#United States #Generative AI #Phoenix #Predictive Analytics #University of Phoenix

Enhancing Online Learning: A New AI Framework

In a recent groundbreaking publication, scholars from the University of Phoenix's College of Doctoral Studies have proposed an innovative framework aimed at shaping the future of online higher education. Authored by Pamayla E. Darbyshire, DHA, MSN/CNS, and Carl Beitsayadeh, MS, this article, titled "Enhancing Student Success through GAI and Predictive Analytics," highlights a 16-stage model that aims to integrate generative artificial intelligence (GAI) with predictive analytics to create a more human-centered approach to support online learners.

The Need for Change in Higher Education

As educational institutions continue their evaluations of AI tools, there's an emerging trend to treat predictive analytics and generative AI separately. Darbyshire and Beitsayadeh's research addresses this fragmentation by emphasizing that these technologies can effectively work in tandem. By doing so, they propose that institutions can create a cohesive ecosystem where data-driven insights, educator judgment, and ethical governance collectively enhance the learning experience.

Darbyshire expresses that "AI in education should begin with the learner experience," stressing the importance of recognition in understanding students' needs. The framework aims to replicate human connection in education rather than replace it, ensuring that educators can respond to students with context and clarity.

A Comprehensive Framework

The proposed 16-stage framework presents a systematic strategy for integrating GAI and predictive analytics into online education. Here are some core components the authors elaborate on:

• - Data Ingestion and Predictive Modeling: This involves collecting data from various institutional sources, including student information and learning management systems, to identify patterns indicating a student's need for support.
• - Generative Feedback: Mechanisms through which students receive personalized messages, resource recommendations, and study plans can be generated based on the insights gained from predictive analysis.
• - Instructor Judgment: A central aspect of the model underscores the importance of educator input in interpreting data and providing personalized support to students, maintaining the essential human element in education.

Darbyshire and Beitsayadeh highlight that educators can utilize predictive analytics to detect signs of disengagement or declining performance in students. Once identified, GAI can help craft timely and tailored interventions.

Ethical Considerations and Implementation

The article also discusses the necessary precautions for responsibly implementing these AI tools in education. Institutions are urged to consider secure data systems, transparent policies for data access, and training for faculty to effectively interpret and utilize AI-generated insights. The safeguarding of student trust and the monitoring of biases are essential steps included in the article's recommendations.

From establishing governance policies for AI usage to creating audits for fairness and transparency, the authors provide a clear roadmap for institutions aiming to build these AI-supported systems.

Summary and Impact

This collaborative piece contributes to a growing body of research focusing on the impactful role of technologies like GAI and predictive analytics in the online learning landscape. As universities work towards adopting these advancements, the framework detailed by the University of Phoenix stands as a vital resource for institutions seeking to improve student success and educational outcomes in a rapidly changing world.

Both Darbyshire and Beitsayadeh bring extensive experience and insight into the field, combining healthcare and engineering backgrounds with data analysis and educational strategy. Their contributions emphasize the importance of a research-driven approach to navigating the complex intersections between technology and education, positioning themselves as thought leaders in the ongoing conversation about AI's role in academic settings. As the University of Phoenix continues to evolve its methodology in higher education, the implications of their research may reverberate far beyond their institution, influencing practices on a global scale.