












































Georgia Journal of Literacy 
2025, Vol. 47(1), pp. 66–78 
https://doi.org/10.56887/galiteracy.183 
ISSN: 2833-7611 

Practitioner Article  

 
 

 

CONTACT Alexis Lawton, Speech-Language Pathologist, MS+30, CCC-SLP, Walterboro, SC; email 
alexis.lawton1@gmail.com.  
 

Leveraging AI to Enhance Literacy in Students 
with ASD: Insights and Applications  
 
Alexis Lawton 
Speech-Language Pathologist, Walterboro, SC   

 

ABSTRACT 

Students with autism spectrum disorder (ASD) face unique challenges in literacy 
development, often requiring more individualized support than traditional 
interventions can provide. This paper explores the potential of generative artificial 
intelligence (AI) tools, such as AI-powered notetaking, natural language processing 
(NLP) applications, image generation, and AI-powered tutoring, to enhance literacy 
outcomes for students with ASD. Drawing on both research evidence and clinical 
practice, the paper highlights how AI tools can offer personalized, adaptive learning 
experiences that promote key literacy skills such as reading fluency, vocabulary 
acquisition, and writing organization. These tools provide a flexible and engaging 
approach to learning that can increase confidence and reduce anxiety, particularly 
for students who struggle with traditional methods. The paper also offers practical 
strategies for educators, therapists, parents, and policymakers to effectively 
integrate AI tools into educational and therapeutic settings, ensuring a collaborative 
approach to supporting students with ASD. However, the author stresses that while 
AI offers promising support, it is most effective when used alongside traditional 
literacy strategies and continually assessed to ensure it meets the evolving needs of 
students with ASD. 

KEYWORDS 

autism spectrum 
disorder; literacy; 
artificial 
intelligence; 
interventions; 
educational 
settings 

 
utism spectrum disorder (ASD) is a neurodevelopmental difference characterized by 
persistent challenges in social communication, restrictive behaviors, and repetitive 
patterns of activity (American Speech-Language-Hearing Association, 2020). This 

definition was selected as it emphasizes the communication aspects of ASD, which are directly 
related to literacy development and central to my work as a speech-language pathologist. Students 
with ASD, regardless of age, often face unique literacy challenges, including difficulties with 
reading comprehension, summarizing information, and understanding text (American Speech-
Language-Hearing Association, 2020). These challenges can significantly impact their learning, 
particularly in the areas of literacy development. This paper specifically focuses on preschool-aged 
(ages 3–5) and school-aged children (ages 6–18) with ASD as they navigate literacy in educational 
settings. Traditional literacy strategies—such as phonics-based programs, comprehension drills, 
and small group instruction—have long been employed. However, these approaches may not 
always align with the needs of students with ASD, as they often do not address sensory 
sensitivities, rigid thinking, or challenges with social communication (Iannone, 2023). 

Generative artificial intelligence (AI) has recently emerged as a promising tool for 
enhancing literacy instruction. Unlike traditional AI, which focuses on tasks like data analysis, 
generative AI can create human-like text, engage in dynamic conversations, and adapt to learners’ 
needs, making it especially suited for environments where individualized support is critical (Solari 

A 

mailto:alexis.lawton1@gmail.com


Lawton 67 
 

et al., 2021). In education, generative AI holds the potential to reshape how students interact with 
content by offering flexible, adaptive learning experiences (Arciuli & Bailey, 2021). For students 
with ASD, this technology offers the potential to address specific literacy development challenges. 
While promising, the empirical research on the effectiveness of generative AI tools for students 
with ASD is still limited. Most existing studies focus on general education contexts, with little 
research exploring how these tools impact literacy outcomes for students with ASD (Yim, 2024). 
Moreover, concerns about biases in AI models exist, which could unintentionally reinforce 
stereotypes or fail to meet the diverse learning needs of students with ASD (Li et al., 2024). These 
gaps highlight the need for further research into how AI tools can be effectively designed and 
implemented to support literacy development in students with ASD. This paper explores the 
potential of generative AI tools in addressing these challenges and provides practical strategies for 
educators and therapists to implement them effectively. 
 
