Incorporating an Intelligent Tutoring System Into a Game-Based Auditory Rehabilitation Training for Adult Cochlear Implant Recipients: Algorithm Development and Validation

• Published in: JMIR serious games Vol. 12; p. e55231
• Main Authors: Gnadlinger, Florian, Werminghaus, Maika, Selmanagić, André, Filla, Tim, Richter, Jutta G, Kriglstein, Simone, Klenzner, Thomas
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 03.12.2024
• Subjects: Adaptation; Adaptive learning; Adults; Artificial intelligence; Cochlear implants; Ears & hearing; Feedback; Games; Hearing loss; Language; Learning; Literature reviews; Original Paper; Rehabilitation; Skills; Transplants & implants; Tutoring; intelligent tutoring system; cochlear implant; evidence-centered design; game-based learning; adaptive learning; eHealth; hearing rehabilitation
• ISSN: 2291-9279; 2291-9279
• DOI: 10.2196/55231

Cochlear implants are implanted hearing devices; instead of amplifying sounds like common hearing aids, this technology delivers preprocessed sound information directly to the hearing (ie, auditory) nerves. After surgery and the first cochlear implant activation, patients must practice interpreting the new auditory sensations, especially for language comprehension. This rehabilitation process is accompanied by hearing therapy through face-to-face training with a therapist, self-directed training, and computer-based auditory training. In general, self-directed, computer-based auditory training tasks have already shown advantages. However, compliance of cochlear implant recipients is still a major factor, especially for self-directed training at home. Hence, we aimed to explore the combination of 2 techniques to enhance learner motivation in this context: adaptive learning (in the form of an intelligent tutoring system) and game-based learning (in the form of a serious game). Following the suggestions of the evidence-centered design framework, a domain analysis of hearing therapy was conducted, allowing us to partially describe human hearing skill as a probabilistic competence model (Bayesian network). We developed an algorithm that uses such a model to estimate the current competence level of a patient and create training recommendations. For training, our developed task system was based on 7 language comprehension task types that act as a blueprint for generating tasks of diverse difficulty automatically. To achieve this, 1053 audio assets with meta-information labels were created. We embedded the adaptive task system into a graphic novel-like mobile serious game. German-speaking cochlear implant recipients used the system during a feasibility study for 4 weeks. The 23 adult participants (20 women; 3 men) fulfilled 2259 tasks. In total, 2004 (90.5%) tasks were solved correctly, and 255 (9.5%) tasks were solved incorrectly. A generalized additive model analysis of these tasks indicated that the system adapted to the estimated competency levels of the cochlear implant recipients more quickly in the beginning than at the end. Compared with a uniform distribution of all task types, the recommended task types differed (χ² =86.713; P<.001), indicating that the system selected specific task types for each patient. This is underlined by the identified categories for the error proportions of the task types. This contribution demonstrates the feasibility of combining an intelligent tutoring system with a serious game in cochlear implant rehabilitation therapies. The findings presented here could lead to further advances in cochlear implant care and aural rehabilitation in general. German Clinical Trials Register (DRKS) DRKS00022860; https://drks.de/search/en/trial/DRKS00022860.