Hospital training often follows a “spray and pray” approach, where standardized modules are assigned broadly, with little accommodation for individual gaps or skill levels. While these modules may deliver consistent information, they fail to account for personal knowledge gaps, previous experience, or unique learning preferences. As a remedy, healthcare institutions frequently rely on preceptors for one-on-one tutoring, which can offer a much-needed personalized touch. However, preceptors vary in their teaching abilities and available time, leading to inconsistent learning experiences. Without structures that systematically tailor training to everyone, hospitals risk uneven skill development, learner disengagement, and decreased overall competence in delivering patient care.
As hospital nurse educators, the challenge of ensuring new nurse residents develop both clinical proficiency and critical thinking skills is ever-present. Traditional training models, while effective, often fail to provide personalized learning experiences tailored to individual competencies and gaps. Integrating artificial intelligence (AI) into simulation training can revolutionize this process, allowing for adaptive learning pathways that enhance knowledge retention and clinical decision-making.
The TSAM® Model — Triage, Survey, Analyze, and Match — provides a structured framework for leveraging AI-driven learning to create customized training programs that meet the unique needs of each nurse resident. This model helps nurse educators systematically assess competencies, analyze learning gaps, and deliver targeted interventions that foster confident, competent nurses.
Understanding the TSAM® Model
The TSAM® Model employs a data-driven approach to personalize nurse training, ensuring that each learner receives the most relevant educational experiences.
T (Triage): Identifying Baseline Knowledge and Skills
The first step involves collecting data on a nurse resident’s current knowledge and skills.
Example in Nursing: During the first week of a residency program, we asked a group of new residents to participate in high quality simulations with standardized patients. While some excelled, others hesitated, unsure of what to prioritize. This variation in competency levels underscored the need for a structured triage process.
AI Enhancement: AI-powered simulation platforms can analyze responses to real-time clinical scenarios, identifying strengths and weaknesses.
S (Survey): Assessing Skills and Confidence Levels
Beyond technical skills, understanding how to instruct to a specific knowledge gap is crucial.
I recall a nurse resident named Emily who struggled with handover. She was disorganized and when she was flustered, it was worse. Her preceptor had become frustrated that Emily was not improving. Our simulation team held a day long simulation day, focusing on key trouble areas, that some of the residents were struggling with. It helped Emily. However, in terms of resources, that day was an 8-hour day for 4 preceptors and 4 clinical educators. All the nurse residents went through the same handover training, whether they needed it or not. Once again, we sprayed and prayed.
AI Enhancement: Machine learning algorithms detect patterns in self-reported data and previous performance metrics, tailoring educational resources accordingly.
A (Analyze): Detecting Patterns and Knowledge Gaps
AI tools analyze collected data to identify trends in learning deficiencies and misconceptions.
I attended patient safety and quality meetings where our committee noticed that several nurses consistently miscalculated epinephrine dosages during emergency care for severe allergic reaction. Reviewing the data, I saw that their struggles stemmed from a lack of reinforcement in anaphylaxis protocol. AI-driven analytics could have identified this trend earlier and recommended additional training before errors surfaced in practice.
AI Enhancement: Predictive analytics forecast where nurses might struggle in clinical settings, enabling preemptive interventions.
M (Match): Customizing Learning Pathways
The final step involves aligning training interventions with individual learning needs.
I worked with a nurse who was exceptional as a medical-surgical nurse who had transferred to Progressive Care but struggled with reading ECGs. Instead of giving her generic online courses, we focused on targeted ECG interpretation training alongside real cases and strips, which accelerated her growth in that area without redundant coursework.
AI Enhancement: Adaptive platforms adjust content dynamically, offering advanced scenarios for high performers and remediation for those needing reinforcement.
AI-Driven Personalization in Action
Consider Jason, a new nurse resident who excelled in bedside assessments but struggled with programming infusion pumps. In a conventional training model, Jason would have received the same standardized pump training as his peers, despite his specific difficulties. However, with the TSAM® Model’s learner modeling approach, Jason's performance in simulated scenarios was continuously analyzed. Each time he was in training, the system flagged the issue, adjusted his learning pathway, and recommended targeted reinforcement exercises. Instead of spending equal time on all competencies, Jason focused intensively on refining his skills through additional simulations and AI-guided remediation.
