Relational Teaching in the Age of AI: A Pilot Study of Learning Analytics and Student - Faculty Engagement
Type of Presentation
Paper
Location
C3380
Start Date
4-10-2026 10:00 AM
End Date
4-10-2026 10:30 AM
Abstract
The growing integration of artificial intelligence (AI) in higher education is transforming how student performance is measured and monitored. As predictive analytics and automated early alert systems become more common, important questions arise regarding their impact on relational teaching and the human-centered interactions that impact compliance, and meaningful engagement in academic settings. Methodology: This pilot study will evaluate graduate student's engagement with faculty within a university setting. A sample of 80–100 students will be recruited to ensure adequate statistical power for quantitative analysis. Data will be collected through a structured survey focusing on student - faculty interactions, interest in collaborative research projects, current opportunities for academic engagement, and suggestions for enhancing academic engagement on campus. These responses will be analyzed using AI-based learning analytics techniques to identify patterns, trends, and relationships in student engagement behaviours. The insights gained from this pilot will inform the development of a predictive AI model capable of recommending strategies to enhance academic engagement at a larger scale, which could be integrated into university administrative and student management systems to support evidence-based interventions. Results (anticipated): This research is expected to generate initial insights into factors that influence student faculty interactions and overall academic engagement. Preliminary analyses are anticipated to reveal trends in interaction frequency, student interest in collaborative projects, and actionable strategies for enhancing engagement. These findings will provide guidance for refining survey instruments and shaping future AI-based predictive models. Conclusion: This study provides a foundational step toward developing a scalable approach to support relational teaching in the Age of AI. By applying learning analytics to student engagement data, the study contributes to data-informed strategies that strengthen student-faculty collaboration and enhance academic engagement at the institutional level.
Identify Grant
Yes
Faculty / Staff Sponsor
Dr. Forhan Emdad
Relational Teaching in the Age of AI: A Pilot Study of Learning Analytics and Student - Faculty Engagement
C3380
The growing integration of artificial intelligence (AI) in higher education is transforming how student performance is measured and monitored. As predictive analytics and automated early alert systems become more common, important questions arise regarding their impact on relational teaching and the human-centered interactions that impact compliance, and meaningful engagement in academic settings. Methodology: This pilot study will evaluate graduate student's engagement with faculty within a university setting. A sample of 80–100 students will be recruited to ensure adequate statistical power for quantitative analysis. Data will be collected through a structured survey focusing on student - faculty interactions, interest in collaborative research projects, current opportunities for academic engagement, and suggestions for enhancing academic engagement on campus. These responses will be analyzed using AI-based learning analytics techniques to identify patterns, trends, and relationships in student engagement behaviours. The insights gained from this pilot will inform the development of a predictive AI model capable of recommending strategies to enhance academic engagement at a larger scale, which could be integrated into university administrative and student management systems to support evidence-based interventions. Results (anticipated): This research is expected to generate initial insights into factors that influence student faculty interactions and overall academic engagement. Preliminary analyses are anticipated to reveal trends in interaction frequency, student interest in collaborative projects, and actionable strategies for enhancing engagement. These findings will provide guidance for refining survey instruments and shaping future AI-based predictive models. Conclusion: This study provides a foundational step toward developing a scalable approach to support relational teaching in the Age of AI. By applying learning analytics to student engagement data, the study contributes to data-informed strategies that strengthen student-faculty collaboration and enhance academic engagement at the institutional level.