Recent advancements in artificial intelligence (AI) and natural language processing (NLP) have revolutionized educational approaches, particularly for neurodiverse learners, including individuals with autism, ADHD, dyslexia, and other cognitive differences. This survey explores various AI applications that enhance educational experiences for neurodiverse learners. Key areas of focus include generative AI, large language models (LLMs), chatbots, personalized learning, and assistive technologies. These tools have been applied to create tailored educational content, facilitate communication, predict educational outcomes, and analyze emotional responses. Additionally, the integration of virtual and augmented reality (VR/AR) offers interactive and immersive learning environments that align with the sensory needs of neurodiverse students. The paper highlights the importance of ensuring AI technologies are free from biases and transparent in their decision-making processes. By leveraging AI, educators can develop inclusive and effective learning environments that cater to the diverse needs of neurodiverse learners, ultimately enhancing their educational outcomes and engagement.
Neurodiverse learners, including individuals with autism, ADHD, dyslexia, and other cognitive differences, often require tailored educational approaches to thrive academically. Recent advancements in artificial intelligence (AI) offer promising solutions to create more inclusive and effective learning environments. This paper explores various AI applications, such as generative AI, large language models (LLMs), chatbots, personalized learning, and assistive technologies, that enhance educational experiences for neurodiverse learners. Additionally, it discusses the role of multisensory teaching in leveraging AI to develop interactive and immersive educational content.
Generative AI and large language models (LLMs) have the potential to revolutionize personalized learning for neurodiverse students. These models can create customized educational content that adapts to individual learning styles and needs, such as generating reading materials at varying difficulty levels or rephrasing complex concepts in simpler terms.
Large Language Models (LLMs), like ChatGPT, can generate reading materials at varying difficulty levels or simplify complex concepts, making them accessible to a broader range of learners (Scarlett et al., 2023).
AI-generated resources are perceived to be of equivalent quality to student-generated content, suggesting their viability as supplementary educational material (Denny et al., 2023).
LLMs have met many longstanding goals in AI, positioning them as key tools in educational innovation (Sejnowski & Churchland, 2023).
Combining visual and textual data inputs, LLMs enhance understanding and response accuracy (Wang et al., 2023).
LLMs fill information gaps by providing precise responses and handling various tasks efficiently (Zahid & Joudar, 2023).
Advances in natural language processing systems, particularly large language models like GPT-3, highlight their potential applications in diverse settings (Kenton et al., 2023).
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LLMs can perform sophisticated tasks with electronic information resources, aiding in interactive engagement, metadata extraction, indexing, and classification (Formanek, 2024).
Multimodal large language models (MLLMs) are analyzed for their architecture, alignment strategies, tasks, datasets, benchmarks, and performance (Caffagni et al., 2024).
LLMs facilitate faster content creation without compromising quality, and further research is needed for strategic and ethical implementation in education (Leiker et al., 2023).
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Integrating LLM-based tools into education can develop critical thinking and sustainable cognitive structures (Joseph, 2023).
ModelGPT generates AI models tailored to user-provided data or task descriptions, making AI models more accessible and user-friendly (Tang et al., 2024).
The potential benefits and challenges of using LLMs and generative AI in assessments emphasize the need for AI literacy among assessment professionals (Hao et al., 2024).
Optimizing large language models' size, performance, and computational resources through novel parameter-sharing methods provides insights for creating more efficient models (Gholami & Omar, 2023).
Transformer models, particularly LLMs like GPT, have a significant impact on NLP, enhancing human-computer interactions and addressing ethical concerns (Yadav, 2023).
Conversational LLMs like ChatGPT impact higher education by enhancing student learning and AI-assisted learning in universities (Laato et al., 2023).
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Generative AI and LLMs have transformative potential across various sectors, emphasizing responsible innovation for humanity's benefit (Linkon et al., 2024).
Adaptive learning systems enhance personalized learning experiences by integrating ubiquitous computing and distributed artificial intelligence techniques. These systems adapt to learners' preferences and tasks, providing personalized learning evaluations and content recommendations based on the learner’s context (Salazar et al., 2015).
The AIIA leverages advanced AI and NLP techniques to provide personalized learning support, significantly impacting the development of AI-enabled Virtual Teaching Assistants (VTAs) (Sajja et al., 2023).
The UDL framework supports all learners by emphasizing flexibility and customization in instructional methods, utilizing technology to cater to diverse learning preferences (King-Sears, 2014).
Chatbots, powered by AI, provide instant, personalized support to students, answering questions, and offering guidance tailored to their learning pace and preferences. Chatbots reduce student anxiety and improve engagement by offering non-judgmental, immediate assistance.
AI chatbots like ChatGPT reduce students' anxiety in English writing classrooms by providing instant feedback and allowing students to learn at their own pace (Hawanti & Zubaydulloevna, 2023).
AI-based chatbots provide students with support, guidance, and counseling for both their professional and personal queries, addressing their issues and doubts effectively (Sharma et al., 2023).
Chatbots assist students with administrative processes and general course-related questions, streamlining information retrieval and enhancing their overall academic experience (Santana et al., 2023).
