The effect of multi-walled carbon nanotubes (MWCNT) coating in the presence of polyethyleneimine (PEI) of different molecular weights (MW) on the interfacial shear strength (IFSS) of carbon fiber/acrylonitrile–butadiene–styrene (ABS) and carbon fiber/epoxy composites was investigated. The IFSS between the carbon fiber and the polymer was evaluated by means of single fiber microbonding test. The results indicated that uses of the carbon fibers uncoated and coated with pristine, low MW PEI-treated, and high MW PEI-treated MWCNT significantly influenced the IFSS of both thermoplastic and thermosetting carbon fiber composites as well as the carbon fiber surface topography. The incorporation of low MW (about 1300) PEI into the carboxylated MWCNT was more effective not only to uniformly coat the carbon fiber with the MWCNT but also to improve the interfacial bonding strength between the carbon fiber and the polymer than that of high MW (about 25,000) PEI. In addition, carbon fiber/epoxy composite exhibited the IFSS much higher than carbon fiber/ABS composite due to the chemical interactions between the epoxy resin and amine groups existing in the PEI-treated MWCNT.

This study examines whether the reinforcement theory would be effectively applied to teaching assistant robots between a robot and a student in the same way as it is applied to teaching methods between a teacher and a student. Participants interact with a teaching assistant robot in a 3 (types of robots: positive reinforcement vs. negative reinforcement vs. both reinforcements) by 2 (types of participants: honor students vs. backward students), within-subject experiment. Three different types of robots, such as ‘Ching-chan-ee’ which gives ‘positive reinforcement’, ‘Um-bul-ee’ which gives ‘negative reinforcement’, and ‘Sang-bul-ee’ which gives both ‘positive and negative reinforcement’ are designed based on the reinforcement theory and the token reinforcement system. Participants’ task performance and reaction rate are measured according to the types of robots and the types of participants. In task performance, the negative reinforcement robot is more effective than the other two types, but regarding the number of stimulus, the less the stimulus is, the more effective the task performance is. Also, participants showed the highest reaction rate on the negative reinforcement robot which implies that the negative reinforcement robot is most effective to motivate students. The findings demonstrate that the participants perceive the teaching assistant robot not as a toy but as a teaching assistant and the reinforcement interaction is important and effective for teaching assistant robots to motivate students. The results of this study can be implicated as an effective guideline to interaction design of teaching assistant robots.