An Analysis of the Use of AI in Education; An Ethical Analysis of Jill Watson

In my technical paper, I discussed how artificial intelligence (AI) is transforming education through tools like adaptive learning platforms, automated grading systems, and AI-powered tutoring bots. These technologies, like Knewton, Gradescope, and Jill Watson, help enhance the educational experience by personalizing instruction, streamlining grading, and improving academic support availability. My STS research paper focuses on one of these technologies, Jill Watson, and uses the Actor-Network Theory (ANT) to discuss how the AI teaching assistant is restructuring the human-technology educational network at Georgia Tech. Together, these papers discuss how AI functions within educational systems, and how its implementation can lead to unintended ethical issues that must be carefully navigated.

My technical paper analyzes several AI applications within education. Adaptive learning platforms utilize machine learning and knowledge graphs to deliver personalized content based on student performance. Automated grading systems combine optical character recognition (OCR), natural language processing (NLP), and clustering algorithms to expedite feedback. AI-powered chatbots, such as Jill Watson, use retrieval-based and generative models to provide real-time academic assistance. These tools have demonstrated the ability to improve student learning, enhance grading speed, and reduce workload for human instructors.

My STS paper focuses specifically on the ethical case of Jill Watson using the Actor-Network Theory. ANT provides a framework for analyzing how human and non-human actors, like students, instructors, developers, AI systems, and a messaging platform like Piazza, create and reshape the educational environment. My paper argues that although Jill Watson reduced TA workload and increased student engagement, its lack of transparency, biased responses due to poor training data, and unclear lines of accountability introduced trust issues causing instability within the network. This paper demonstrates how the implementation of educational AI is not just a technical challenge but an ethical one as well.

Working on both papers gave me a better and more conclusive understanding of the role of AI in education. The technical report allowed me to learn and understand the practical and successful use of AI in education, while the STS paper allowed me to dive into the broader ethical implications of one of those technologies. This duality has shown me the importance of analyzing technical inventions to better understand the unintended ethical consequences. In the future, I hope to approach complex technical projects not just with an engineering mindset, but also an awareness of the broader ethical implications that may arise.

School of Engineering and Applied Science

Bachelor of Science in Computer Science

Technical Advisor: Rosanne Vrugtman

STS Advisor: Kathryn Webb-Destefano

Technical Team Members: Jacob Huynh