The paper, authored by Christina Neider, Ed.D., Vice Provost of Colleges, and Marc Booker, Ph.D., Vice Provost for Strategy, describes the university’s institutional framework for embedding generative AI across academic programs, student learning experiences, and the processes, policies, and workflows that keep a large online institution running. Rather than treating AI as a single tool to be bolted onto existing systems, the authors present a three-pillar structure intended to balance innovation, ethical use, and learner outcomes. The framework is explicitly aligned with the needs of working adult learners, a population the university serves almost exclusively, and with the evolving expectations of employers who increasingly want graduates who can use AI effectively and responsibly.

The first pillar focuses on embedding AI into programs and course content. The authors argue that learners now need three interlocking competencies: foundational knowledge of how generative AI works, practical skills for applying it in professional contexts, and the critical judgment required to evaluate AI-generated output. To build these competencies, the university has made a generative AI platform available to all students and offers an elective introductory course on generative AI. A set of brief, self-paced AI essentials modules has been embedded in the learning management system, allowing students across every program and course to develop AI literacy and confidence at their own pace. The Phoenix Success Series, the introductory course sequence taken by first-year students, will introduce generative AI as a support tool for learning beginning in 2026.

The scale of the curriculum work described in the paper is notable. The university has deployed an interactive tool for creating Socratic dialogues in five courses, reaching approximately 1,300 students across disciplines through scenario-based practice that is reviewed by faculty. This approach reflects a deliberate pedagogical choice: rather than letting students passively consume AI output, the Socratic format requires them to engage in structured dialogue, defend reasoning, and refine understanding, with human instructors retaining oversight of the exercise. In parallel, more than 20 degree programs have been strategically selected for curriculum revision, with at least two AI-integrated summative assessments planned in each program by March 2027. Summative assessments, which evaluate what students have learned at the end of a course, represent a high-stakes point of integration, signaling that AI competence is being treated as a graduation-level outcome rather than an optional enrichment.

The second pillar addresses the use of AI tools to enhance the learning experience itself. Here the emphasis shifts from what is taught to how students and faculty navigate their academic environment. The paper describes a range of use cases, some fully available and others in pilot stages, spanning every level of the institution. Students have access to general AI assistance, while academic services questions can be answered within the classroom environment itself. Faculty receive their own AI assistance tailored to their instructional needs. Perhaps most significantly, course-specific AI assistants support students with content and curriculum inquiries directly connected to the courses in which they are enrolled, providing a layer of just-in-time academic help that sits between broad student services and individual faculty office hours.

This tiered support architecture is one of the more technically interesting aspects of the framework. By connecting AI assistants to specific course content, the university reduces the risk of generic or inaccurate responses and grounds the assistance in the actual materials students are studying. The distinction between general assistance, service-oriented support, and course-connected help illustrates how AI deployment in education benefits from deliberate layering rather than a single monolithic chatbot. It also reflects a broader principle in human-centered AI design: tools should meet users where they are, in the context of their immediate task, rather than requiring them to seek help elsewhere.

The third pillar extends AI integration beyond students to faculty, staff, policies, training, and operational practices. The authors emphasize that meaningful implementation cannot stop at the classroom door. The university is applying AI to improve both academic and business efficiencies, and the paper argues that ethical AI use requires institutions to translate policy into practice. A philosophy on generative AI established in 2023 has informed subsequent policy development, faculty guidance, and the creation of AI literacy resources designed to support responsible use across the institution. The university’s Center for AI Resources, described in the paper as foundational to this effort, offers resources for students, faculty, and staff and has reached more than 90,000 users to date.

The framework’s intellectual grounding is the Digital Education Council AI Literacy Framework, a human-centered model that the authors cite as informing the university’s commitment to foundational AI knowledge, practical skills, and responsible-use habits. This alignment matters because it situates the university’s approach within an emerging international consensus about what AI literacy means. Literacy, in this view, is not merely technical fluency. It encompasses the judgment to know when AI output should be verified, the ethical awareness to recognize bias and misuse, and the habit of treating AI as a collaborator whose contributions must be critically assessed. Dr. Booker frames the institutional imperative in similarly holistic terms, noting that the rapid evolution of generative AI is forcing institutions to transform and that the answer lies in integrating thoughtfully across governance, tools, processes, and curriculum, with policy strengthening cross-departmental connections as well as operational and academic functions.

