Generation AI

Predictive AI: The Art and Science of Student Engagement

Episode Summary

This episode of the Generation AI podcast delves into the evolution and application of predictive AI within higher education, focusing on enrollment predictions and marketing. Hosts Ardis Kadiu and Dr. JC Bonilla explore machine learning's roots, its distinction from generative AI, and its critical role in modeling prospective student behaviors. They discuss the transition from demographic to behavioral data for more accurate predictions, the importance of model tuning and validation, and the future of AI in personalizing student engagement through autonomous agents. The conversation highlights the blend of art and science in feature selection and the significance of adopting models that are understood and trusted by users.

Episode Notes

This episode of the Generation AI podcast delves into the evolution and application of predictive AI within higher education, focusing on enrollment predictions and marketing. Hosts Ardis Kadiu and Dr. JC Bonilla explore machine learning's roots, its distinction from generative AI, and its critical role in modeling prospective student behaviors. They discuss the transition from demographic to behavioral data for more accurate predictions, the importance of model tuning and validation, and the future of AI in personalizing student engagement through autonomous agents. The conversation highlights the blend of art and science in feature selection and the significance of adopting models that are understood and trusted by users.

Introduction to Predictive AI

• Hosts Ardis Kadiu and Dr. JC Bonilla delve into predictive AI's history and its application in higher education, focusing on enrollment and marketing.
• They discuss predictive AI's evolution from advanced analytics and machine learning (ML) to its current state.

Machine Learning Basics

• Explanation of machine learning as pattern recognition and its importance in predictive AI.
• The transition from demographic to behavioral data for improved predictions.

Model Building and Validation

• The process of model building, including feature selection, training, and validation.
• The importance of model tuning and validation for accurate predictions.

Behavioral Data in Predictive Models
Shift towards using behavioral data for more nuanced and accurate predictions.
How behavioral data surpasses demographic data in predicting student behaviors and interests.

Feature Engineering and Selection

• The art and science of selecting the right features for predictive models.
• Discussion on the significance of domain knowledge in feature selection.

Model Adoption and Interpretation

• Challenges in model adoption and the importance of model interpretability for end-users.
• How understanding and trust in the model's predictions are crucial for successful implementation.

Future of Predictive AI in Higher Education

• Insights into the future of predictive AI, focusing on personalized student engagement and autonomous agents.
• The potential of large language models and AI agents in transforming higher education marketing and enrollment strategies.

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