What Nursing Programs Should Look for in Third-Party AI Partners

This article is adapted from the State of Nursing Education, the authoritative ATI series examining key issues affecting today’s academic nursing programs, deans and faculty. State of Nursing Education content reflects extensive research, evidence, and interviews, with perspectives from nursing industry leaders. To read more from the series, click here.

"Is AI integration necessary?"

If this question is still being pondered at your academic program, it already has fallen behind. AI isn’t a perfect technology, but the benefits of a well-maintained system to most nursing programs are simply too good to ignore.

In fact, they have the potential to affect the entire student journey.¹ More engaged nurse educators. More successful remediation. More precise assessments and performance diagnostics. Even the National League for Nursing sees the writing on the wall — their 2026 AI toolkit is a signal of their commitment.

“How do I integrate AI safely and successfully?”

That’s the question you need to ask. Not only because AI is a rapidly growing force in the healthcare industry, but also because nursing programs need to be crystal clear about their approach to artificial intelligence. Over 90% of nursing students want clear guidance on AI use, and generative content creation is still an unknown territory for many nurse educators.^2,3

Simply put, the way forward isn’t exactly obvious.

As such, nursing programs need real planning — with real steps and real third-party partners — to make sure they are capitalizing on this transformative technology.

“These steps ensure that AI benefits everyone,” says Beth Cusatis Phillips, PhD, RN, CNE, a former educator and program director who is now Strategic Nursing Advisor for ATI. “By taking an iterative approach and establishing clear guidelines around how AI should be used, institutions can ensure the technology is a net positive.”

Because a positive outcome isn't the only possibility. AI integrations can create value, but they can also create risk.

What Creates Value? What Creates Risk?

Effective strategies often carry a great deal of risk. They can do the job well, but they also require patience and guidance to succeed — or simply to avoid fallout. Meanwhile, such strong solutions might make things worse for everyone if they are unguided, unsupported, and open to security violations.

In this sense, the challenge isn't identifying whether AI has value. The challenge is understanding which systems create value and which ones introduce unnecessary risk. AI solutions don't succeed because they're new or innovative. They succeed because they're built on trusted content, supported by expert oversight, and aligned with intentional student outcomes.

That distinction is especially important in nursing education because inaccurate information, poor implementation, or overreliance on technology can have consequences far beyond the classroom. As programs evaluate AI partners, the goal should be simple: Maximize the benefits while minimizing the risks.

This checklist detailing 5 essentials for AI-powered learning systems is a good place to start.

A Purpose-Built AI Engine

A purpose-built AI engine does more than generate an in-the-moment response. Its information is rooted in the unique realities of nursing education, including NCLEX preparation, clinical judgment development, student remediation, and faculty workflows. Such nuance matters a great deal because the healthcare industry is not looking for generic solutions. It needs systems capable of supporting highly specialized educational outcomes.

What does this mean in practice? AI systems should bring a lot to the table:

• Full alignment with nursing standards, including NCLEX frameworks and clinical judgment expectations.
• Educator support tailored to nursing education workflows, which means more engagement from instructors.
• A direct connection between performance and meaningful intervention, so no student goes without the right support.
• Curated, specific learning support that adheres to nursing curriculum and nursing practice.
• Reinforced practice readiness through tools designed around real healthcare expectations and clinical competencies.

When AI systems like Claire AI^® are purpose-built for nursing education, they create more value. They deliver more relevant insights and provide better support for the goals of students, faculty and programs alike.

THE BOTTOM LINE: AI solutions for academic nursing should be designed specifically for nursing education, with support for NCLEX readiness, clinical judgment, and educator workflows.

Actionable, Data-Driven Insights

By itself, data can't do much.

Likewise, general-purpose LLMs and GPT integrations aren't capable of transforming nursing education without the right guidance and the right customizations. Data only becomes valuable to nursing programs when it helps students succeed and assists educators in making better decisions. At the same time, AI engines only become transformative to the education journey if they are designed specifically for nursing education.

That’s why the most effective AI tools do more than collect information. They work within a system that is built on decades of verified nursing content and pass rate analysis. They identify patterns, surface learning gaps, and help faculty understand where students need support most.

These capabilities allow programs to move beyond reactive interventions and take action before challenges become more significant barriers to success. As Bethany Gifford, MSN, RN, Nursing Instructor at Central Community College, explains, “Using the Engage® Series, we can see the outcomes of our students. We can view analytics and see what students are struggling with, what they're asking questions about, and tailor our lectures to create better outcomes for the NCLEX and our patients.”

In practice, keeping a close, forward-thinking eye on performance metrics pays off for nursing programs:

• Identifies at-risk students earlier and intervene before things get worse
• Recognizes learning trends across students, courses and cohorts
• Targets remediation efforts where they will have the greatest impact
• Adapts instruction in real time based on student performance and engagement
• Connects data to outcomes that improve NCLEX readiness and clinical preparation.

When systems are informed by years of nursing education data, they inspire meaningful action. When they inspire meaningful action, they become much more than reporting tools. They become the de facto source for the information needed to make faster, smarter, more effective decisions on behalf of their students.

THE BOTTOM LINE: AI should turn student data into actionable insights that support earlier intervention, targeted remediation, and stronger outcomes.