The workforce dimension runs throughout the paper and gives the framework much of its urgency. As generative AI reshapes work across industries, employers increasingly expect candidates who can use these tools productively while understanding their limitations. Dr. Neider argues that working adult learners need meaningful opportunities to practice, improve outcomes, and understand the ethical considerations that accompany AI use, and that AI literacy requires human judgment as well as practice. By embedding AI throughout the academic experience rather than confining it to a single course, the university aims to help students develop the skills and responsible-use habits that employers increasingly value. For adult learners who are often studying while working, the immediate applicability of these skills is a practical benefit as much as an academic one.

What emerges from the white paper is less a prescription than a proof of concept: a demonstration that AI integration can be organized, measured, and scaled when treated as an institution-wide undertaking rather than a collection of disconnected experiments. The three pillars, curriculum, learning experience, and institutional process, are designed to reinforce one another, so that policy informs practice, practice informs pedagogy, and pedagogy serves the ultimate goal of student success. For a sector still wrestling with questions of academic integrity, equity of access, and the pace of technological change, the paper offers a concrete data point in an ongoing experiment. Whether the model transfers to institutions with different missions and resources remains an open question, but the underlying logic, that AI adoption must be holistic, human-centered, and tied to real workforce demands, is likely to shape the conversation well beyond a single university.

Subject of Research: Institutional framework for integrating generative artificial intelligence in higher education

Article Title: Three-pillar framework for integrating AI in higher education outlined in University of Phoenix white paper

Article References: Three-pillar framework for integrating AI in higher education outlined in University of Phoenix white paper. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: generative AI, higher education, AI literacy, University of Phoenix, curriculum design, working adult learners, educational technology, AI ethics, institutional policy, workforce readiness, Digital Education Council, white paper

The study, titled “Strategic Integration or Skill Compensation? Understanding GenAI Use in Online Higher Education,” was published on June 30, 2026, in the journal Artificial Intelligence in Education, appearing in volume 2, issue 1, on pages 205 through 223. It was conducted by Jessica Sylvester, Melinda Kulick and Leonidas Maganares, all holding doctorates of education, along with Stella Smith, PhD, associate university research chair. All four are fellows or scholars affiliated with the university’s Center for Educational and Instructional Technology Research, known as CEITR, which is housed within the University of Phoenix College of Doctoral Studies and supports research on teaching, learning, educational technology and institutional practice.

The research team surveyed 491 non-traditional undergraduate students enrolled in online general education courses at the university. Non-traditional students, broadly defined, are typically older than the conventional college-age cohort and often balance coursework with jobs, family obligations and other responsibilities, making them a population of particular interest as universities grapple with how artificial intelligence is reshaping learning. Of the 491 students surveyed, 43.2 percent, or 212 students, reported using generative AI tools in their coursework, a figure that indicates the technology has moved well past the novelty stage and into routine academic life for nearly half of the online learners surveyed.

To understand what drove that use, the researchers turned to multiple regression analysis, a statistical technique that allows investigators to assess the independent contribution of several predictor variables to a single outcome while holding the others constant. The analysis identified exactly two statistically significant predictors of how frequently students used generative AI. The first was grade point average: students with stronger academic performance reported using generative AI tools more often. The second was technology confidence: students who expressed greater confidence in their own technical abilities also reported more frequent engagement with the tools. Both associations point in a direction that challenges the compensation narrative that has dominated much of the public conversation about AI in higher education.

Just as revealing was what did not predict use. Confidence in reading, writing and mathematics, the core academic skills that many observers assumed would push struggling students toward AI assistance, showed no statistically significant relationship with how often students turned to generative tools. Nor did time pressure: the number of hours students spent studying, working or meeting family responsibilities was not significantly related to generative AI use. In other words, neither a deficit in foundational academic skills nor a crunch on available time, the two most commonly cited explanations for why students might lean on AI, emerged as a meaningful driver of behavior in this sample.

The performance data among users add further texture to the picture. Among the 212 students who reported using generative AI in their coursework, 91.1 percent held a GPA of 3.0 or higher, and 68.9 percent earned a perfect 4.0 grade in the specific course in which they reported using the technology. Whatever generative AI is doing in the lives of these learners, the study’s authors argue, the tools appear to be concentrated among students who are already performing well and who feel comfortable navigating digital environments, rather than among those who are falling behind.

