1. What is the focus of your action research?
The focus of this study is on applying AI-based student platforms to automate and improve admissions and academic advising functions in higher education. The research seeks to identify how such platforms can streamline tracking of applications, document handling, course guidance, and academic status monitoring.
2. Why is your study being conducted?
The purpose of this study is to examine the effectiveness of AI-driven student platforms in improving the efficiency of the admissions process and the quality of academic advising. This research will explore how these platforms impact student engagement, reduce administrative workload, and enhance decision-making for both students and advisors. This includes whether AI-based advising tools result in increased retention of students as a result of more accurate course recommendations, better prediction of challenges in academics, and real-time information on academic standing.
3. What is your basic research question?
This study seeks to answer the question: How does the implementation of an AI driven student platform influence the efficiency of admissions, the effectiveness of academic advising, and student retention rates in higher education?
4. What is your research design? Qualitative, quantitative both (mixed-methods) Why?
A mixed-methods research design will be used, incorporating both qualitative and quantitative approaches. The quantitative aspect of the study will focus on collecting statistical data related to admissions processing time, accuracy of advising recommendations, and student satisfaction levels. The qualitative component will involve gathering insights from students and advisors through interviews and focus groups to better understand their experiences with the platform. The mixed-methods approach is ideal because it allows for a comprehensive analysis, combining numerical data with personal perspectives to assess the platform’s overall impact.
5. What is the most appropriate type of data to collect?
To evaluate the effectiveness of the AI-driven platform, the study will collect both quantitative and qualitative data. Quantitative data will include student and advisor satisfaction survey results, admissions processing times before and after the implementation of the platform, the accuracy rates of AI-generated advising recommendations, and student engagement metrics such as frequency of usage and course selection decisions. Qualitative data will also be collected through focus groups and interviews to elicit personal experiences, feedback, and perceived benefits or challenges of the platform.
6. What measurement instruments will you employ?
A variety of measurement instruments will be employed to evaluate the effectiveness of the platform. Pre- and post-implementation surveys will be administered to measure student and advisor satisfaction. System-generated analytics will monitor the usage of the platform and measure its effect on admissions processing time and advising accuracy. Case studies will be
created to examine the experience of students using the platform for academic planning. Semi-structured interviews with admissions staff and academic advisors will provide further insight into how the platform influences their workflow and student interactions.
7. What is the focus of your literature review?
This literature review will consider how artificial intelligence fits into higher education, focusing particularly on student services, including admissions and advising. It will assess best practices of integrating AI with such functions as well as how students and faculty view AI-based platforms. A number of case studies on similar institutions and analyses of the resulting impacts on engagement, accuracy, and retention for these institutions are also part of this review.