Using AI and ML technologies in the classroom
Not only are artificial intelligence (AI) and machine learning (ML) gaining traction in Silicon Valley, but they are also making an appearance in our classrooms. Given that we are at the vanguard of technological advancement, it is clear that tools using AI and ML have the power to completely transform the educational system. These cutting-edge technologies promise to improve learning outcomes and completely reimagine the educational experience for both students and teachers, from individualized learning experiences to predictive analytics.
The ethical issues that come with incorporating AI and ML technologies into educational settings, however, cannot be disregarded in the middle of this wave of change. Even while the possibilities are exciting, we must proceed cautiously and consider the moral ramifications that emerge when data and algorithms come together. Let’s examine these consequences in more detail:
Discrimination and Bias: Taking Issue with the Status Quo
Algorithms used in AI and ML work on the basis of data pattern recognition. Nevertheless, these algorithms have the potential to maintain systematic injustices and discrimination if the training data contains biases. Research reveals that students from vulnerable populations are disadvantaged by AI-driven evaluation systems that often display gender and racial prejudices. One study, which was published in the Journal of Educational Computing Research, discovered that, regardless of the quality of the material, an AI-driven grading system routinely gave essays authored by female students a worse rating than those submitted by their male peers. These differences highlight how crucial bias mitigation techniques are to the creation and use of AI in educational settings.
Data Privacy: Protecting Learning Resources
Large-scale student data repositories are often required in order to use AI and ML techniques. According to recent studies, 89% of educational establishments gather and retain sensitive student information, such as personal identifiers and academic achievement records. It is morally and legally required to ensure the security and privacy of this data.
Imagine a situation where a teaching platform driven by AI gathers and examines students’ internet behavior and browser history to customize their tutoring sessions. Even if the goal is to tailor education, there is a significant risk of data breaches or abuse. Only 25% of instructors believe they have received sufficient training on data privacy procedures, according to a poll. This underscores the critical need for thorough privacy measures in educational AI applications.
Managing Ethical Issues for Conscientious Application
A number of guiding concepts come into play while negotiating the moral minefield that is the integration of AI and ML in education:
Emphasizing Data Privacy: To prevent unwanted access to or abuse of student information, educators must have strong data privacy policies in place, such as encryption techniques and limited access restrictions.
Mitigating Biases: To find and fix discriminatory trends in training datasets, AI developers should use inclusive data sampling techniques and bias detection algorithms.
Maintaining Teacher-Student Relationships: Educators need to emphasize the vital role that human instructors play in providing mentoring, direction, and emotional support to students along their educational journey, even as they embrace technological developments.
Ensuring Transparency: When it comes to the application of AI and ML tools, educational institutions and schools need to promote candid communication and openness. This will give educators and students a thorough understanding of how data is used, how algorithms make decisions, and how this could affect student learning.
