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Beyond the Bot: A Dual-Phase Framework for Evaluating AI Chatbot Simulations in Nursing Education.

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A new AI-Integrated Method for Simulation (AIMS) framework evaluates AI chatbots in nursing education, assessing prompt design and performance. It highlights the need for inclusive, safe AI integration in simulation-based learning.

Keywords:
AI chatbot evaluation frameworkAI chatbotsAIMS evaluation frameworksimulation-based learning

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Area of Science:

  • Nursing Education
  • Artificial Intelligence
  • Simulation-Based Learning

Background:

  • Rapid integration of AI chatbots in nursing education, especially for simulation-based learning.
  • Lack of structured evaluation models for AI-generated simulations.
  • Need for frameworks to assess prompt design and chatbot performance.

Purpose of the Study:

  • Introduce the AI-Integrated Method for Simulation (AIMS) evaluation framework.
  • Adapt the FAITA model for dual-phase evaluation (prompt design and chatbot performance).
  • Assess AI chatbot utility in nursing education simulations.

Main Methods:

  • Developed and applied the AIMS framework (Phase 1: prompt domains, Phase 2: performance criteria).
  • Utilized a scoring rubric and feedback loop for refinement.
  • Demonstrated application using an AI tool (Eval-Bot v1) with expert review.

Main Results:

  • AI chatbot showed strengths in prompt alignment and interactive learning for emergency planning.
  • Challenges identified in personalization, inclusivity, tone, and complexity adaptation.
  • Concerns flagged regarding safety prompts and inclusive language, impacting Phase 2 performance.

Conclusions:

  • AIMS provides a practical, replicable method for evaluating AI chatbots in simulation-based education.
  • Framework aids in identifying strengths and areas for improvement in AI integration.
  • Emphasizes intentional design, safety, and inclusivity for effective and ethical AI use in health education.