AI-Driven in Education: Pioneering the Future of Learning

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.