Author’s Perspective 
As a school-based speech-language pathologist, I have worked with students with autism spectrum 
disorder (ASD) for many years and have observed firsthand the difficulties they face in literacy 
development. Traditional literacy methods often fall short because they do not consider the unique 
cognitive and social-emotional needs of these students. For instance, some students struggle to 
connect text to their personal experiences, while others find it challenging to process complex 
language or abstract concepts. In my practice, I’ve begun incorporating generative AI tools to 
support literacy development, and I have observed promising results. AI offers a personalized, 
adaptive learning experience that traditional methods often cannot provide. These tools help 
students remain engaged, practice literacy in dynamic ways, and receive immediate, context-
specific feedback, essential for students with ASD. This isn’t just about innovation for its own 
sake—it’s about finding real, effective solutions to the literacy challenges my students face every 
day. I am eager to share these insights and help others in the field explore how generative AI can 
transform literacy education for students with ASD. 
 
Target Audience and Relevancy 
This paper is intended for professionals working with students with autism spectrum disorder 
(ASD), including educators, therapists, parents, and policymakers. It provides valuable insights 
into the integration of generative AI tools to support literacy development in these students. 
General and special education teachers will find practical guidance for implementing these tools 
in classrooms, while therapists, such as speech-language pathologists, can explore how AI 
enhances therapy sessions. Parents and caregivers will receive strategies for reinforcing literacy 
skills at home, ensuring consistent support beyond the classroom. Policymakers will benefit from 
evidence-based recommendations on incorporating AI into educational settings for students with 
special needs. 

The literature review offers a comprehensive overview of AI tools in education and their 
potential to address literacy challenges for students with ASD. It identifies existing gaps in 
research while exploring AI’s benefits, limitations, and practical applications. The review first 
discusses the context of ASD and literacy challenges, then examines AI’s role in education, 
including specific tools like Grammarly, ChatGPT, and Otter.AI. It concludes with evidence and 
recommendations for how educators, therapists, and policymakers can effectively utilize these 
tools to meet the needs of students with ASD. By addressing the research gaps, this paper aims to 



68 Georgia Journal of Literacy 47(1)  
 

guide future studies and practices that can enhance educational experiences and outcomes for these 
students. 
 
Method 
I used a comprehensive and systematic approach to identify relevant studies on AI tools in literacy 
interventions for students with autism spectrum disorder (ASD). I searched academic databases 
such as Google Scholar, PubMed, and ERIC, using keywords like “AI in education,” “literacy 
interventions for ASD,” “generative AI tools,” and “technology in special education.” Selection 
criteria focused on peer-reviewed articles published within the past ten years, prioritizing research 
on the impact of AI on literacy outcomes for students with ASD and the efficacy of specific tools 
(e.g., Grammarly, ChatGPT, Otter.AI). Additionally, I included studies on personalized learning 
and data-driven instructional strategies in special education. After screening, I narrowed down 
sources based on relevance, methodological rigor, and empirical data to ensure a comprehensive 
and up-to-date review. 

AI Tools for Literacy Development in Students with Autism Spectrum Disorder: Literature 
Review and Practical Applications 
Artificial intelligence (AI) technologies have garnered attention in educational settings for their 
potential to provide personalized learning experiences, especially for students with autism 
spectrum disorder (ASD; see Table 1 below) Students with ASD often face unique challenges in 
communication, social interaction, and academic performance, which traditional literacy 
instruction may not fully address (Popenici & Kerr, 2017). AI tools, by offering customized 
interventions and real-time feedback, present a promising solution for enhancing literacy outcomes 
(Gillon et al., 2017). Research highlights their potential in improving reading fluency, vocabulary 
acquisition, and writing skills (Solari et al., 2021), although research on their effectiveness in 
special education remains in the early stages (Rose et al., 2016). This paper explores how various 
AI tools—such as Otter.AI, Microsoft Word, Grammarly, Turnitin, ChatGPT, DALL-E 2, and 
Khanmigo—can be implemented to support literacy development in students with ASD. 