“These findings complicate the assumption that students primarily turn to generative AI because they are struggling academically or simply looking to save time,” said Jessica Sylvester, senior manager of College Operations at University of Phoenix and lead author of the study. “Instead, we found GenAI use associated with stronger academic performance and digital confidence. That points to an important opportunity for higher education to help all learners develop the digital fluency, critical judgment and ethical awareness needed to engage with AI effectively.” Her remarks frame the results less as a celebration of AI adoption than as a signal that institutions may need to actively teach the skills that make AI use productive rather than substitutive.

Open-ended survey responses from the 212 students who reported using generative AI offered a more granular view of how the technology is actually woven into coursework. Three themes dominated. The most prominent was using AI to improve understanding and clarify complex concepts, suggesting that many students treat the tools as a personalized explainer rather than a text generator. The second theme cast generative AI as a “thinking partner,” a learning aid for brainstorming and organizing ideas before or during the writing process. The third was efficiency: students reported that the tools helped them work faster and be more productive on academic tasks. Taken together, the qualitative responses sketch a portrait of AI as an amplifier of existing study strategies rather than a replacement for them, at least among these high-performing online learners.

The students themselves were not uncritical enthusiasts. Alongside their enthusiasm, respondents raised concerns about the accuracy and originality of AI-generated content, worried about preserving their individual academic voice, and expressed uncertainty about what constitutes appropriate use under their institutions’ academic integrity policies. That last concern is a recurring tension in higher education, where policies on generative AI have often lagged behind adoption, leaving students to navigate ambiguous rules on their own. The researchers argue that this uncertainty itself is a problem worth solving, and that the findings support a shift away from approaches centered primarily on restricting AI and toward clearer guidance that cultivates generative AI literacy, critical evaluation skills, and transparent and ethical use.

The study carries specific practical recommendations for institutions. The authors suggest incorporating AI literacy across academic disciplines rather than confining it to technology courses, designing assignments that encourage students to critique and reflect on AI-generated information, and providing consistent institutional guidance about acceptable use. The rationale is straightforward: if the students most likely to engage with generative AI are those with the strongest grades and the greatest technology confidence, then leaving AI skills to develop informally risks widening a digital fluency gap between confident and hesitant learners, precisely the outcome an equitable higher education system would want to avoid.

The authors are careful to flag the limits of what their data can show. The research used a cross-sectional, correlational design and drew a non-probability sample from a single online university, which means the findings cannot establish that generative AI use causes stronger academic performance, and they should not be generalized to all college students. It is equally plausible that higher-performing, more tech-confident students are simply more willing to adopt the tools, or that the direction of the relationship runs the other way, or that unmeasured factors influence both use and performance. To untangle those possibilities, the authors recommend longitudinal and multi-institutional studies that track how patterns of generative AI use evolve over time and whether AI-supported learning affects longer-term academic outcomes.

Even with those caveats, the study arrives at a consequential moment. Universities worldwide are revising integrity policies, redesigning assessments and debating the role of AI in classrooms as adoption accelerates among students of every description. Evidence that frequent use among adult online learners is associated with stronger rather than weaker performance cuts against both alarmist bans and complacent embrace, and instead supports the more demanding path the authors propose: teaching students to use these tools thoughtfully, critically and ethically. Sylvester and Kulick previously presented findings from this research at the 2025 Association for Educational Communications and Technology International Convention, and a summary of that presentation, “Transforming Higher Education: Harnessing Generative AI for Student Engagement,” was subsequently published in The Journal of Applied Instructional Design, reflecting the team’s sustained effort to move the conversation about generative AI from panic and prohibition toward evidence-informed pedagogy. As the technology continues to evolve, studies of this kind, grounded in the actual behaviors of the students most affected, will be essential to ensuring that institutional policy keeps pace with classroom reality.

Subject of Research: Generative AI use and its association with academic performance and technology confidence among non-traditional undergraduate students in online higher education

Article Title: University of Phoenix study finds GenAI use among online adult learners associated with academic performance and technology confidence

Article References: University of Phoenix study finds GenAI use among online adult learners associated with academic performance and technology confidence. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: generative AI, online education, adult learners, academic performance, technology confidence, AI literacy, academic integrity, higher education, non-traditional students, University of Phoenix, ChatGPT, educational technology