Empirical Evaluation of an Ethical AI Framework for STEM Education
A Mixed-Methods Study of Privacy-Preserving Technologies and Learning Outcomes
DOI:
https://doi.org/10.32674/04054e51Keywords:
artificial intelligence in education, federated learning, blockchain technology, educational ethics, STEM pedagogy, data privacyAbstract
In this paper introduces a new ethical AI framework for undergraduate STEM courses, putting a strong focus on privacy, transparency, and accountability.
Our study involved 412 students across three universities over two semesters. Rather than relying solely on quantitative data, we also collected direct narratives and experiences from instructors and students through in-depth interviews and focus groups.
The results clearly show that responsible design of AI not only does not reduce system performance, but also significantly improves student engagement (35%), instructor acceptance (78%), and the performance gap between students is reduced by 40%—all while maintaining the model’s predictive accuracy at 89%.
This research demonstrates that AI can be designed to be both technically robust and ethically committed.
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