AI in Education: Balancing Innovation and Ethics with Value Sensitive Design

The views expressed are those of the author and do not necessarily reflect the views of ASPA as an organization.

By Mauricio Covarrubias
July 12, 2024

In this column, we explore the integration of Value Sensitive Design (VSD) into educational care, focusing on the implementation of artificial intelligence (AI) algorithms. VSD is an approach that seeks to incorporate human and social values into the technological design process, recognizing that technology is not neutral and its impacts on society should align with ethical and social values. The application of AI in education presents both opportunities and challenges, and this column examines how VSD can guide this integration to ensure these technologies enhance the educational experience while respecting fundamental educational values.

What is Value Sensitive Design?

VSD is a normative and analytical framework that focuses on incorporating human values into technology design. According to this approach, design refers not only to the final product but also to the planning and creation process. A design can be evaluated in terms of its function, ease of use, safety and how it respects and promotes values such as justice, dignity and privacy. For more detailed information on this concept, you can refer to the book Value Sensitive Design: Shaping Technology with Moral Imagination by Friedman & Hendry.

In the educational context, VSD involves considering how technological tools can support educational goals and respect the values of students, teachers and the educational community. The integration of AI algorithms in education should be evaluated not only for their technical efficiency but also for their impact on equity, accessibility and respect for student privacy. More insights on this can be found in the book Responsible Innovation, specifically the chapter on Value Sensitive Design and Responsible Innovation by Van den Hoven.

Implementing AI Algorithms in Education

AI algorithms have the potential to transform education in multiple ways. They can personalize learning, identify areas where students need more support and automate administrative tasks to allow teachers to focus more on teaching. Nevertheless, implementing these technologies also raises significant ethical and practical issues.

Personalized Learning. One of the greatest promises of AI in education is personalized learning. Algorithms can analyze student performance data and adapt educational content to the individual needs of each student. This can help students progress at their own pace and receive the specific support they need. Even so, it is crucial that this process is carried out in a way that respects student privacy and does not label them harmfully. VSD can guide the design of these systems to ensure that data is used ethically and that students have control over their information. For more information on ethical guidelines for trustworthy AI, refer to the European Commission’s Ethics Guidelines for Trustworthy AI.

Assessment and Feedback. AI algorithms can provide instant assessments and feedback. This can be especially useful in areas like mathematics and science, where incorrect answers can quickly identify concepts that students do not understand. Nonetheless, excessive reliance on automated assessment can dehumanize the educational process. It is important that AI systems complement, rather than replace, human interaction. Values of dignity and respect should guide the design to ensure that students do not feel reduced to mere data points.

Accessibility and Equity. AI can make education more accessible for students with disabilities by offering adaptive tools and accessible content. For example, voice recognition systems can help students with motor impairments interact with educational materials. However, it is essential that these technologies do not exacerbate existing inequalities. Institutions must ensure that all students have access to the necessary technology and that algorithms do not perpetuate biases that could harm certain groups of students.

Prospective and Retrospective Evaluation

VSD involves both prospective and retrospective evaluation. Prospective evaluation refers to analyzing how we should design and implement AI in educational contexts in the future. This includes considering ethical values from the beginning of the design process. Retrospective evaluation, on the other hand, involves analyzing how existing implementations of AI in education have affected students and the educational community. This analysis can reveal areas where adjustments are needed to better align technology with educational values.

Final Thoughts

The implementation of AI algorithms in education must align with the ethical and policy principles established by entities such as the European Union, which promote the responsible use of technology.  This includes ensuring transparency in how data is used, ensuring equity in access to technology, and protecting student privacy.

Value Sensitive Design provides a crucial framework for guiding the implementation of AI algorithms in educational care. By considering and respecting values such as justice, dignity and privacy, we can ensure that these technologies not only improve the efficiency and personalization of learning but also contribute positively to the well-being of students and the educational community. Prospective and retrospective evaluation, as well as the involvement of multiple perspectives and disciplines, are essential for developing a more equitable and humane educational system in the digital age.

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Author: Mauricio Covarrubias is Professor at the National Institute of Public Administration in Mexico.  He is co-founder of the International Academy of Political-Administrative Sciences (IAPAS).  He is the founder and Editor of the International Journal of Studies on Educational Systems (RIESED). Member of the National System of Researchers of CONACYT.  He received his Ph.D. from the National Autonomous University of Mexico.  He can be reached at [email protected] and followed on Twitter @OMCovarrubias

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