Development of personalized profiles of students with autism spectrum disorder for interactive interventions with robots to enhance language and social skills.

The team built a short checklist that captures each child's favorite toys, sensory triggers, and peer style. They tested it with a small group of ASD students in regular kindergarten classes.

After filling out the form, staff picked tablet games and robot moves that matched each profile. Kids played the games with a small robot during center time.

Pilot runs showed the kids stayed in the activity longer and talked more with classmates. Teachers said planning was faster because the form pointed to ready-made robot scripts.

No extra training was needed; paraprofessionals ran the sessions after a ten-minute briefing.

Eggleston et al. (2018) already showed that four sensory-based subtypes hide inside preschool ASD. The new form folds those sensory clues into a one-page plan, so you can act on the old subtype idea without doing stats.

Merlo et al. (2023) used the BEHAVE app to track mand training in the same inclusive rooms. Both studies used single-case designs and saw gains, but Merlo focused on teacher-collected data while Herrero-Martín let the robot collect engagement clicks.

Mayes et al. (2003) mapped how verbal and non-verbal IQ gaps close with age. The new profile adds peer-preference items, updating the old IQ-only picture with social pieces that matter for group work.

You can copy the one-page profile in under five minutes. Circle sensory likes, pick a robot script, and you have a tailored station that keeps ASD kids engaged and talking to peers. No extra devices or long assessments needed.