AI-driven assistive technologies support neurodiverse learners by providing tools such as speech-to-text and text-to-speech applications. These technologies ensure that neurodiverse students can participate fully in educational activities, reducing barriers to learning.
An NLP-based assistive tool aids in the education and training of autistic and mentally retarded children by utilizing NLP to bridge expressions and concepts through relevant images (Kiliçaslan et al., 2023).
Neuroscience enhances education for students with learning disabilities by emphasizing early identification of learning problems and targeted interventions (Moats, 2004).
Assistive technologies improve reading fluency for dyslexic students, significantly aiding both children and adults with dyslexia (Patnoorkar et al., 2023).
Interactive and immersive educational content that combines visual, auditory, and tactile elements can be created using AI technologies. For example, virtual and augmented reality (VR/AR) applications offer engaging, multi-sensory learning experiences that cater to the unique sensory preferences of neurodiverse students.
Virtual laboratories using NEURON and Python facilitate interdisciplinary instruction in undergraduate neuroscience, encouraging interdisciplinary thinking and reducing barriers to entry (Donley et al., 2021).
A multi-sensory interactive training system incorporates human-computer interaction and a neural network recognition algorithm, offering a virtual environment that is highly reliable and adaptive to the needs of users (Yu-yi, 2009).
Interactive 3D simulations facilitate learning through engagement and immediate feedback, emphasizing the importance of internalizing a 3D map of the neuraxis for long-term retention (Hamza-Lup & Thompson, 2009).
VR/AR applications provide a dynamic and immersive way to present educational content, making learning more concrete and relatable. For neurodiverse learners, VR/AR can offer tailored experiences that align with their sensory needs, such as reducing visual clutter for students with ADHD or providing consistent routines and visual supports for students with autism.
VR applications assist educators in understanding and empathizing with autistic children's behaviors, enhancing the school experiences of autistic learners (Camilleri et al., 2019).
VR can be used as a learning tool for autistic children, with potential applications in understanding autism and guiding treatment efforts (Strickland et al., 1996).
User-centered VR environments support the educational needs of children with ADHD, showing positive perceptions of use and satisfaction (Cardona-Reyes et al., 2021).
VR applications for autism intervention and other fields beyond entertainment offer significant potential in designing academic modules and serious games for intervention (Lahiri, 2020).
VR and AR improve the learning process by providing tailored experiences that align with the sensory needs of neurodiverse learners (Rana, 2021).
Augmented reality and 3D technologies improve learning outcomes and help students integrate 2D images into a complex representation of the brain (Holman et al., 2018).
AI-driven technologies enhance cognitive accessibility for neurodiverse individuals by providing personalized learning experiences (Deetjen-Ruiz et al., 2024).
Cross-sensory interaction design and co-design promote inclusive learning, addressing the challenges and benefits of cognitive diversity in education (Barter et al., 2022).
An IoT device as a sensory feedback tool facilitates learning for neurodivergent students, promoting inclusivity in education (Sims et al., 2023).
AI applications in interventions for autistic children, including early diagnosis, monitoring, and game interaction, show significant effects and provide insights for future AI interventions for special groups (Li et al., 2022).
AI applications hold significant promise for improving educational outcomes for neurodiverse learners. The integration of AI in education has shown potential in various domains, including generative AI, large language models (LLMs), chatbots, personalized learning systems, and assistive technologies. These tools create tailored educational content, facilitate communication, and predict educational outcomes, providing personalized support and interventions that cater to the unique needs of neurodiverse students.
Key findings from the survey indicate that:
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Generative AI and LLMs: These technologies can revolutionize personalized learning by generating reading materials at varying difficulty levels, simplifying complex concepts, and creating tailored educational content. Their potential to provide personalized learning paths makes them invaluable tools in enhancing accessibility and understanding for neurodiverse learners.
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Chatbots: AI-powered chatbots offer instant, personalized support, answering questions, providing guidance, and identifying when students need additional resources. They help reduce student anxiety and improve engagement by offering non-judgmental, immediate assistance.
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Assistive Technologies: AI-driven assistive tools such as speech-to-text, text-to-speech, predictive text, and grammar correction tools support students with reading and writing difficulties. These technologies ensure that neurodiverse students can participate fully in educational activities, reducing barriers to learning.
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Virtual and Augmented Reality (VR/AR): These technologies provide dynamic and immersive learning experiences that align with the sensory needs of neurodiverse learners. VR/AR can simulate real-world scenarios and abstract concepts, making learning more concrete and relatable. They also offer tailored experiences that enhance engagement and understanding for students with different cognitive profiles.
The survey underscores the importance of ensuring that AI technologies are free from biases and transparent in their decision-making processes. As AI technology continues to advance, its potential to transform neurodiverse learning will only grow, paving the way for more inclusive and effective educational environments.
By harnessing the power of AI, educators can create learning experiences that are more responsive to the individual needs of neurodiverse students. This approach not only enhances educational outcomes but also fosters a more inclusive and supportive educational landscape, where every learner has the opportunity to thrive.
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