Educational technology and cognitive diversity: exploring attitudinal responses of autistic students

Purpose: The incorporation of technological resources in education may facilitate more personalised learning experiences tailored to students’ individual characteristics. This may be particularly relevant for autistic students, who often require specific support to enhance participation and engagement in educational activities. Accordingly, this pilot study explored the influence of cognitive level groups, resource types, and their interaction on the attitudes of autistic students across different school tasks. Methods: The sample comprised seven autistic students representing low, medium, and high cognitive level groups, who completed four conceptually similar activities adapted to their cognitive abilities. A within-subjects sequential design with a fixed order was implemented. Conditions were not counterbalanced due to the exploratory nature of the study and the small sample size. Students received standardised instructions before each session. Four tools were used: traditional assessment (pre-test), virtual reality (VR), tablet, and traditional assessment (post-test), administered weekly over four weeks. Post-test tasks were similar but not identical to pre-test tasks to reduce memory effects and support transfer. Results: Results suggested that, for Activity Type 1, an exploratory pattern of more favourable attitudes emerged when medium cognitive level was combined with VR use. For Activity Type 3, students at the high cognitive level showed more positive attitudes than those in the low cognitive level. Given the small sample size, findings are preliminary and descriptive and should be interpreted as hypothesis-generating. Conclusions:The findings provide preliminary evidence that the effects of educational technologies on autistic students’ attitudes may vary according to cognitive level and task characteristics. Although exploratory, these results highlight the importance of tailoring technology-enhanced interventions to individual learner profiles. Further research with larger samples is needed to test the robustness of these patterns and to examine additional technological resources, such as robotics.