AI and Authorship 

by Jocelio Ferreira

    PUBLISHED FEB 9, 2026                    

Clarifying Academic Integrity in the Age of AI

This artefact addresses a growing issue in Education:

Students copying assignments into AI systems and submitting the generated output unchanged.

The project reframes the problem using a compliance and authorship lens:

Authorship exists when a human meaningfully controls the creative process.

The goal was not to criticize AI use, but to clarify the boundary between assistance and substitution.

The Problem

Faculty report a recurring pattern:

• An assignment is created;

• A student copies the assignment into an AI tool;

• AI generates a response;

• The student submits the output unchanged.

This creates confusion around:

• Authorship;

• Academic integrity;

• Responsibility;

• Evaluation standards.

Many institutions frame this as “AI cheating.”

This artefact reframes it as misattributed authorship.

AI is a tool, not an author.

Authorship follows the person who:

• Provides meaningful input;

• Guides the process;

• Makes decisions;

• Evaluates and verifies content;

• Accepts responsibility for the result.

When a student does not exercise intellectual control, authorship does not transfer to the student.

In that case, the output reflects the structure of the assignment designer, not the reasoning of the student.

Submitting it as original work constitutes plagiarism by misrepresentation of authorship.

This artefact introduces an “Authorship Chain” model:

Normal Academic Flow

Professor → Student interpretation → Student decisions → Student submission

→ Authorship belongs to the student.

Copy–Paste AI Flow

 Professor → Assignment copied into AI → AI generates → Student submits unchanged

 → Student removed from the authorship chain. Technically the authorship here is the person who develops the assignment.  

Without intellectual control, there is no student authorship.

This clarification supports:

• Faculty authority in academic integrity cases;

• Institutional compliance language;

• Clear student expectations;

• Policy development around AI use;

• Ethical AI literacy.

It moves the conversation away from “anti-AI” reactions and toward measurable standards of intellectual contribution.

Design Approach

Format: Visual infographic for LinkedIn

Audience: Education faculty and administrators

Tone: Direct, non-emotional, legally grounded

Objective: High clarity, minimal interpretation required

Key design choices:

• Step-by-step process diagram;

• Clear authorship definition;

• Explicit use of the word “plagiarism”;

• Visual emphasis through structured flow.

The publication generated discussion among faculty regarding:

• Assignment design resilience;

• Authorship definitions in AI contexts;

• Practical classroom enforcement;

• Reframing plagiarism in the AI era.
  
  

It demonstrates how compliance reasoning can be translated into accessible public scholarship.

© Jocelio Ferreira — AI Workflow Guide — 2026