## 5 Program Customizability and Adaptability
### Automatic Adaption of Programs  
In [40], the difficulty level is adjusted based on whether or not the participant answered a question correctly via a menu-driven interface. The program has four levels of task difficulty. The difficulty level is reflected from the questions asked for each story. If the answer from the participant is deemed adequate, the next question will be elevated to a higher level unless the current level is already the highest. [PERSON] and [PERSON] [150] reported an adaptive training for adolescents with ASD using neural network to monitor participant performance and a learning fuzzy inference system to determine deficits to instructional techniques.  
Eye tracking is one of the few relatively mature technologies that has been used to retrieve nonverbal communication of a participant in realtime. Some studies used eye gaze information to alter the scene displayed for a participant. In [123], [PERSON] et al. studied the joint attention pattern using eye tracking and virtual avatars. The virtual display blurs all the scene except a small rectangular region centered on the point of gaze of the participant. This is done so to provide visual feedback on what the participant is looking at, which enhances self-awareness of gaze.  
In [28], the program included a cued condition where an avatar would fade to 70% transparency in six seconds if the participant failed to fixate his/her eyes on the particular avatar when prompted. The entire cued condition is fully automated enabled by the virtual reality program and gaze tracking via a head-mounted display and sensors. The avatar would become opaque again when the participant looked at it again.  
An individualized adaptive response module constitutes a core component in the virtual reality system used in [37; 41]. The module consists of two adaptive mechanisms, namely, the performance-sensitive system (PS), and the engagement-sensitive system (ES). In PS, the task difficulty is adjusted solely based on the input provided by the participant via the menu-based conversation interface. In ES, the task difficulty is adjusted based on both the input provided by the participant and the engagement level as revealed by the three features obtained from eye gaze tracking data, i.e., the mean pupil diameter, the mean blink rate, and the average fixation duration for each region of interest. For PS, the task difficulty is adjusted based on the performance of the previous task. The performance is determined by the points earned on a task. If the participant earns 70% or more the maximum score possible, then the performance is regarded as adequate. Otherwise, it is regarded as inadequate. If the performance on a task is deemed adequate, then the difficulty level of the next task is elevated to the next higher level. Otherwise, it is switched to a lower difficulty level. For ES, the rule for adjusting difficulty levels is more complicated because it must fuse both the performance from participant direct input (categorized as adequate and inadequate), and the performance from eye gaze analysis, which is categorized into \"good enough\" and \"not good enough\".  
It is encouraging to see that this line of research is gaining momentum. In addition to eye gaze, we have seen studies that employ various methods to obtain user feedback for adaptive control of a treatment/education program, including facial expression, head orientation, human gesture tracking, and physiological data collection [135; 136; 141; 143; 146; 151].