New University of Phoenix research outlines human-centered AI framework for online student success

Published in International Journal for Educational Media and Technology, the conceptual paper proposes a 16-stage model connecting generative AI, predictive analytics, faculty judgment and ethical governance in online higher education.

PHOENIX, June 19, 2026 /PRNewswire/ -- University of Phoenix College of Doctoral Studies scholars Pamayla E. Darbyshire, DHA, MSN/CNS, and Carl Beitsayadeh, MS, have published the article, "Enhancing Student Success through GAI and Predictive Analytics," in International Journal for Educational Media and Technology, an issue that featured refereed papers originally presented at the 2025 Teaching, Colleges, and Community Worldwide Online Conference (TCC). The conceptual paper proposes a theoretically grounded 16-stage framework for integrating generative artificial intelligence and predictive analytics into a unified, closed-loop support system for online higher education.

As colleges and universities continue evaluating AI-enabled learning tools, implementations might treat predictive analytics and generative AI as separate technologies. Darbyshire and Beitsayadeh's article addresses that gap by outlining how predictive insights, generative feedback, educator judgment and institutional governance can work together as an adaptive socio-technical ecosystem to support online learners.

"AI in education should begin with the learner experience," said Darbyshire. "This framework brings generative AI and predictive analytics together in a way that supports earlier recognition of student needs while keeping faculty judgment and ethical oversight at the center. For online learners, timely support matters. The goal is not to replace the human relationship in learning, but to help educators respond with greater context, clarity and care."

A framework for AI-enhanced student support

The article proposes a model designed to help institutions think more systematically about AI-enhanced student support in online higher education. Drawing on systems theory and the learning analytics cycle, the framework illustrates how data ingestion, predictive modeling, generative feedback and educator judgment can function together in a continuous cycle.

Key contributions of the article include:

• A 16-stage integration framework for connecting generative AI and predictive analytics in online higher education
• A closed-loop model organized around data and modeling, risk-aligned interventions, monitoring and feedback, and institutional refinement
• A human-centered approach that positions instructor judgment as a core interpretive layer
• Practical considerations related to data infrastructure, interoperability, faculty development and governance
• Ethical safeguards related to transparency, fairness, bias monitoring, student trust and human discretion

The authors note that predictive analytics may help institutions identify learner support needs earlier, while generative AI may help produce more timely and personalized feedback, study guidance or resource recommendations. The framework emphasizes that those capabilities should be guided by educator review, institutional policies and ongoing evaluation.

How predictive analytics and generative AI can work together

In the proposed framework, institutional data systems such as student information systems, learning management systems and analytics platforms help surface patterns that may indicate a student needs additional support. Predictive models can identify signals such as disengagement, late submissions or declining performance and translate those signals into risk tiers.

Generative AI can then support tailored interventions, including personalized messages, formative quizzes, resource recommendations or study plans. Faculty review and contextualize those outputs, helping ensure that AI-enabled support reflects both data-informed insight and human understanding of the learner's situation.

"Much of the current conversation treats predictive analytics and generative AI as separate technologies," said Beitsayadeh. "This framework brings them together within a single adaptive system, where data-informed insights, AI-enabled support, faculty judgment, and institutional oversight operate as interconnected parts of a continuous improvement cycle."

Responsible AI implementation in online higher education

The article emphasizes that responsible AI implementation requires more than adopting new technology. Institutions must consider secure and interoperable data systems, clear policies for data access, audit trails, faculty development and governance structures that evaluate accuracy, equity and alignment with institutional goals.

The article identifies practical implications for institutions considering AI-enabled student support systems, including:

• Developing governance policies for AI use, data access and model evaluation
• Preparing faculty to interpret predictive analytics and AI-generated recommendations
• Monitoring systems for bias, fairness and unintended consequences
• Designing AI tools that support, rather than replace, instructor judgment
• Building feedback loops that allow institutions to refine interventions over time

About AI research at University of Phoenix

The authors are affiliated with the University of Phoenix College of Doctoral Studies' Center for Educational and Instructional Technology Research (CEITR), which studies how emerging technologies—including artificial intelligence—are reshaping teaching, learning, and research practices in digital learning environments.

The publication contributes to a growing body of scholarship examining generative AI in higher education, predictive analytics, digital learning ecosystems, faculty development and responsible AI-enabled student support.

About the authors

Pamayla E. Darbyshire, DHA, MSN/CNS, is a research fellow in CEITR. She has worked in nursing for over 45 years, including over 15 years of extensive contributions in the U.S. Air Force Nurse Corps, and is an active member and contributor in professional associations including Sigma Theta Tau International Honour Society of Nursing, Association of perioperative Registered Nurses (AORN) National Research Committee, and Case Management Society of America (CSMA). She holds a Master of Science in Nursing (Clinical Nurse Specialist, Ed.) and earned her doctorate in health administration from University of Phoenix. Darbyshire often serves as a peer reviewer for numerous international scientific journals and has presented at multiple virtual conferences with global audiences. Her research interests include case management, chronic disease, neurodiversity, and the application of AI in higher education. 

Carl Beitsayadeh, MS, is a University of Phoenix faculty member and research fellow with CEITR. Combining a professional background in mechanical engineering with expertise in quantitative data analysis, statistical modeling, and algorithm design, he has published and presented internationally across several fields, including measurement and methodology, AI in higher education, technology-enhanced education, neuroscience-informed learning, systems theory, genomics, and heat transfer. His current scholarship focuses on interdisciplinary applications of systems thinking, quantitative methods, and measurement frameworks to build conceptual models bridging theory and practice across scientific and educational domains.

About the College of Doctoral Studies

University of Phoenix's College of Doctoral Studies focuses on today's challenging business and organizational needs, from addressing critical social issues to developing solutions to accelerate community building and industry growth. The College's research program is built around the Scholar, Practitioner, Leader Model which puts students in the center of the Doctoral Education Ecosystem® with experts, resources and tools to help prepare them to be a leader in their organization, industry and community. Through this program, students and researchers work with organizations to conduct research that can be applied in the workplace in real time.

About University of Phoenix 

University of Phoenix is Built for Real Life. 50 Years Strong. The University innovates to help working adults enhance their careers and develop skills in a rapidly changing world through flexible online learning, relevant courses, academic AI pillars, and skills-mapped curriculum for associate, bachelor's and master's degree programs. Active students and alumni have access to Career Services for Life^® resources including career guidance and tools. For more information, visit phoenix.edu.

SOURCE University of Phoenix