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Here you will find links to academic journals related to neurodivergence, artificial intelligence, autism in educational settings, and more. If you have a journal article (peer reviewed, full text) suggestion, please email us at michaelhiggins2@my.unt.edu.
Included in these article columns are the abstract, article title, availability, and article link below the abstract. You may also see AI generated podcasts associated with different articles so you can listen to them on the go!
Journal Articles
AI in Autism Education: A Review of Collaborative and Longitudinal Approaches
Requires institution access or article purchase.
This study presents a comprehensive literature review examining the role of artificial intelligence (AI) in autism education. It explores how AI technologies—such as adaptive learning systems, natural language processing–based communication aids, emotion recognition algorithms, socially assistive robots, and immersive augmented and virtual reality platforms—support teaching, learning, and behavioral development for autistic individuals.
The Reality of Implementing Artificial Intelligence Apps in the Special Needs Classroom, a Teacher's Perspective
Requires institution access or article purchase.
The study aimed to investigate the actual utilization of educational applications of artificial intelligence by special education instructors in the Kingdom of Saudi Arabia, as well as the prevailing inclination towards this technology from the teachers' perspective. The study employed a descriptive and analytical methodology to accomplish its objectives. The study sample consisted of 270 teachers, randomly selected from the study population. An e-questionnaire was developed as a tool for collecting data, consisting of 40 items categorized into four sections. The findings indicated that special education teachers strongly agreed on the significance of utilizing educational apps for artificial intelligence (AI). Similarly, they agreed on the obstacles and the inclination toward using such apps. However, their level of knowledge and skill associated with the use of educational applications for AI was rated as neutral.
Embracing the Future: Artificial Intelligence in the Provision of Pre-employment Transition Services to Autistic and Neurodivergent Youth
Requires institution access or article purchase.
Transition-aged youth with autism and other neurodiversities face high levels of unemployment and underemployment and can benefit from pre-employment and transition services to improve long-term outcomes. While the mainstream proliferation of artificial intelligence tools has garnered global attention over the last year, there is a growing body of research exploring how these technologies can be used to enhance communication and workplace readiness skills for these transition-aged youth.
Beyond the One-Size-Fits-All: A Systematic Review of Personalized and Gamified e-Learning for Neurodivergent Learners
This article is open access.
Traditional education, characterized by rigid curricula and inflexible teaching methods, often fails to accommodate the diverse cognitive profiles of neurodivergent learners, including those with Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), and dyslexia. Although e-Learning has introduced greater flexibility and interactivity into education, many existing platforms continue to adopt a one-size-fits-all approach, primarily catering to neurotypical learners, often overlooking the diverse cognitive and behavioral needs of neurodivergent students. The neurodivergent students frequently encounter challenges related to attention regulation, sensory processing, and retention of information, and these factors are rarely addressed in the design of conventional digital learning environments.
Artificial Intelligence for Enhancing Special Education for K-12: A Decade of Trends, Themes, and Global Insights (2013–2023)
Requires institution access or article purchase.
This paper provided a review of 210 studies on AI-enhanced special education from 2013 to 2023. Through bibliometric analysis, this review aimed to explore trends, focus areas, developments, and evolving themes of the field of AI for enhancing special education. Several noteworthy findings emerged from our analysis. The trend analysis of publications and citations revealed distinct phases, including an initial exploratory phase (2013–2016) followed by a period of rapid development (2017–2023). keyword co-occurrence networks and emergent word mapping highlight AI’s transformative potential, especially in autism spectrum disorder interventions and advancements in learning environments. Emerging trends focus on mathematics learning outcomes and educational equity, evolving through phases of understanding AI's support and integrating advanced tools like virtual reality and educational robots. Topic clustering analysis revealed categories including cognitive rehabilitation and ethical AI integration, emphasizing personalized instructional environments. Implications for research stress the need to bolster foundational skills and explore innovative teaching methods, including addressing challenges in gamified learning and integrating AI seamlessly.
Revolutionizing Autism Education: Harnessing AI for Tailored Skill Development in Social, Emotional, and Independent Learning Domains
This article is open access.
This review explores the potential of artificial intelligence (AI) to address the special needs of children with autism. It examines the issues of specific skill development in the course of this review through some features in areas such as Social Skills Training, Emotional Regulation Support, and Independent Learning Skills. This study incorporates input from psychology, education, and technology. Although the article lacks presentation of the original empirical data, it nonetheless synthesizes existing information to provide a comprehensive overview of the current state of the field. The paper firmly recommends the use of individually adapted interventions, leveraging virtual reality, natural language processing, and adaptive learning technologies. This detailed and futuristic approach will help to augment the speed of autism education, via personalizing and enhancing social, emotional, and independent learning for individuals with ASD to address current challenges and ethical considerations. It contributes by proposing AI-driven solutions to improve educational outcomes and promote greater inclusivity and adaptability in autism education.
Enhanced rationality in autism spectrum disorder
Requires institution access or article purchase.
Challenges in social cognition and communication are core characteristics of autism spectrum disorder (ASD), but in some domains, individuals with ASD may display typical abilities and even outperform their neurotypical counterparts. These enhanced abilities are notable in the domains of reasoning, judgment and decision-making, in which individuals with ASD often show ‘enhanced rationality’ by exhibiting more rational and bias-free decision-making than do neurotypical individuals. We review evidence for enhanced rationality in ASD, how it relates to theoretical frameworks of information processing in ASD, its implications for basic research about human irrationality, and what it may mean for the ASD community.
The Use of AI Chatbots for Autistic People: A Double-Edged Sword of Digital Support and Companionship
This article is open access.