AI-Powered Notetaking Tools 

AI-powered notetaking tools like Otter.AI and Microsoft Word offer significant benefits for 
students with ASD. These tools can improve engagement and participation, particularly for those 
with learning difficulties (Provenzani et al., 2019). For example, Otter.AI’s speech recognition 
system aids with reading fluency by providing real-time feedback on pronunciation and sentence 
structure (Knight et al., 2019). In one case, an eighth-grade student of mine with ASD, who 
experiences fine motor difficulties and struggles with handwriting, uses Otter.AI to transcribe 
spoken content during our virtual therapy sessions. Not only does the tool capture the verbal 
information, but it also summarizes key takeaways and breaks down the material into manageable 
sections (Bannur, 2023). This feature is particularly helpful for students with ASD, who often 
experience information overload and may struggle to prioritize essential details by organizing the 
material into clear, digestible segments, ensuring better comprehension and sustained engagement 
(Pangriptaningrum & Hermanto, 2022). The improvements in my student’s ability to recall and 
apply key points were evident after a semester of using Otter.AI. The student showed a 25% 
increase in their ability to summarize and recall reading comprehension material, as measured by 
pre- and post-reading comprehension probes. Additionally, the student and their parent expressed 
increased independence in the sessions.  



Lawton 69 
 

Educators can allow upper elementary to high school students to use Otter.AI during 
writing assignments to help them dictate their thoughts, which are then transcribed into text. As 
students become more comfortable using Otter.AI, they can gradually take on more independent 
tasks, completing writing assignments alongside their peers. During therapy sessions, therapists 
can provide a series of prompts, and the student dictates their responses to Otter.AI. The therapist 
can then review the text with the student, focusing on ways to improve clarity, organization, and 
grammar. By focusing on improving the quality of the written output, the therapist helps the 
student become more aware of how to structure and organize their ideas more effectively. Parents 
can also use Otter.AI to assist their child with dictation exercises, such as short stories or 
journaling. The child can dictate their thoughts aloud, focusing on generating ideas and expressing 
themselves verbally, rather than struggling with the mechanics of handwriting. This helps alleviate 
stress around writing and allows the child to feel more confident in their ability to communicate. 
However, challenges related to background noise, unclear speech, and specialized vocabulary 
remain. Despite these limitations, the overall impact of Otter.AI on the student’s literacy 
development has been positive. 

Similarly, Microsoft Word offers several AI-powered tools like Editor and Dictation, 
which are invaluable for students of various grade levels with ASD who have difficulty with 
writing composition, grammar, and organization. A sixth-grade student of mine, who struggles 
with writing structure, finds the Editor tool especially helpful for restructuring sentences for clarity 
and flow. The Dictation feature also eliminates the physical challenge of typing, allowing the 
student to focus more on generating ideas rather than on the motor difficulties associated with 
writing (Mukherjee et al., 2022). Over the course of two months, this student demonstrated a 40% 
improvement in writing organization and clarity, as evidenced by pre- and post-intervention 
writing samples. The student also reported feeling more independent in her writing process and 
less anxious about spelling and grammar. Teachers observed increased assurance and a reduction 
in avoidance behaviors typically associated with writing tasks.  

Like Otter.AI, educators can use Dictation to help students express their ideas verbally, 
allowing them to focus on content rather than the mechanics of writing, and then refine their work 
using Editor for grammar and clarity. Therapists can incorporate Dictation for students who 
struggle with writing by transcribing their verbal responses, while Editor can guide students 
through grammar corrections and improve written expression. At home, parents can use these tools 
to assist with homework and creative writing activities, helping their child express ideas freely 
without the stress of writing by hand. By reviewing the Editor’s feedback, stakeholders can 
reinforce grammar lessons in a supportive, non-judgmental way. While Microsoft’s AI tools are 
beneficial, challenges remain. Over-reliance on AI suggestions can sometimes hinder the 
development of critical thinking skills and an understanding of writing conventions (Chen et al., 
2022). Additionally, AI-generated feedback may not always be contextually appropriate, which 
emphasizes the importance of teacher or therapist oversight to ensure that students’ intended 
meaning is preserved (Barrios-Fernandez et al., 2020). 
 
Natural Language Processing (NLP) 

Natural Language Processing (NLP) tools, which use machine learning techniques to understand 
and generate human language, have become increasingly important in literacy development. They 
have often been integrated into literacy platforms to support comprehension and text analysis 
(Ghilain et al., 2016). These tools, including Grammarly, TurnItIn, and ChatGPT, offer 
personalized, real-time feedback that helps students improve their writing skills (Dai et al., 2020). 



