Incorporating an Intelligent Tutoring System Into a Game-Based Auditory Rehabilitation Training for Adult Cochlear Implant Recipients: Algorithm Development and Validation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: 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. OBJECTIVE: 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). METHODS: 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. RESULTS: =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. CONCLUSIONS: 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. TRIAL REGISTRATION: German Clinical Trials Register (DRKS) DRKS00022860; https://drks.de/search/en/trial/DRKS00022860.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it