There is a rise in AI ‘friends’ and chatbots that promise companionship. This commentary examines the complex benefits and dangers of such AI companions for autistic individuals. AI chatbots and social robots can offer low-pressure social interaction, consistent acceptance, and a respite from the double empathy problem that complicates autistic communication with non-autistic people. They may serve as accessible social partners and self-help tools, providing practice for conversations and relief from social anxiety in a controlled environment. Yet, alongside these promises are significant psychological, social, and ethical risks. Cases of AI companions amplifying social withdrawal, encouraging harmful behaviours, or triggering rejection sensitivity dysphoria illustrate how vulnerable users can be hurt in the absence of safeguards. The lack of accountability and regulation around AI companion apps raises further ethical concerns, from misinformation and biased advice to privacy and safety issues.
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Artificial Intelligence to Enhance STEM Education for Students With Autism
Requires institution access or article purchase
This technology in action article offers practical guidance for educators on integrating artificial intelligence (AI) tools into STEM instruction for students with autism. Many autistic students have strengths in pattern recognition, logic, and detail-oriented tasks but face challenges with executive functioning, abstract reasoning, and engagement in typical classroom settings. Utilizing evidence-based practices and the SETT (Student, Environment, Tasks, Tools) framework, the manuscript highlights three instructional supports facilitated by AI: personalized learning, executive functioning support, and engagement strategies.
A Review of Artificial Intelligence Interventions for Students with Autism Spectrum Disorder
This article is open access.
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder with challenges in social communication and interaction as well as stereotyped and repetitive behaviors, interests, and activities. Students with ASD often prefer to engage with technology because of its predictability and limited social demands. In recent years, the application of Artificial Intelligence (AI) in education has gained considerable attention. The present study aims to reveal the research trends regarding the design and development of AI teaching interventions in special education, especially for students with ASD, who often face significant challenges in academic, cognitive, and social domains. A search of the research literature from 2018 to 2024 in three electronic databases identified 1762 records. After applying eligibility criteria, 13 empirical studies were finally included, which were coded and analyzed in detail.
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Teachers and Educators’ Experiences and Perceptions of Artificial-powered Interventions for Autism Groups
This article is open access.
Artificial intelligence-powered interventions have emerged as promising tools to support autistic individuals. However, more research must examine how teachers and educators perceive and experience these AI systems when implemented.
The first objective was to investigate informants’ perceptions and experiences of AI-empowered interventions for children with autism. Mainly, it explores the informants’ perceived benefits and challenges of using AI-empowered interventions and their recommendations for avoiding the perceived challenges.
A Systematic Review of AI, VR, and LLM Applications in Special Education: Opportunities, Challenges, and Future Directions
This article is open access.
The rapid advancements in modern technologies have opened new possibilities for enhancing educational experiences for students with Special Educational Needs and Disabilities (SEND). This paper conducts a systematic review of 139 studies on the integration of AI, VR, and LLMs in Special Education. Using a deductive thematic analysis framework, it identifies key themes and challenges to synthesize the current state of knowledge and propose future research directions. The findings underscore the transformative potential of AI and Immersive Technologies in fostering personalized learning, improving social engagement, and advancing cognitive development among SEND students. Additionally, current SEN methodologies and practices are defined, teachers' attitudes toward inclusion and technology adoption, and the prevailing technological tools utilized, based on various syndromes and disorders.
The Impact of Artificial Intelligence-Based Personalized Learning Systems on the Cognitive Ability Enhancement of Children With Special Needs
This article is open access.
This study explores the impact of artificial intelligence (AI)-based personalized learning systems on the cognitive development of children with special needs in China. Through a 12-month longitudinal experiment involving 360 participants, the research demonstrates significant improvements in attention, memory, and problem-solving skills compared to traditional methods. The AI system, integrating multimodal data and adaptive algorithms, induced neuroplastic changes in key brain regions, with effects varying by disorder type (e.g., attention deficit hyperactivity disorder, autistic spectrum disorders). Results revealed a dose-response relationship between system usage and cognitive gains, highlighting optimal training thresholds. The findings support AI's potential in special education while addressing challenges like resource equity and teacher adaptability. The study provides empirical evidence for optimizing AI-driven interventions to enhance learning outcomes for children with cognitive disabilities.
Memory in autism spectrum disorder: A meta-analysis of experimental studies.
Requires institution access or article purchase.
To address inconsistencies in the literature on memory in autism spectrum disorder (ASD), we report the first ever meta-analysis of short-term memory (STM) and episodic long-term memory (LTM) in ASD, evaluating the effects of type of material, type of retrieval and the role of interitem relations. Analysis of 64 studies comparing individuals with ASD and typical development (TD) showed greater difficulties in ASD compared with TD individuals in STM (Hedges’ g = −0.53, 95% CI [−0.90, −0.16], p = .005, I² = 96%) compared with LTM (g = −0.30, 95% CI [−0.42, −0.17], p < .00001, I² = 24%), a small difficulty in verbal LTM (g = −0.21, p = .01), contrasting with a medium difficulty for visual LTM (g = −0.41, p = .0002) in ASD compared with TD individuals. We also found a general diminution in free recall compared with cued recall and recognition (LTM, free recall: g = −0.38, p < .00001, cued recall: g = −0.08, p = .58, recognition: g = −0.15, p = .16; STM, free recall: g = −0.59, p = .004, recognition: g = −0.33, p = .07). We discuss these results in terms of their relation to semantic memory. The limited diminution in verbal LTM and preserved overall recognition and cued recall (supported retrieval) may result from a greater overlap of these tasks with semantic long-term representations which are overall preserved in ASD. By contrast, difficulties in STM or free recall may result from less overlap with the semantic system or may involve additional cognitive operations and executive demands. These findings highlight the need to support STM functioning in ASD and acknowledge the potential benefit of using verbal materials at encoding and broader forms of memory support at retrieval to enhance performance.