication effectively.

6. Continuous Improvement

6.1 Data Analysis for Trends

Utilize AI to analyze data over time for trends in academic integrity violations. This can help institutions adapt policies and educational approaches.

6.2 Training and Development

Implement training sessions for educators on the use of AI tools and the importance of academic integrity. Continuous professional development ensures that staff are equipped to handle violations effectively.