70 Georgia Journal of Literacy 47(1)  
 

For instance, Grammarly is an AI-powered writing assistant that provides feedback on grammar, 
syntax, vocabulary, and overall clarity. It helps students refine their writing by suggesting 
improvements in sentence structure, word choice, and punctuation (Cardon et al., 2021). Another 
sixth-grade student of mine with ASD, who struggles with grammar and sentence construction, 
showed significant improvement after using Grammarly. Over the course of their Individualized 
Education Plan (IEP), this student exhibited a 30% improvement in syntactical accuracy and a 
40% improvement in overall clarity, as measured by pre- and post-assessments. The student 
reported feeling more confident in writing tasks, citing that immediate feedback helped them feel 
in control of their work (Fiok et al., 2021). 

Educators can incorporate Grammarly to assist students with ASD across various grade 
levels who struggle with grammar, sentence structure, and writing organization. Students can type 
their thoughts into a word processor, and Grammarly will automatically highlight errors, offering 
suggestions for corrections. Therapists can use Grammarly to identify areas for improvement in 
grammar, punctuation, and sentence structure. At home, parents can integrate Grammarly into the 
child’s daily homework routine. As the child writes their assignments, they can use Grammarly to 
revise their work, receiving immediate, receptive feedback on grammar, spelling, and sentence 
structure.  

Similarly, Turnitin—known for its plagiarism detection capabilities—also offers powerful 
NLP tools that assist students in improving writing coherence and syntax. The platform’s feedback 
encourages students to focus on clarity and flow, which are essential components of literacy 
development (Hemment, 2023). My twelfth-grade student with ASD, who struggles with writing 
organization, improved in both overall organization and coherence of his writing, showing a 35% 
improvement in writing structure, as observed in teacher assessments and writing samples. For 
middle and high school students, teachers can assign essays and encourage students to submit 
drafts to Turnitin for feedback. The tool will check for plagiarism and originality and offer writing 
suggestions, helping students refine their work before final submission. This encourages students 
to engage with their writing more critically and become more aware of the importance of academic 
integrity. 

ChatGPT, a generative AI tool, further supports students in creative and conversational 
contexts. Research suggests that AI models like ChatGPT can improve writing skills and reading 
comprehension (Zhou et al., 2020). I used ChatGPT with a student who struggles to write cohesive 
stories. The tool helped him generate ideas for characters, settings, and plot development, 
ultimately producing more structured narratives. Engaging in this interactive writing process 
allowed him to expand his vocabulary and improve the quality of his writing drafts. This approach 
is particularly effective for students with ASD, who often struggle with traditional social 
interactions and written communication (Kreinsen & Schulz, 2023).  

Educators can allow students in middle and high school with ASD who struggle with 
organizing their thoughts to use ChatGPT to help them with narrative writing. For example, the 
tool can provide structured guidance by asking questions like, “Tell me about the main character,” 
or “Describe the setting of your story.” This interaction helps students generate coherent narratives 
while fostering independent thinking and creativity. In a therapy session, if a student struggles 
with generating ideas for a story, the student can provide a brief description of the story they want 
to write, and ChatGPT will offer suggestions for plot twists, character development, and settings. 
Parents can work alongside the child, using ChatGPT to refine ideas and guide them through the 
writing process. Over time, as the child becomes more comfortable with the tool, they can begin 
using ChatGPT independently to brainstorm and develop future writing assignments. This gradual 



Lawton 71 
 

increase in autonomy helps build confidence in the child’s writing abilities. However, while NLP 
tools like ChatGPT are effective in generating contextually relevant responses, they sometimes 
fail to provide detailed, targeted feedback on grammar and syntax unless specifically prompted by 
the user (Song, 2023). Therefore, these tools are best used as part of a broader curriculum that 
includes direct language instruction and teacher or therapist guidance (Lund, 2023). 
 
