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Generative AI in Collaborative Academic Report Writing: Advantages, Disadvantages, and Ethical Considerations

Mahshid Sadeghpour, Arathi Arakala, Asha Rao

TL;DR

This paper examines Generative AI in collaborative academic report writing, highlighting both productivity opportunities and risks such as bias, misinformation, privacy concerns, and environmental impact. It surveys the emergence of GenAI and its enabling technologies, discusses ethical use, and presents case studies demonstrating both harms and benefits in educational and professional contexts. Practical guidelines are proposed for ethical pre- and post-application use, including consent, attribution, data handling, and transparent documentation of AI involvement. The work aims to equip students and educators with a framework to leverage GenAI to enhance learning while preserving integrity, critical thinking, and personal authorship in an AI-enabled era.

Abstract

The availability and abundance of GenAI tools to administer tasks traditionally managed by people have raised concerns, particularly within the education and academic sectors, as some students may highly rely on these tools to complete the assignments designed to enable learning. This article focuses on informing students about the significance of investing their time during their studies on developing essential life-long learning skills using their own critical thinking, rather than depending on AI models that are susceptible to misinformation, hallucination, and bias. As we transition to an AI-centric era, it is important to educate students on how these models work, their pitfalls, and the ethical concerns associated with feeding data to such tools.

Generative AI in Collaborative Academic Report Writing: Advantages, Disadvantages, and Ethical Considerations

TL;DR

This paper examines Generative AI in collaborative academic report writing, highlighting both productivity opportunities and risks such as bias, misinformation, privacy concerns, and environmental impact. It surveys the emergence of GenAI and its enabling technologies, discusses ethical use, and presents case studies demonstrating both harms and benefits in educational and professional contexts. Practical guidelines are proposed for ethical pre- and post-application use, including consent, attribution, data handling, and transparent documentation of AI involvement. The work aims to equip students and educators with a framework to leverage GenAI to enhance learning while preserving integrity, critical thinking, and personal authorship in an AI-enabled era.

Abstract

The availability and abundance of GenAI tools to administer tasks traditionally managed by people have raised concerns, particularly within the education and academic sectors, as some students may highly rely on these tools to complete the assignments designed to enable learning. This article focuses on informing students about the significance of investing their time during their studies on developing essential life-long learning skills using their own critical thinking, rather than depending on AI models that are susceptible to misinformation, hallucination, and bias. As we transition to an AI-centric era, it is important to educate students on how these models work, their pitfalls, and the ethical concerns associated with feeding data to such tools.

Paper Structure

This paper contains 30 sections, 6 figures.

Table of Contents

  1. Introduction
  2. Emergence and Evolution of GenAI
  3. Advantages and Responsible use of GenAI Tools
  4. Potential Benefits of GenAI Tools
  5. Exploratory Brainstorming:
  6. Improving Productivity:
  7. Responsible use of GenAI Tools
  8. Terms and Conditions:
  9. Properly Acknowledge and Cite the use of any Software or Tool:
  10. Develop Communication Skills while at School:
  11. Disadvantages and Irresponsible use of GenAI Tools
  12. Disadvantages
  13. Bias:
  14. Repetition and Plagiarism:
  15. Inaccuracy and Misinformation:
  16. ...and 15 more sections

Figures (6)

  • Figure 1: A Venn diagram illustrating where Generative AI is positioned within the AI field. In this figure, Artificial Intelligence, Machine Learning, Deep Learning, Generative AI, and Large Language Models are represented by ML, DL, GenAI, and LLMs, respectively.
  • Figure 2: Using GenAI to generate synthetic visual data to avoid invading individual's privacy rights by using synthetic data: This image represents a synthetic image generated using DALL.E from the prompt "A realistic illustration representing a minor Instagram influencer under $6$ years old" on 13/07/2024.
  • Figure 3: Using ChatGPT to re-style a reference.
  • Figure 4: Using ChatGPT to debug codes in Overleaf.
  • Figure 6: This figure represents a screenshot of a ChatGPT account settings window. Users can personalise their account settings to stop this model from memorising their feeds, and personal preferences.
  • ...and 1 more figures