Ethics Series

As artificial intelligence revolutionizes education, it raises important ethical questions. The following scenarios explore pressing challenges surrounding the use of generative AI in educational settings, encouraging critical reflection on the responsible development and deployment of these powerful technologies. By engaging with these dilemmas, we aim to foster open dialogue and shape a future where AI enhances the transformative power of education while upholding its core values. 

Augmenting Independent Work


Lin is a diligent high school senior who moved to the US two years ago. While her English has improved greatly, writing essays is still difficult for her. A classmate introduces Lin to an AI tool that can refine her writing. Eager to improve, Lin uses the tool to edit her essays before turning them in.

Lin's grades rise as her writing becomes clearer and more articulate. She feels empowered being able to better express herself in English.

However, Lin's English teacher Mrs. Johnson notices the sudden improvement and accuses Lin of cheating after running her work through an AI detector. Lin explains she only used the tool to assist with editing, not to write the essays. But Mrs. Johnson insists Lin’s work is not genuine.

Lin now faces disciplinary action as a result of academic dishonesty.


Use cases like this one highlight the substantial benefits provided by generative AI tools. These tools extend beyond just supporting non-native speakers like Lin; they can be leveraged by anyone to make their writing more coherent, fluent, and articulate. However, just as access to a calculator does not negate the importance of learning foundational mathematics, developing the skills to produce quality writing remains crucial for learners. 

Transforming Intellectual Property


Taylor, a college sophomore, balances her demanding biology studies with a job to support her family. Unable to attend after-school study sessions, she finds herself struggling to grasp the material in Dr. Ellis's advanced biology course.

Seeking a solution, Taylor discovers an AI tool that allows her to upload files that are then converted into easily understood learning materials. She uses it to transform Dr. Ellis's intricate PowerPoints into clear, concise study guides. This innovation leads to a noticeable improvement in her grades, as she begins to understand the course content more deeply.

However, Dr. Ellis learns of Taylor's use of the AI tool on his materials and is troubled by the violation of his intellectual property rights. Despite Taylor's academic improvement and her intentions to better her understanding of the course, Dr. Ellis views her actions as a breach of trust and informs her that disciplinary action will be taken.


AI-powered learning tools can greatly enhance students' understanding of complex course materials, making education more accessible and effective. However, the use of these tools raises questions about intellectual property rights and the boundaries between transformative use and infringement.

Imitation of Creative Works


Jamie, an aspiring artist, attends a workshop hosted by Yang Xia, a renowned artist whose work reflects a deep understanding of the interplay between light and shadow. Jamie has long admired Xia's ability to capture emotion through brushwork and has been experimenting with generative AI to recreate similar effects.

After an enlightening presentation, Jamie excitedly shares artwork created with a popular AI art generative tool using prompts like "in the style of Yang Xia". Each piece is a homage, blending Jamie's ideas with the iconic style that Xia has spent years developing.

Xia, however, reacts with dismay, explaining that while imitation is flattering, the use of her name and style in AI prompts crosses a boundary. Xia has never consented to have her work used as training data for AI, and even amongst arguments of fair use, feels the AI generated work undermines the authenticity and individuality of her work.

Jamie is taken aback.  She loves the work she has been able to create with the help of generative AI technologies, but she now feels as though her work using AI is morally wrong.  Can machines imitate a style just like humans can?  When does it go too far?


Generative AI has opened up new avenues for artistic expression, allowing creators to explore and build upon the styles of others in innovative ways. However, this technology also raises ethical concerns about consent, attribution, and the value of individual artistic style.

Automation of Feedback


Dr. Alex Hartman, a professor at a large university, teaches multiple sections of a popular course, totaling over 400 students. To manage the extensive workload, Dr. Hartman integrates an AI assessment tool that provides instant formative feedback to students and compiles comprehensive reports on student performance for him.

Many students appreciate the AI's immediate feedback, as it highlights areas needing improvement and aids them in preparing for significant summative assessments. Dr. Hartman relies heavily on this AI-generated data to assign grades throughout the semester, praising its efficiency.

However, discord brews as a group of students raise concerns. They claim the AI inaccurately represents their work, occasionally adding erroneous details, which affects their grades negatively. Despite these complaints, Dr. Hartman continues to use the AI tool, arguing that no system is perfect and the overall benefits outweigh the drawbacks.


AI-powered assessment tools can revolutionize the way educators provide feedback and evaluate student performance, offering immediacy and efficiency in large-scale educational settings. However, the reliance on these tools also raises questions about accuracy, fairness, and the importance of human judgment in the assessment process.

Predicting Success


At Prestige University, the admissions team pilots an AI system designed to streamline the processing of thousands of applications. This tool, called Admit-Me-Not, employs complex algorithms to evaluate essays and predict student success based on historical data. The system swiftly becomes indispensable, lauded for its ability to handle massive workloads and identify candidates who are likely to excel.

However, as acceptance and rejection letters are dispatched, an investigative journalist reveals that Admit-Me-Not has a troubling flaw: it disproportionately recommends candidates from affluent ZIP codes, mirroring biases found in its training data. Prospective students from less privileged backgrounds, regardless of their achievements, find themselves at a disadvantage.

The revelation sparks outrage and a heated debate. Some argue that the AI is only perpetuating systemic biases under the guise of impartiality. Others defend the system, pointing to its overall accuracy and the impracticality of manually reviewing every application.

The university must now confront the ethical quagmire: Can they continue to use Admit-Me-Not despite its biases, or is it time to reevaluate the role of AI in shaping the future of young minds?


AI-driven admissions tools have the potential to make the application process more efficient and data-driven, but they also risk perpetuating and even amplifying existing biases and inequalities in education. As institutions increasingly rely on these tools, it is crucial to examine their impact on fairness, diversity, and access to education.