The aim of this paper is to provide information regarding diversity in speech and language profiles of individuals with Autism Spectrum Disorders (ASD) and try to classify these profiles according to the combination of the communication difficulties. Research findings confirm the existence of heterogeneity of communication challenges in ASD across the lifespan. A lot of children with ASD experience communication challenges and strengths across all language sub-systems including pragmatics, grammar, semantics, syntax, phonology, and morphology in both oral and written language, while some children with autism demonstrate exceptional language abilities incl. linguistic creativity. Communication issues vary on a continuum of severity so that some children may be verbal, whereas others remain non-verbal or minimally-verbal. The diversity of profiles in speech and language development stem from either the presence of comorbid factors, as a core symptom of autistic behavior without comorbidity or both, with the development of complex clinical symptoms. Difficulties with the semantic aspect of language affect the individual’s skills in abstract thinking, multiple meanings of words, concept categorization, and so on. Finally, the coexistence of ASD with other communication difficulties such as a Language Disorder, Apraxia of Speech, Speech Sound Disorders or/and other neurodevelopmental disorders raises the need for examining more carefully the emergence of new clinical profiles and clinical markers useful in performing differential diagnosis and different intervention.

<p style="text-align: justify;">During COVID-19 in Athens, Greece, 535 general education and 170 special education teachers were tested for computer use self-efficacy, ICT competence, and computer attitudes. Demographic and occupational factors impacted computer attitudes and computer use self-efficacy. The GCAS and GCSES showed that general and special education teachers liked computers. Teachers were computer-savvy and confident. Computer attitudes boosted computer use self-efficacy. Computer self-efficacy is strongly linked with computer attitudes, subscales of confidence and affection and moderately linked with cognitions about computers. Age, position, and ICT training substantially influenced computer attitudes and computer use self-efficacy. ICT-trained teachers had improved their attitudes and computer use self-efficacy. Computer self-efficacy and attitudes about computers did not change for special education teachers, but computer confidence increased. Except for those under 25, younger teachers demonstrated higher computer self-efficacy than older ones.</p>