TITLE:
Construction of an Autonomous Learning Platform in Next-Generation Nursing Education: Algorithmization of the Wellness Nursing Process and a Multilayered Practicum Model for the DX Era
AUTHORS:
Yuko Harding
KEYWORDS:
Maternal Nursing Education, Autonomous Learning, Wellness Nursing Process, Clinical Reasoning Algorithm, Digital Transformation (DX)
JOURNAL NAME:
Open Journal of Nursing,
Vol.16 No.8,
August
27,
2026
ABSTRACT: Background: In modern perinatal nursing clinical practice, students face two major obstacles. First, there is the “bloat of records,” where students spend a great deal of time documenting the vast amount of physiological data that is updated daily. With the traditional blank record format, the content of the writing depends on the individual student’s ability, resulting in inconsistent record quality and causing students’ attention to focus on the record sheet rather than the patient. Second, there is the “stagnation of integration of the clinical field,” where acute care in the hospital and community-based support are fragmented, making it difficult to cultivate a perspective of continuous support. Objective: The purpose of this study is to clarify the educational usefulness of an “autonomous learning platform.” This platform aims to transform clinical practice from a mere data transcription task into a time of “autonomous learning.” Here, students make professional judgments using their own abilities. This is achieved by clearly presenting the “information that instructors want students to acquire.” Method: The “Educational Design Research (EDR)” framework, using a qualitative self-reflective evaluation design based on professional knowledge, was applied to address problems in educational settings. As an intervention, we introduced a “clinical reasoning algorithm” designed to allow students to critically examine information using their own abilities by vertically integrating the curriculum from the second to third year, conducting a 10-day cyclical clinical rotation visiting hospitals, maternity clinics, and community settings, and organizing vast amounts of physiological data into steps requiring a “Yes/No” decision (where “Yes” indicates normal or expected states). The usefulness of this platform was verified by the developer through a “self-reflective evaluation based on professional knowledge,” using a triangulation method that assessed three indicators: logical consistency with the university’s educational policies (DP/CP), student transformation based on the clinical rubric, and information integration ability. It should be noted that the presented model is a generalized “thinking pattern,” and ethical considerations were taken to avoid including information that identifies individuals or facilities. Results: The creators’ evaluation revealed the following achievements: The introduction of the algorithm eliminated the disparities in competence observed in traditional record-keeping methods, establishing a process in which students proactively conduct assessments while comprehensively capturing the “information to be acquired” as intended by instructors. This accelerated decision-making and dramatically reduced the burden of record-keeping, creating “cognitive surplus” that could be used for professional exchanges of opinions with instructors and for deep empathy and intervention with patients. Furthermore, students transformed into “autonomous learners” who autonomously recognized their own knowledge deficiencies and proactively conducted literature research. A shift in perspective from “patients” in the hospital to “individuals living in the community” was also observed, enabling the integration of information within the framework of post-discharge life and the logical proposal of concrete support plans. Moreover, the “internship contract” prior to the internship and the presentation of clear evaluation criteria laid the foundation for responsible, autonomous nursing professionals. Discussion and Conclusion: This platform freed students from administrative tasks and set a new educational standard that balances “empathy” and “logical thinking,” the essence of nursing. The logical framework developed in this study will be extremely effective as a “logical engine” for detecting student judgment errors and supporting autonomous learning in future nursing education utilizing AI and digital technologies and will serve as a transformative model for training the next generation of nursing professionals.