Coaching Thinking, Not Just Answers: Clinical Judgment in the Age of AI
by Dr. Christi Doherty DNP, RNC-OB, CNE, CHSE, CDP, Executive Director, Nursing & i-Human Patients | March 3, 2026
As nurse educators, we know our work is about more than covering content. We are shaping clinical judgment. Every class we teach, every remediation conversation we lead, and every practice question we review is an opportunity to strengthen how students think. At Kaplan, we are fortunate to share a common language for that thinking: the Decision Tree. Whether we are coaching live, working one-on-one, or guiding students in responsible AI use, our goal remains the same–help students apply a structured, safe, repeatable decision-making process.
The Foundation: Kaplan’s Decision Tree
Kaplan’s Decision Tree is built on three simple but powerful steps: Topic, Strategy, and Outcome. The simplicity is intentional. When students internalize these three steps, their thinking becomes more organized and far less reactive.
It always begins with the Topic. Before students jump to ABCs or delegation rules, they must identify what the question is truly about. What system? What concept? What phase of care? So often, when students miss a question, it is not because they lacked knowledge, it is because they misidentified the topic. Slowing them down and asking, “What’s the topic?” can completely change the trajectory of their reasoning.
Once the topic is clear, we move into Strategy. This is where coaching becomes very intentional. Is this a priority question or an evaluation question? If it’s priority, are we identifying a priority action or a priority client? From there, we guide them through structured frameworks: assessment versus implementation, physical versus psychosocial, ABCs, stable versus unstable, expected versus unexpected, acute versus chronic, actual versus potential. These aren’t just test tricks—they are safety filters.
We also remind students of alternate elimination strategies when they feel stuck. Thinking in terms of indicated versus contraindicated, normal versus abnormal, infectious versus non-infectious, or simply tallying risk factors slows impulsive answering and promotes critical analysis. When students can name the strategy they are using, they are far less likely to guess.
And then comes the most underused step: Outcome. After choosing an answer, students should pause and ask themselves, “Does this match my topic? Is this the desired outcome? Does it promote safety?” That final safety check often catches errors before they commit to them. If an answer doesn’t improve safety, it is rarely correct.
Coaching the Breakdown, Not Just the Content
One of the most important shifts we can make as educators is focusing on where the thinking breaks down instead of automatically reteaching content. When reviewing missed questions, it helps to ask: Did they misidentify the topic? Did they apply the wrong strategy? Did they skip evaluating the outcome?
When we pinpoint the breakdown, remediation becomes targeted and efficient. We move from overwhelming students with more information to strengthening the reasoning pathway itself. Requiring students to verbalize their thinking is especially powerful. Moving them from “It just seemed right” to “The topic is heart failure. This is a priority action question. I used ABCs. This promotes safety because…” builds confidence and clarity.
Reinforcing Scope and Communication
Two areas where structured coaching makes a significant difference are delegation and therapeutic communication. Students frequently miss delegation questions not because they don’t understand tasks, but because they fail to consistently apply scope of practice. Reinforcing that the RN cannot delegate assessment or initial teaching, that the LPN/LVN cares for stable clients with predictable outcomes, and that UAPs perform standard, unchanging procedures builds a protective habit into their thinking. Encouraging them to automatically ask, “Is this within scope?” strengthens both exam performance and real-world safety.
Therapeutic communication questions require equal attention. Students often answer these from instinct rather than structure. Reminding them to respond in the feeling tone, provide information, use reflection, ask open-ended questions, and avoid “why,” yes/no formats, or false reassurance aligns compassion with professionalism. Coaching these consistently ensures they approach communication with the same strategic mindset they use for clinical scenarios.
Using AI to Reinforce Clinical Judgment in Nursing
As AI tools become more present in education, we have an opportunity to guide students in using them responsibly (Annak & Duzgun, 2025). AI can reinforce the Decision Tree beautifully when students prompt it correctly. Encouraging them to ask AI to walk through questions using Topic, Strategy, and Outcome keeps the focus on reasoning. They can generate additional priority questions, practice delegation scenarios, or paste their rationales to identify where their thinking broke down.
Used intentionally, AI becomes a practice partner–a clinical judgment gem that offers repetition and reflection. What it cannot replace is the human element we provide: encouragement, reassurance, accountability, and the ability to recognize patterns. Our role is to ensure AI expands practice opportunities without becoming a shortcut to answers (Bulek et al., 2025).
Why This Matters Now for Nurse Educators
Clinical judgment questions are more layered than ever. With NGN-style case studies, unfolding scenarios, and increasing cognitive complexity, students aren’t just recalling information; they’re synthesizing it under pressure (Betts et al., 2019). At the same time, information has never been more accessible. Students can look up content instantly, and AI tools can generate explanations in seconds. But access to information is not the same as structured thinking. In fact, the more information available, the more essential structure becomes.
This is where our consistency matters most. The Kaplan Decision Tree provides stability in an environment that can feel overwhelming to students. When anxiety rises, structure reduces it. When questions feel complex, a repeatable framework simplifies them. When students are unsure, Topic → Strategy → Outcome gives them a place to start.
And perhaps most importantly, every time we use this shared language, we reinforce a culture of clinical judgment. We are not just helping individual students answer questions correctly. We are modeling disciplined thinking. We are shaping habits that extend beyond the exam and into patient care.
In a world where tools are evolving quickly, our role as educators remains steady. We coach the reasoning. We model safety. We build confidence through structure. And when we stay aligned as Kaplan partners, we give students something incredibly powerful–clarity in complexity.
We are not simply preparing students to select correct answers. We are coaching them to think like safe, entry-level nurses. The Decision Tree gives us the structure. Our expertise and relational coaching bring it to life. And when human instruction and AI reinforcement work together thoughtfully, we extend our impact even further.
That work matters–and we do it best together.
References
- Annak, I. M., & Duzgun, N. (2025). The use of artificial intelligence in nursing education: Opportunities, challenges, and solutions. J Nurs Care Res, 2(3), 85-91. Doi: 10.51271/JNCR-0035
- Betts, J., Muntean, W., Doyoung, K., Jorion, N., & Dickison, P. (2019). Building a method for writing clinical judgment items for entry-level nursing exams. Journal of Applied Testing Technology, 20(S2), 21-36
- Bulek, D., Nelson, S., Mason, E., & Piantoni, K. (2026). Building foundations for independent clinical reasoning in an AI era: Student experiences with the independent authentic clinical reasoning task in prelicensure nursing education. Teaching and Learning in Nursing, 21, 21-29. https://doi.org/10.1016/j.teln.2025.08.034
- Kaplan Nursing. (n.d.) Kaplan Decision Tree.
Dr. Christi Doherty is the Executive Director of Nursing & i-Human Patients at Kaplan North America. Dr. Doherty is a skilled researcher, valued professor of nursing, experienced clinical nurse, and designer of virtual simulations. She has earned certifications in nursing education, healthcare simulation education, diversity, and inpatient obstetrics. Dr. Doherty has published several books and journal articles and presented nationally and internationally on diverse subjects such as clinical judgment, mentorship, simulation, and students' engagement in statistics and informatics.