Supporting the Learning of Visual Perception Skills in Cytology Through a Computational Tool: Development and Evaluation Study.

Background: Advances in hardware and software have transformed how users perform activities across various domains by integrating technology in ways that enhance task execution. In education, these processes facilitate pedagogical innovation and learning through diverse interaction models, such as web systems, augmented or virtual reality, and mobile devices. An example of these approaches occurs in the context of cytopathology. Digitization and slide visualization technologies allow the use of real images in a computational environment, enabling an alternative model of interaction between the professional and the samples. Objective: This study aims to propose a computational framework to support the teaching-learning process of visual perception skills, using cervical cytology as a case study. Methods: The framework described in the study provides interactive, adaptive exercises that simulate practical laboratory experiences, enabling students to engage with specific diagnoses and rare scenarios. A proof-of-concept system was implemented and evaluated with undergraduate students and professionals in cervical cytology. Results: Test participants answered an average of 88.36% (SD 10.30%) of the activities and correctly responded to an average of 69.86% (SD 14.78%) of the questions. Compared with previous approaches, these results indicated no significant difference in student performance compared with this work approach, suggesting that the proposed framework offers similar, if not improved, performance, while providing better usability and overall user experience. Additionally, the results demonstrate the system's effectiveness in supporting cervical cytology learning while minimizing its impact on the user's routine. Furthermore, the test participants reported a positive impression of the system and its approach to supporting the learning process in cervical cytology. Conclusions: The study highlights the framework's potential for scalability and adaptation across other disciplines that require visual analysis, offering a promising avenue for enhancing education through technology.