RESUMEN
BACKGROUND AND OBJECTIVE: Within the context of an interdisciplinary research project, we created a cutting-edge prototype of an adaptive digital auditory training system designed for cochlear implant (CI) users. By leveraging the evidence-centered design (ECD) framework, we integrated a dynamic difficulty adjustment feature that tailors the experience to the unique performance capabilities of each individual user. METHODS: The ECD provides a conceptual design framework suitable for complex assessments of competence and dynamic performance. In the first phase, the domain of hearing was first defined in the context of CI users. In the development phase the three core models of the ECD, the competence model, the evidence model, and the task model, were developed and implemented. In addition, an asset pool of sound and language files was created, which included comprehensive linguistic feature descriptions for calculating item difficulties. RESULTS: Based on the requirements described, an adaptive exercise generator, an AI service, and other components were implemented. This included the development of a game environment and a dashboard for patient data management. The exercises' difficulty levels were determined based on various parameters (e.g., sound, word frequency and number of words, grammatical properties) in combination with defined task types and levels. CONCLUSION: An adaptive digital auditory training system can help to supervise and train CI patients in a continuous, interactive process based on their individual needs. We see the ECD as an effective way to build a user-based adaptive system.