Image Generation  

Image generation tools, such as DALL-E 2, can also enhance literacy development, particularly 
for students who benefit from visual aids. Research shows that combining visual and textual 
learning can improve comprehension and recall, especially for students with ASD who may 
struggle with abstract concepts or verbal expression (Lurtz, 2021). These tools allow students to 
generate images from text prompts, bridging the gap between abstract and concrete thinking. For 
instance, my kindergarteners love using image generation, and one student in particular, with 
whom I worked, generated an image of a forest described in a story passage. This exercise helped 
her connect the details in the text to a visual representation, which improved her comprehension 
of the key story elements. After using DALL-E 2, the student demonstrated a 20% improvement 
in her ability to describe key elements of the story and an increase in vocabulary usage as she 
described the images she created.  
 Educators can ask students of any age to write descriptive passages about an imaginary 
scene or character and then use DALL-E 2 to generate images based on their descriptions. This 
visual representation of their writing helps students connect words to imagery, improving 
comprehension and stimulating creative thinking. In therapy, the therapist can prompt the student 
to describe their favorite place or a dream vacation and then use DALL-E 2 to generate images 
based on the student’s descriptions. Parents can prompt the child to write a short story or describe 
a scene in detail and then use DALL-E 2 to generate images based on the child’s descriptions. 
Incorporating both writing and visual elements into storytelling makes the writing process for 
students more interactive, enjoyable, and effective. While this tool can support comprehension, 
they do not directly address writing structure or grammar, making it important to integrate it with 
other literacy tools. Additionally, the quality of generated images depends heavily on the 
specificity of the prompt, which may require guidance to ensure the output aligns with the student’s 
understanding (Derevyanko, 2023). 
 
AI-Powered Tutoring 

Khan Academy’s Khanmigo is another AI tool that tailors responses based on student input, 
offering personalized feedback to support literacy development. Research suggests that AI tutors 
can improve engagement and learning outcomes by delivering immediate, context-specific 
feedback (McDowell, 2024). Khanmigo has helped my students break down complex passages by 
prompting them with questions that encourage critical thinking about the text. For example, my 
first-grade student with ASD, who struggled with analyzing character motivations, benefited from 
Khanmigo’s guidance to explore character choices and their broader implications. After using the 
tool for several months, the student showed steady improvement in writing structure and the ability 
to articulate ideas more clearly. Despite its strengths, Khanmigo has limitations. It does not address 
non-cognitive barriers, such as drive or emotional regulation, which are often present in students 
with ASD (Pitt & Carless, 2021). Although Khanmigo helped one student improve his reading 
fluency and engagement by 18% over six weeks, the student still required additional support to 
manage emotional responses and build classroom confidence. AI tools like Khanmigo can play a 



72 Georgia Journal of Literacy 47(1)  
 

crucial role in fostering academic progress. However, they should be considered part of a broader, 
more comprehensive learning plan that includes teacher or therapist support (Rovagnati et al., 
2021). 

Educators can use Khanmigo for any grade level to tailor assignments to provide step-by-
step guidance, breaking tasks and readings into smaller, manageable pieces. Therapists can use 
Khanmigo as a self-paced learning tool. If the student encounters difficult vocabulary or has 
trouble understanding key concepts in a passage, they can ask Khanmigo for explanations or 
clarifications. For practicing both math and reading skills at home, parents can use Khanmigo to 
work through practice problems in math or reading exercises. This individualized support allows 
the child to learn at their own pace, building confidence in their abilities while reinforcing 
foundational skills in a way that feels engaging and supportive. 
 
Table 1: Overview of Generative AI Tools for Literacy 

AI Tool Description Key Features Potential Benefits for 
ASD Students Studies/References 

Otter.AI 

Speech-to-text 
transcription and 
note-taking tool 

Real-time 
transcription, 
customizable 
vocabulary, 
supports 
multilingual inputs 

Provides students with 
ASD a way to transcribe 
speech to text, aiding in 
writing tasks, improving 
comprehension, and note-
taking 

Positively received by 
educators and therapists, 
who note its effectiveness 
in supporting children with 
neurodevelopmental 
disorders, including ASD 
(Barua et al., 2022). 

Microsoft 
AI 

A suite of AI-
powered 
educational tools 
for reading 
support 

Text-to-speech, 
translation, 
syllable 
highlighting, 
customizable 
fonts, and 
backgrounds 

Assists students with 
reading fluency and 
comprehension; helps 
those with dyslexia or 
attention difficulties 

Can facilitate a more 
inclusive learning 
environment, allowing 
educators to tailor their 
instruction to meet the 
unique needs of each 
student (Barua et al., 
2022). 