Meanwhile, another nurse resident, Kate, demonstrated proficiency in nursing care but hesitated in emergency decision-making under pressure. The AI-driven system detected her pattern of delayed responses and tailored her simulations to emphasize high-stakes, time-sensitive clinical decision-making. Over time, Kate’s confidence and response speed improved—proving that training should evolve with the learner, not be dictated by a one-size-fits-all curriculum.
This learner modeling approach aligns with research on Intelligent Tutoring Systems (ITS), which use real-time data collection and machine learning to adapt educational content to the learner’s needs. In the TSAM® framework, the AI triages initial knowledge gaps, surveys ongoing performance, analyzes deficiencies, and matches learners to targeted interventions—ultimately leading to more competent, confident nurses.
AI-Powered Applications of TSAM® in Nurse Education
When integrated with AI, the TSAM® Model transforms nurse education by:
1. Automating Competency Assessments: AI-driven platforms reduce the manual burden of evaluating nurse residents, providing instant feedback.
2. Delivering Real-Time Feedback: Virtual patients and simulation scenarios provide immediate insights, reinforcing correct actions and guiding improvement.
3. Personalizing Learning Pathways: AI adjusts training intensity and complexity based on each nurse’s progression.
4. Enhancing Predictive Learning: By analyzing historical and real-time performance, AI can anticipate potential struggles and offer preemptive support.
TSAM® and Simulation-Based Learning
Simulation training is a cornerstone of nursing education, and the TSAM® Model enhances it by:
Triage: Pre-simulation assessments to gauge readiness.
Survey: Self-reported confidence levels influencing scenario complexity.
Analyze: AI tracking of decision-making speed, accuracy, and effectiveness.
Match: Personalized post-simulation feedback and targeted review modules.
Institutional Benefits of AI-Enhanced TSAM® Implementation
1. Efficiency: Automating assessments saves faculty time, allowing for more meaningful educator-learner interactions.
2. Improved Engagement: AI-driven pathways keep learners motivated with tailored content.
3. Better Outcomes: Addressing knowledge gaps early leads to higher competency levels and improved patient safety.
4. Scalability: AI-powered platforms accommodate large cohorts without compromising learning quality.
Practical Steps for Implementing TSAM® in AI-Powered Learning
1. Align Learning Objectives with Specific Job Description and Standards of Care for Unit Specific Training (Role-based Unit Competencies).
2. Select AI-Integrated Learning Platforms (competency management system and simulation tools with robust analytics communicating through an integrated API).
3. Develop Valid and Reliable Assessments (Objective clinical evaluations and adaptive testing).
4. Implement a Data Strategy (Clear metrics for tracking performance and progress).
5. Pilot, Evaluate, and Refine (Test with a small cohort, adjust based on feedback, and scale effectively).
6. Train Faculty (Ensure nurse educators are proficient in AI-driven analytics and adaptive learning systems).
Overcoming Challenges and Ethical Considerations
1. Data Privacy: Ensure compliance with HIPAA and FERPA regulations.
2. Faculty Buy-In: Provide evidence of AI’s efficacy to ease concerns about technology replacing human instruction.
3. Content Curation: AI should supplement, not replace, evidence-based nursing education.
4. Continuous Improvement: Regularly refine AI models to enhance accuracy and fairness.
Additional Information
For a comprehensive understanding of TSAM®, you can refer to the article "Transforming Orientation Through a Tiered Skills Acquisition Model" by M. Ellen Joswiak, published in the Journal for Nurses in Professional Development. This article details the implementation of TSAM® at a teaching hospital with 188 newly licensed registered nurses and discusses its positive outcomes.
Additionally, Joswiak's doctoral thesis, "Transforming Nursing Orientation Using a Tiered Skills Acquisition Model: Promoting New Nurse Retention and Competence," provides an in-depth exploration of TSAM® 's theoretical foundations and practical applications. This work is accessible through Augsburg University's repository.
For practical resources, the "TSAM Toolkit® " developed by Hartford Healthcare offers structured guidance to facilitate consistent and sustainable growth of TSAM® within healthcare settings. This toolkit includes materials such as an orientee guidebook, badge buddy, preceptor expectations, and tier cards, all designed to support the effective implementation of TSAM®.
The TSAM® Model, when paired with AI-driven adaptive learning, represents the future of nurse education. It empowers nurse educators to move beyond one-size-fits-all training, fostering a personalized, data-driven learning ecosystem. By leveraging AI to triage, survey, analyze, and match nurse residents with tailored educational experiences, we can create a more competent, confident, and prepared nursing workforce—ready to deliver high-quality, patient-centered care.