Identifying Future Trends in AI-Driven Assistive Technologies: Insights from a National Delphi Survey of Stakeholder Perspectives

Advancements in assistive technologies (ATs), particularly those powered by Artificial Intelligence (AI), have significantly enhanced the quality of life and independence of individuals with various disorders, thus fostering progress in education, employment, social engagement and well-being. Despite these advancements, significant challenges remain in the implementation of such technologies in various domains, emphasizing the necessity for deeper exploration of their future development. This study examines the future trends and perspectives of ATs, providing valuable insights from a national context and comparing the results with those of international Delphi research. Using a two-round Delphi method, we engaged a panel of 23 experts from Bulgaria, representing disability organizations, academia and practice. Ten future-oriented projections were assessed, while participant demographics and attitudes toward ATs were also analyzed. The survey’s statistical findings indicate a moderate consensus among the Bulgarian experts. They demonstrate cautious optimism about the use of ATs, especially AI-driven technology, for individuals with disabilities. The results are similar with those of the international study, with only few differences, highlighting the shared perspectives of experts at both national and international levels. The research offers valuable insights into emerging trends in AI-driven ATs and provides valuable knowledge for policymakers, researchers and developers seeking to align innovation with the expectations of diverse stakeholders.

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Publication Details

Journal
Societies
Published
2025-09-04
DOI
https://doi.org/10.3390/soc15090246
Primary Topic
Delphi Technique in Research
Type
article
Field-Weighted Citation Impact
0.00

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article

Identifying Future Trends in AI-Driven Assistive Technologies: Insights from a National Delphi Survey of Stakeholder Perspectives

Paulina Tsvetkova, Mayiana Mitevska, Anna Lekova, Alexander Simov
Societies
Delphi Technique in Research
article

Identifying Future Trends in AI-Driven Assistive Technologies: Insights from a National Delphi Survey of Stakeholder Perspectives

Paulina Tsvetkova, Mayiana Mitevska, Anna Lekova, Alexander Simov
article en

Abstract

Advancements in assistive technologies (ATs), particularly those powered by Artificial Intelligence (AI), have significantly enhanced the quality of life and independence of individuals with various disorders, thus fostering progress in education, employment, social engagement and well-being. Despite these advancements, significant challenges remain in the implementation of such technologies in various domains, emphasizing the necessity for deeper exploration of their future development. This study examines the future trends and perspectives of ATs, providing valuable insights from a national context and comparing the results with those of international Delphi research. Using a two-round Delphi method, we engaged a panel of 23 experts from Bulgaria, representing disability organizations, academia and practice. Ten future-oriented projections were assessed, while participant demographics and attitudes toward ATs were also analyzed. The survey’s statistical findings indicate a moderate consensus among the Bulgarian experts. They demonstrate cautious optimism about the use of ATs, especially AI-driven technology, for individuals with disabilities. The results are similar with those of the international study, with only few differences, highlighting the shared perspectives of experts at both national and international levels. The research offers valuable insights into emerging trends in AI-driven ATs and provides valuable knowledge for policymakers, researchers and developers seeking to align innovation with the expectations of diverse stakeholders.

SocietiesVol. 15(9)
University of Library Studies and Information Technologies (BG), Bulgarian Academy of Sciences (BG), Plovdiv University (BG)
Bulgarian Academy of Sciences
Openalex Percentile: Top 68%
Delphi Technique in Research
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