Grammarly 

AI-powered 
writing assistant 
for grammar and 
style 
improvements 

Grammar checks, 
writing style 
suggestions, tone 
detection, 
translation 

Supports students with 
writing challenges; helps 
students with ASD improve 
their grammar, clarity, and 
expression 

Research indicates that the 
use of such tools can lead 
to improved writing 
outcomes, as students 
become more aware of 
their writing mechanics and 
develop greater confidence 
in their abilities (Wiggins et 
al., 2019). 

Turnitin 

Plagiarism 
detection and 
writing feedback 
tool 

Grammar checks, 
originality reports, 
and feedback 
from instructors 

Helps students with ASD 
improve writing through 
feedback; teaches proper 
citation practices 

Helps students understand 
the importance of proper 
citation and ethical 
considerations, which is 
particularly relevant for 
children with ASD who may 
require additional support 
in these areas (Barua et al., 
2022). 

ChatGPT 

AI chatbot for 
personalized 
tutoring and 
conversation 

Text generation, 
language 
comprehension, 
and problem-
solving 
capabilities 

Can engage students with 
ASD in personalized 
learning, offer real-time 
feedback, and adapt to 
different literacy levels 

Can be used to facilitate 
low-pressure writing tasks, 
allowing children with ASD 
to generate and refine their 
ideas in a structured 
manner (Dixon et al., 
2019). 



Lawton 73 
 

Limitations and Future Directions  
While integrating AI tools into literacy instruction for students with autism spectrum disorder 
(ASD) holds significant potential, several limitations must be addressed to optimize effectiveness 
(see Table 2 for a concise summary of these generative AI tools). Technological challenges include 
the varying quality, accessibility, and adaptability of AI tools and the requirement for stable 
internet and hardware, which may not be available in all educational settings. Many tools are not 
explicitly designed for ASD students, limiting their applicability for certain needs. Moreover, the 
implementation of these tools can differ widely across educational contexts due to factors such as 
classroom size, teacher familiarity, and support staff availability. AI tools may not be equally 
effective for all students with ASD, as their cognitive and emotional profiles vary widely, 
necessitating research into how these tools can be more effectively tailored to individual needs. 

Ethical and privacy concerns are also significant, as AI tools often require the collection of 
sensitive student data, raising questions about data security, consent, and privacy. Since students 
with ASD may be particularly vulnerable to misuse of their data, clear guidelines for safeguarding 
privacy while utilizing the benefits of AI tools are crucial. Policymakers play a critical role in 
advocating for equitable access to these tools, ensuring adequate school funding, and training for 
educators and therapists. Moreover, they must address data privacy issues by implementing robust 
safeguards to protect students’ sensitive information and ensure the development of best practices 
accessible to all students, regardless of socioeconomic status or disability. 

Future research should focus on several areas: longitudinal studies to track the long-term 
effects of AI on literacy, academic progress, and social-emotional development; comparative 
research to identify the most effective AI tools for specific subgroups within the ASD population; 
and research into customizing AI systems to accommodate individual student strengths, 
challenges, and learning preferences. Additionally, ethical guidelines for data security and the 
integration of AI tools in educational settings need further exploration. Training programs for 
educators and therapists will be crucial for effective tool integration, while studies on integrating 
AI into holistic educational frameworks that blend traditional methods with AI interventions will 
help support both literacy and social-emotional growth. 
 
Table 2: Summary of Generative AI Tools  

AI Tool Type of Tool Age/Grade 
Level 

Applicability 
for ASD 
Literacy 

Limitations 

DALL-E 2 Image 
generation 

Preschool to 
high school 
(PreK-12th 
grade) 

Assists in 
visualizing 
abstract 
concepts in 
reading or 
writing 

The images generated may not accurately 
represent the intended concepts, which can be 
problematic for children with ASD who rely on 
visual aids for understanding (Saharia et al., 
2022). 

Khanmigo AI-powered 
tutoring 

Preschool to 
high school 
(PreK-12th 
grade) 

Provides one-
on-one tutoring, 
including 
literacy skills 

Potential to suppress critical thinking and 
communication skills by providing answers 
without encouraging deeper engagement with 
the material (Huang, 2023). 

Otter.AI Speech-to-text Elementary 
to high 
school 
(Grades 3-
12) 

Supports 
students in 
converting 
speech to text 
for written 
assignments 

Reliance on clear audio input; background 
noise or unclear speech can lead to 
inaccuracies in transcription, which may 
confuse students who depend on accurate text 
representation for learning (Abouammoh et al., 
2023). Additionally, it does not inherently teach 



74 Georgia Journal of Literacy 47(1)  
 

social skills or contextual understanding, which 
are crucial for children with ASD (Sallam, 
2023). 

