Exploring Emerging Frontiers in Global Assistive Technology Research

A Computational Analysis

Abstract

Abstract

Purpose: This study investigates global research on Assistive Technology (AT) in education from 2005-2024, focusing on emerging trends, research hotspots, prolific authors, journals, and thematic knowledge structures. It aims to reveal research patterns and propose directions for future work.

Design/methodology/approach: A mixed-method approach was applied, combining bibliometric, sentiment, and topic modeling techniques. Data (2,652 documents) were retrieved from Scopus and analyzed using Biblioshiny (descriptive analysis), VOSviewer (visualization), TextBlob (sentiment), and BERTopic (topic modeling). The analysis emphasized conceptual and thematic structures of AT research.

Findings: The dataset shows contributions from 9,618 scholars, with rapid growth in AT publications from 2020-2024, influenced by technological progress and the pandemic. Bradford’s Law identified 34 core journals producing one-third of the output, while Lotka’s Law confirmed expected author productivity trends. Collaboration index findings highlight limited global co-authorship. Four major keyword clusters were identified. Sentiment analysis revealed predominantly positive and objective tones, indicating optimism and fact-based reporting in AT research. Topic modeling highlighted 10 promising directions, including AI-enabled multimodal learning, personalized literacy tools, dementia-friendly education, robotics, psychometrics, smart libraries, and AI for emotional learning.

Practical/implications: AT research is undergoing technological transformation, with future studies expected to focus on AI, inclusive education, assistive devices, skills training, mobility, and information services.

Originality/value: This study uniquely combines bibliometrics, sentiment, and topic modeling to map AT research, offering a replicable framework for analyzing research dynamics across domains.