Human-in-the-Loop Oversight

Efficiency is only valuable if the output is reliable. Nursing education depends on accurate information, evidence-based curriculum, and consistent clinical standards. If an AI system produces unsupported recommendations, outdated content, or information that conflicts with current best practices, it becomes a liability rather than a resource.

That is why content quality should never be treated as an afterthought. Programs evaluating AI solutions should put it front and center:

• Drawn from evidence-based, trusted nursing resources, not unrestricted internet content
• Aligned with NCLEX and current evidence to support practice readiness
• Provides consistent, evidence-based guidance no matter the content or context
• Reduces the risk of misinformation through persistent expert validation
• Reinforces educational objectives instead of generating content with no direct purpose.

When faculty and students can trust the information being provided, AI becomes more than a productivity tool. It becomes a dependable extension of the education process, creating a stronger foundation for the engagement and interventions that drive student success.

THE BOTTOM LINE: AI should enhance faculty expertise, not replace it. Educators must remain in control of learning, assessment, and decision-making.

A Focus on Faculty Efficiency

Nursing faculty are being asked to do more with less.⁴ Between assessment development, remediation planning, faculty shortages, and student support, administrative responsibilities are competing with the work that matters most.

The right AI system should reduce that burden, giving educators more time to teach, mentor, and support student success. These systems are specifically designed to accelerate content creation and performance analysis, increasing faculty efficiency across the board. And it's no secret that faculty efficiency is essential to making time for student engagement and directly tied to faculty burnout.

Patty Knecht, PhD, RN, ANEF, Chief Nursing Officer for Ascend Learning, is a former nurse educator who understands the challenge inherent in finding time for more interaction with their students.

“Claire AI helps nurse educators focus more on building one-on-one relationships with students and guiding them through challenging topics,” she said. “It frees faculty to spend more time doing what they got into education to do.”

In practice, faculty-focused AI systems can impact multiple areas of the workflow, shrinking the size of work overall and opening new opportunities for students and teachers. These enhancements touch every part of the education journey:

• Reduce administrative workload so educators can spend more time on quality assessments and interventions
• Accelerate routine tasks and get back to valuable instruction time
• Expand student support with 24/7 access without increasing faculty burden
• Create more personalized instruction and more meaningful student engagement
• Refocus educator expertise on teaching, mentoring, and clinical preparation.

When AI improves efficiency without compromising quality, educators gain something even more valuable: the time necessary to devote greater attention to students. That makes the quality and reliability of the content behind AI of utmost importance.

THE BOTTOM LINE: AI should reduce administrative burden, streamline workflows, and give faculty more time to teach, mentor, and support students.

Verified, Evidence-Based Content

AI needs to be trusted. In nursing, AI needs to be backed by redundant verification and rigorous oversight.

Nursing education depends on accuracy, consistency, and evidence-based content. When an AI system draws from questionable sources or generates unsupported recommendations, it introduces uncertainty into a profession where reliability is essential. Consequently, nursing programs are seeking AI solutions that draw from trusted nursing education resources with alignment to industry standards and measurable educational outcomes.

When educators trust the information being delivered, they can focus less on verification and more on implementation. Reliable content creates a stronger foundation for identifying learning needs, recognizing performance patterns, and turning information into meaningful action, which is exactly where actionable insights begin.

THE BOTTOM LINE: AI systems should draw from trusted, evidence-based nursing content and make accuracy a nonnegotiable standard.

ATI and Making the Right Decisions on AI Integration

AI adoption is accelerating, but successful integration depends on making the right decisions. The best systems help programs improve learning, support faculty, and prepare students for today’s healthcare without sacrificing oversight, accuracy, or trust.

At the same time, learning and technology are becoming more mobile every single year, and integrating AI features into user-friendly platforms is the next logical step.

Kimberly White, PhD, MSN, RN, Director of West Virginia Wesleyan School of Nursing, is already seeing the change happen. “This generation of students learn completely differently,” she said. “They do not listen to lectures, so you need active involvement and easy access. They want the technology. You need Claire AI on your phone.”

The question is no longer whether AI belongs in nursing education. The question is whether your AI solution is built to support the outcomes that matter most. Is it purpose-built? Is it reliable? Is it engaging and mobile-friendly? ATI and Claire AI are ahead of the game, offering the leading solutions to AI integration.

Talk to an expert about what AI can do for your program.

References

1. Alrazeeni DM, Alharrasi M, Rony MKK, Biswas RK, Tama IJ, Halder CR, et al. Transforming nursing education with artificial intelligence: a systematic review (2010-2025). SAGE Open Nursing. 2026;12:23779608261424597. doi:10.1177/23779608261424597
   
2. Digital Education Council. Global AI Faculty Survey 2025. Published January 20, 2025. https://www.digitaleducationcouncil.com/post/digital-education-council-global-ai-faculty-survey
   
3. Hong M, Shin H, Kim SS, De Gagne JC. Nurse Educators' Perceptions and Experiences of Generative Artificial Intelligence: A Cross-Sectional Study Analysis. Computers, Informatics, Nursing. 2025;43(7):e01273. doi:10.1097/CIN.0000000000001273
   
4. American Association of Colleges of Nursing. AACN Faculty Shortage Fact Sheet. July 2026. https://www.aacnnursing.org/Portals/0/PDFs/Fact-Sheets/Faculty-Shortage-Factsheet.pdf