Imam, YeminaNiazi, Imran KhanHolt, KellyMartin, KatieAguayo, Claudio2025-07-182025-07-182025-05-01Imam, Y., Niazi, I. K., Holt, K., Martin, K., & Aguayo, C. (2025). Immersive XR Simulation with AI Virtual Patient for Chiropractic Learning: A pilot-ready educational tool to improve communication and history-taking skills through virtual patient interaction. Pacific Journal of Technology Enhanced Learning, 7(2), 12-13. https://doi.org/10.24135/pjtel.v7i2.2162624-47052624-4705http://hdl.handle.net/10292/19569Clinical education relies on history-taking, clinical reasoning, and building communication between patient and doctor (Hecimovich and Volet, 2009). Conventional methods mostly face challenges such as a limited number of available patients, lack of case varieties and various ethical concerns (Pohlman et al., 2020). To overcome these, we introduce an Immersive Learning (XR) project featuring an AI-based non-playable character with Natural Language Processing, designed to create a simulated clinical environment for chiropractic interns. This combination of immersive and experiential learning techniques offers a valuable opportunity to improve clinical competence in a safe and controlled setting. An intern joins a virtual environment through a Meta Quest, or other available similar VR headset, and interacts with an AI-driven virtual patient. This virtual patient is trained in chiropractic case studies with the aim of enhancing communication skills and cognitive learning through diverse case histories. The core components of this patient history-taking are: Chief Complaint – Linked to spinal regions. Symptoms – Numbness, tingling, pain. Duration and Frequency – Time and severity scaling. Triggers – Identifying motion-based pain influencers. Radiation – Tracking referring pain patterns. This prototype mimics real world clinical notetaking and supports interactive, repeatable training sessions. Inspired by Aguayo and Eames (2023) and Aguayo (2023), this project utilizes a DBR approach and the 4E+ cognition framework in the design of immersive learning, to iteratively design and refining the XR learning solution in a real-world educational setting. This research will unfold in four key phases: • Exploration and Analysis: Reviewing literature and identifying learning challenges with chiropractic students and faculty to define Xr training requirements. • Design: Developing an XR prototype featuring AI-driven virtual patients and their case histories to enhance engagement and communication skills. • Evaluation: The prototype will be tested with 5-10 students, following quantitative and qualitative mixed method approach to collect iterative feedback. • Reflection: Analysing the design principles and assessing integration into the chiropractic curriculum. A functional prototype has been developed with an AI driven virtual patient and a UI for history-taking practices. Formal evaluation is on pending and the system is pilot-ready. The upcoming feedback will assess its potential effectiveness in improving student’s engagement, communication skills and usability. Similarly, studies on immersive learning environments and AI based patients have proven improvement in clinical reasoning and communication skills in healthcare education (Narayanan et al., 2023). This prototype was developed in collaboration with a New Zealand College of Chiropractic (NZCC). The system contributes to clinical simulation by offering immersive, interactive learning experiences that enhance communication skills and clinical reasoning. Additionally, its adaptable design allows for application across various healthcare disciplines, such as nursing and physiotherapy, where effective communication is essential. This presentation will showcase the interactive responses of the AI-driven Virtual patient to illustrate how history-taking process works within the system. It will also discuss the pedagogical strategies employed in developing this system. This presentation will benefit XR designers, developers, educators and clinical researchers interested in innovative approaches to simulation-based training.Copyright (c) 2025 Yemina Imam, Imran Khan Niazi, Kelly Holt, Katie Martin, Claudio Aguayo. Creative Commons License. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.https://creativecommons.org/licenses/by-nc-nd/4.0/3901 Curriculum and Pedagogy39 Education3904 Specialist Studies In EducationPain ResearchChronic PainClinical ResearchNetworking and Information Technology R&D (NITRD)Behavioral and Social Science4 Quality Education3901 Curriculum and pedagogy3904 Specialist studies in educationConference ContributionOpenAccess10.24135/pjtel.v7i2.216