READINESS AND PERCEIVED BARRIERS TOWARDS AI-ASSISTED TOOLS AMONG SPECIAL EDUCATION UNDERGRADUATES

Abstract

Artificial Intelligence (AI) is increasingly recognised as a transformative force in education, offering opportunities
to personalise learning, reduce teacher workload, and support diverse learners. In special education, where students often
require tailored instruction and adaptive approaches, AI-assisted tools such as intelligent tutoring systems, adaptive learning
platforms, and automated content creators hold particular promise. Yet, the promise of these tools ultimately rests on how
prepared future educators feel, and the challenges they believe may stand in the way. This pilot study investigates the
readiness of Special Education undergraduates to integrate AI-assisted tools into their future teaching practice, while also
examining the barriers that may hinder adoption. A quantitative survey was conducted among 30 undergraduates in the
Bachelor of Special Needs Education (BSNE) programme, focusing on readiness, perceived barriers, and the predictive role
of Technology Acceptance Model (TAM) constructs. Findings revealed high readiness across all year groups, with ANOVA
results confirming no significant differences between cohorts. Regression analysis demonstrated that Perceived Usefulness
and Perceived Ease of Use significantly predicted readiness, explaining 31% of the variance. Despite this, frequency
analysis of barriers showed that 40% of respondents expressed high concern about data privacy, while concerns about
credibility and overdependence were more evenly distributed. A weak negative correlation between readiness and barriers
suggests that confidence alone is insufficient; ethical safeguards and institutional support remain critical. By reframing this
work as a pilot investigation, the study contributes preliminary evidence to the TAM framework in Special Education
contexts. It underscores the need for structured training in AI literacy, ethical awareness, and curriculum design that embeds
AI systematically across teacher education programmes. These insights provide practical implications for policymakers and
institutions seeking to embed AI responsibly in inclusive education.