Examining the Ethical Concerns of AI Applications in Education and Public Acceptability

Abstract

The study focused on the ethical concerns about AI in education and social acceptability. The research context was also set in the rapid adoption of AI solutions (adaptive learning systems, machine grading or chatbots) whose effectiveness and personalization benefits were paradoxically at the epicenter or ethical critical challenges related to data privacy biases and transparency and cadre shifts. A quantitative research design was employed, in which data were collected from 470 respondents representing various stakeholder groups using a structured questionnaire. A total of 410 valid questionnaires were analyzed. It was centered on fundamental questions/considerations, like data privacy, algorithmic bias, transparency, the replacement of teachers and influence on student creativity/fostering critical thinking; accompanied with perceived benefits such as efficiency, personalized learning and lowering the burden from teachers. The additional statistical analysis revealed that privacy, fairness and transparency were the most influential factors of trust and public acceptability of AI in education, while perceived benefits had positive impacts on acceptance, provided that there were clear regulatory frameworks. The findings proposed that despite the general public positivity about AI game-changing educational potentials, trust and socially beneficial deployment warranting the presence of ethical safeguard measures, be accompanied by well-informed regulatory frameworks and transparent mechanisms. This research provides practical implications on how ethical concerns may impact public opinion and policy recommendation about the proper use of AI in education.

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