Although questions about academic dishonesty is often the primary issue raised when educators think about potential problems with generative AI in education, there are also other issues to be considered in the realms of ethics and privacy. A short summary of some of those topics, found in a Research Guide in the library at James Madison University, can be found below:
The ethical and societal implications of AI, especially in education, are numerous and complex. Below are some issues to consider:
Accessibility and Equity: On the one hand, AI can help make education more accessible and personalized, enabling students to learn at their own pace and providing teachers with tools to identify areas where students are struggling. It could also create opportunities for students in remote areas or those who cannot attend school due to health issues or disabilities. However, on the other hand, not all students and schools have equal access to the technology and infrastructure needed for AI-based education. This digital divide can exacerbate educational inequalities.
Data Privacy and Security: AI systems in education often rely on collecting and analyzing large amounts of data about students. This raises questions about how this data is stored, who has access to it, and how it is used. There are risks of breaches of privacy and potential misuse of data.
Bias and Fairness: Like all AI systems, educational AI can be subject to bias, depending on how it's trained and what data it's trained on. For instance, an AI tutoring system could potentially favor certain types of students over others, based on the data it was trained with. This could perpetuate existing biases and inequalities.
Teacher-Student Relationship: While AI can automate some tasks, it cannot replace the human interaction and emotional support provided by teachers. There are concerns that over-reliance on AI could erode the teacher-student relationship and the social skills students develop in the classroom.
Skill Development: As AI and automation become increasingly integrated into the workforce, there is a need to ensure education systems are adequately preparing students with the skills they will need for the future. This includes not just technical skills for working with AI, but also soft skills like critical thinking and creativity that AI is currently not able to replicate.
Transparency and Understanding: It's important for students, parents, and educators to understand how AI tools make decisions. Unfortunately, many AI systems are "black boxes" where the decision-making process is opaque. This can make it hard to trust the system or to challenge its decisions.
Regulation and Policy: Given all these issues, there is a clear need for regulation and policies to guide the use of AI in education. These regulations need to address issues like data privacy, transparency, and equity. However, policy-making in this area is complex and needs to strike a balance between protecting students and enabling innovation.
All these issues underline the need for an interdisciplinary approach to AI in education, incorporating not just technological expertise, but also input from educators, psychologists, sociologists, ethicists, and legal experts.
"Artificial Intelligence (AI) in Education." James Madison University Libraries. https://guides.lib.jmu.edu/AI-in-education/ethics#s-lg-box-31333737
Another issue with generative AI is a product of the data which is fed into the AI and on which it is trained. If an AI is fed biased data, it will produced biased output, which can then be exacerbated by that output being used to train other AI, resulting in an accelerating trend of biased material flooding the system. See how that can happen below: