The ongoing advancement of the “New Medical Sciences” initiative is catalyzing a profound transformation in medical education, shifting its focus from knowledge transmission to competency development. China’s Guiding Opinions on Accelerating the Innovative Development of Medical Education explicitly calls for reforms in classroom instruction to strengthen the cultivation of students’ clinical reasoning and research innovation capacities¹. Similarly, the “Healthy China 2030” Planning Outline emphasizes the need to foster interdisciplinary, innovative medical professionals². Within this context, Pathology Technology, a pivotal bridging course between basic science and clinical practice, has become a critical focal point for enhancing the comprehensive competencies of medical postgraduates.

Traditional Pathology Technology instruction has long been constrained by a triad of persistent challenges: static case resources, delayed pedagogical feedback, and a deficit in research-oriented thinking. These issues not only hinder students’ ability to grasp the complexity of clinical scenarios and correct cognitive biases in a timely manner but also create a disjunction between technical training and the development of innovative capacity. The rapid evolution of artificial intelligence (AI) offers a transformative opportunity for medical education. Breakthroughs in AI-driven image recognition and data mining have elevated AI from a mere auxiliary tool to a “cognitive amplifier” capable of reshaping the entire pedagogical workflow. Existing research indicates that AI-augmented PBL can enable personalized resource delivery and immediate feedback ([1]; [3]), and that multimodal AI-integrated models can effectively enhance clinical decision-making skills ([5]; [11]). However, most current studies focus on the empirical validation of technological applications, with limited systematic construction of pedagogical models from a cognitive theory perspective and a lack of theoretical explanation for the core question of “how AI facilitates deep learning” ([2]; [12]).

To address this gap, our study constructs a “Clinical Problem-AI Validation-Research Innovation” triadic instructional model, grounded in a synthesized theoretical framework of constructivist learning theory, situated learning theory, and cognitive load theory, and designed according to OBE principles. The model’s theoretical logic is threefold: 1) authentic clinical cases establish a “problem space” (situated learning); 2) AI tools facilitate “cognitive offloading” to reduce extraneous cognitive load; and 3) research innovation tasks drive “competency transfer” to promote deep learning. This framework not only responds to the practical dilemmas of traditional teaching but also provides an interpretable mechanistic model for AI-empowered medical education.

An OBE-aligned, multidimensional assessment system was constructed to evaluate knowledge, ability, and professionalism (Table 1). The implementation of the triadic model yielded significant positive outcomes across all three assessed dimensions.

Table 1.OBE-aligned assessment framework for the triadic model.

First, with respect to knowledge internalization, the data indicate a substantial deepening of conceptual understanding. Post-module assessments revealed that students achieved a 92% mastery rate in core “principles and applications” of pathology technologies. Notably, their grasp of the more complex and clinically critical concept—the “appropriate use and limitations of different techniques”—exceeded 85%. This represents a remarkable 28-percentage-point improvement from their pre-class baseline. This finding suggests that the model’s problem-driven approach, which anchors abstract technical knowledge within authentic clinical contexts, is highly effective in moving students beyond rote memorization towards a functional and integrated understanding of the subject matter.

Second, the assessment of competency attainment demonstrated high levels of student engagement and practical skill development. The survey, which achieved a 100% response rate (N = 72), showed that at least 92% of students either “agreed” or “strongly agreed” with the model’s effectiveness. Objective metrics from the learning management system further corroborated this self-reported satisfaction: task completion rates for key AI tools were consistently high (QuPath: 96%, Primer-BLAST: 94%, literature search: 98%). Crucially, all student groups successfully completed the capstone “comprehensive proposal” task, and 46% of these proposals were evaluated as high-quality, demonstrating logical experimental design and innovative thinking. This convergence of subjective and objective data strongly supports the conclusion that the integration of AI as a cognitive partner empowers students to efficiently execute complex technical tasks and develop preliminary research competencies.

Third, the cultivation of professionalism was evident in students’ qualitative performance during case discussions and ethical scenarios. Students consistently exhibited sophisticated clinical judgment by integrating multiple factors into their decision-making, such as patient benefit, diagnostic accuracy, and cost-effectiveness. A representative example was the reasoned argument made by several groups advocating for a stepwise diagnostic approach (e.g., FISH before NGS for initial ALK testing) to balance precision medicine with resource stewardship. Furthermore, in structured ethical decision-making tasks, over 90% of students adopted a clearly patient-centered perspective, focusing on the implications of test results for the patient’s well-being and treatment options. These observations suggest that the model successfully fosters not only technical and cognitive skills but also the essential humanistic values of a future physician-scientist.

Table 2.Key performance indicators before and after reform.

(Note: Pre-reform data from 34 students in the 2023 cohort; Post-reform from 72 students in 2024-2025 cohorts.)

The overall impact of the pedagogical reform is summarized in Table 2, which shows significant improvements across all key institutional metrics, including a 14-percentage-point rise in final exam excellence rates and a 15-percentage-point increase in university-level research grant success.

This study demonstrates that the “Clinical Problem-AI Validation-Research Innovation” triadic model, underpinned by OBE, is a powerful framework for modernizing Pathology Technology education. Its success stems from a synergistic mechanism: authentic clinical problems ignite intrinsic motivation; AI tools act as cognitive prosthetics, enabling efficient information processing and immediate feedback; and research innovation tasks scaffold the transfer of knowledge into genuine scientific inquiry ([6]; [9]).

The data reveal a clear shift from passive knowledge reception to active, self-directed learning. The high engagement in AI tool usage and the quality of research proposals confirm that students are not just learning about technology but are learning to use it as a partner in scientific discovery ([4]). The observed gains in professionalism further suggest that the model fosters a holistic development of the future physician-scientist.

However, our reflection identified a key bottleneck: the transition from AI-assisted signal interpretation (e.g., FISH) to independent scientific reasoning about complex concepts like clonal evolution remains challenging for some students. This highlights a crucial “last-mile” problem in the triadic chain.

Our OBE-guided triadic model has proven effective in enhancing graduate students’ clinical, research, and technical competencies in Pathology Technology. Its core innovation lies in its theoretically grounded, closed-loop design that leverages AI not just as a tool but as a catalyst for deep learning.

Future work will focus on three areas to address the identified bottleneck and scale the model: 1) integrating dynamic simulation platforms (e.g., Cancer Simulator) to visualize clonal dynamics under therapeutic pressure; 2) developing a tiered library of real-world FISH cases within QuPath for targeted skill refinement; and 3) advancing our learning analytics platform to generate individualized “learning portraits” for precision teaching. By continuously refining this model, we aim to provide a replicable blueprint for “New Medical Sciences” talent development across a broader range of medical technology courses.

This work was supported by the 2025 Guangxi Graduate Education Innovation Program (JGY2025335).

*These authors contributed equally to this work.

¹https://www.gov.cn/zhengce/zhengceku/2020-09/23/content_5546373.htm?ivksa=1023197a

²https://www.mofcom.gov.cn/zcfb/zgdwjjmywg/art/2017/art_5808daa8f56e4dabaa08dcd254db897f.html