Training the Body: The Potential of AIED to Support Personalized Motor Skills Learning

Abstract

This paper argues that the research field of Artificial Intelligence in Education (AIED) can benefit from integrating recent technological advances (e.g., wearable devices, big data processing, 3D modelling, 3D printing, ambient intelligence) and design methodologies, such as TORMES, when developing systems that address the psychomotor learning domain. In particular, the acquisition of motor skills could benefit from individualized instruction and support just as cognitive skills learning has over the last decades. To this point, procedural learning has been considered since the earliest days of AIED (dating back to the 1980's). However, AIED developments in motor skills learning have lagged significantly behind. As technology has evolved, and supported by the do-ityourself and quantified-self movements, it is now possible to integrate emerging interactive technologies in order to provide personal awareness and reflection for behavioural change at low cost and with low intrusion. Many activities exist that would benefit from personalizing motor skills learning, such as playing a musical instrument, handwriting, drawing, training for surgery, improving the technique in sports and martial arts, learning sign language, dancing, etc. In this context, my suggestions for AIED research in the coming 25 years focus on addressing challenges regarding 1) modelling the psychomotor interaction, and 2) providing appropriate personalized psychomotor support.

Special Issue on the Generalized Intelligent Framework for Tutoring (GIFT): Creating a Stable and Flexible Platform for Innovations in AIED Research

“…A key aspect of this objective is supporting one-to-one tutoring for individuals learning psychomotor tasks that are assessed by measures such as speed, accuracy, balance, and coordination (Simpson 1972). In exploring the artificial intelligence in education (AIED) literature, Santos (2016) posed the challenges of modeling psychomotor interaction and providing personalized support for psychomotor tasks that ranged from sports to surgical procedures to sign language. Since the AIED literature addressing tutoring of psychomotor tasks is lagging with respect to cognitive domains, we suggest that the development and validation of measures, expert models, and adaptive support for psychomotor tasks could benefit from tools and processes in the GIFT testbed.…”

Evaluation of Sports Visualization Based on Wearable Devices

“…A. U. Alahakone et al [4] put forward the method of gait research and rehabilitation, and achieved the effect of scientific gait training by using inertial sensor technology to identify toe-off and landing on the heel when exercising on a treadmill. In the research of virtual reality technology in physical education [5][6][7], Lin Zhang et al [8] and Xu Lanjun [9] discussed the application of sports simulation technology and tactics in basketball teaching practice and volleyball teaching, respectively; In the field of campus sports simulation, Liu Heng et al [10] focused on how to stimulate students' independent learning. In the research of Markov process on sport competitive prediction and teaching prediction, Yonggan Wang [11] predicted and analyzed the basketball competition results, while Liu Yaxin studied the application of Markov process on teaching evaluation.…”

Toward Personalized Vibrotactile Support When Learning Motor Skills

“…In a previous work [9], seventeen systems that supported motor skills learning have been reviewed. Those six [16][17][18][19][20][21] that provide vibrotactile feedback are compiled in Table 1, analyzing the information that is monitored (sensors used, processing approach, measures computed), how it is compared to detect errors (user, movement and context data used to diagnose errors) and what correction support (actuators used, feedback trigger and delivery) is provided.…”

“…Systems for learning motor skills require that the physical actions carried out by the user when acquiring enactive knowledge are monitored, compared and, when needed, corrected [9]. Thus, there exists an interesting research path to develop personalized motion learning systems that provide vibrotactile feedback in the psychomotor learning domain (i.e., the learning domain dealing with motor skills learning).…”

“…In fact, since each person has different goals, physical condition and motor skills, this training should be done in a personalized way, considering her own body build, corporal features and specific physical abilities, her current performance and the particularities of the motor skills to be learnt [9]. This means that each person should receive the advice and support that is most appropriate for her individual characteristics in her current context, which might be very different from the advice and support required by another person in a different context.…”