AI-Driven in Education: Pioneering the Future of Learning
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Date
2024
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Publisher
University of York
Abstract
Background
The research investigates how AI-driven models may affect admissions to UK universities, with
a specific emphasis on how these models may affect diversity, fairness perceptions, and the
effectiveness of the admissions process when compared to more conventional approaches. The
increasing use of AI technologies in a variety of fields, including education, and the pressing
need to comprehend how these technologies affect fairness and openness in admissions
procedures are the driving forces behind this work. The main study topic seeks to determine if AI
systems can reduce biases in admissions decisions and how they affect perceptions of fairness
across various demographic groups.
Methods
Using structured online surveys, data were collected from four groups, including students,
parents, academic staff, and professionals, to investigate these questions using a quantitative
methodology. Important variables including opinions about fairness, trust in AI systems, privacy
concerns, and demographic data were all intended to be included in the survey. This strategy
made it easier to gather statistically meaningful data, which enabled a thorough examination of
the answers and patterns across various groups in admissions decisions.
Results
Diverse viewpoints regarding AI-driven admissions systems were found in the study from the
key groups. Students had moderate confidence about these systems' fairness and expressed
worries about potential biases and data privacy. Parents were cautious, prioritising transparency
and trust in conventional admissions procedures. Professionals accepted AI's efficiency but were
dubious about its capacity to eliminate biases; they continued to look for additional proof.
Academic staff held similar opinions, acknowledging AI's potential to enhance admissions while
also raising concerns about its impact on diversity and fairness.
Discussion
These results hold importance for how AI will be going forward to be put into use in college
admissions going forward. To build confidence among, academic professionals' cautious position
highlights the need for additional improvement and transparency in AI systems. The research
also emphasises how crucial it is to include a variety of viewpoints in the implementation and
development of the AI-driven admissions process since the current approaches do not adequately
capture the complexities of effectiveness and fairness. Overall, this study emphasises the
necessity for ethical concerns and inclusive practices when the technology is used in admissions,
which adds insightful information to the ongoing conversation about AI in education.
Key Words
AI-driven admissions, fairness, diversity, bias, quantitative methodology, participants
perceptions, educational technology, UK universities.
Description
Keywords
AI In Education, Fairness in AI, AI-Driven Admission, Educational Technology, Data Privacy in Education, Bias Reduction in AI