Microsoft 
AI 

Reading 
support 

Elementary 
to high 
school 
(Grades 3-
12) 

Enhances 
reading fluency, 
comprehension, 
and 
engagement 

Suggestions may not always align with the 
individual learning styles or needs of children 
with ASD, potentially leading to frustration or 
disengagement (Sallam et al., 2023). 
Furthermore, students may become overly 
dependent on technology for generating ideas 
and structuring their writing (Hung, 2023). 

Grammarly Writing 
assistant 

Middle 
school to 
college 
(Grades 6-
12, higher 
education) 

Helps improve 
writing quality 
for students with 
ASD 

Children with ASD may struggle with nuanced 
language use, and Grammarly’s algorithms 
may not fully grasp the subtleties of their 
writing intentions (Derevyanko, 2023). 
Additionally, the tool does not address the 
emotional or social aspects of writing, which 
are particularly important for children with ASD 
who may need support in expressing their 
thoughts and feelings effectively (Truong, 
2023). 

Turnitin Plagiarism 
detection 

Middle 
school to 
college 
(Grades 6-
12, higher 
education) 

Supports writing 
improvement 
through 
feedback 

This emphasis on compliance with academic 
standards can lead to anxiety and may stifle 
the creative expression that is essential for 
students’ engagement and learning (Alneyadi 
& Wardat, 2023). 

ChatGPT Conversational 
AI 

Middle 
school to 
college 
(Grades 6-
12, higher 
education) 

Personalized 
tutoring, helps 
with 
communication 
and 
comprehension 

Additionally, the quality of responses 
generated by ChatGPT can vary, and it may 
not always provide accurate or contextually 
appropriate information, leading to confusion 
(Choudhry, 2023). 

 
Conclusion 
Integrating AI tools into literacy interventions for students with autism spectrum disorder (ASD) 
offers significant promise for enhancing educational outcomes. Both research and clinical 
experience highlight AI’s potential to address the literacy challenges faced by students with ASD, 
particularly in areas such as reading fluency, vocabulary acquisition, and comprehension. AI tools 
provide personalized, adaptive learning experiences that engage students in ways traditional 
methods may not fully achieve. However, it is essential that AI complements, not replaces, 
established intervention methods. While AI can enhance traditional approaches, human 
interaction, direct literacy instruction, and ongoing assessments remain foundational for success. 

A key takeaway from the literature is the necessity for customization and personalization 
in AI tools. Given the diversity within the ASD population, AI tools must adapt to individual 
learning profiles to be effective. This calls for dynamic systems capable of meeting students’ 
unique needs. Moreover, the collaborative role of educators, therapists, and parents is crucial for 
ensuring the effective use of these tools and alignment with students’ evolving needs. 

AI’s ability to analyze large datasets is another promising aspect for improving literacy 
outcomes. By tracking student performance in real-time, AI tools can identify specific literacy 
challenges and offer data-driven insights, helping educators and therapists tailor instructional 
strategies accordingly. This approach ensures that interventions remain personalized and 
responsive, fostering long-term literacy growth. For educators, therapists, and policymakers, the 



Lawton 75 
 

next step is to adopt a holistic approach that integrates AI tools with traditional methods. By 
continuously adapting and refining AI-driven strategies, we can create more equitable, engaging 
learning environments for students with ASD, helping them overcome literacy challenges and 
improve academic outcomes. Thoughtful integration of AI into special education holds exciting 
possibilities, and with careful planning, we can ensure these tools contribute to the long-term 
success and well-being of students with ASD. 
 
References 
Abouammoh, N., Alhasan, K., Raina, R., Malki, K. A., Aljamaan, F., Tamimi, I., Muaygil, R., 

Wahabi, H., Jamal, A., Al-Tawfiq, J. A., Al-Eyadhy, A., Soliman, M., & Temsah, M. H. 
(2023). Exploring perceptions and experiences of ChatGPT in medical education: A 
qualitative study among medical college faculty and students in Saudi Arabia [Preprint]. 
medRxiv. https://doi.org/10.1101/2023.07.13.23292624  

Alneyadi, S., & Wardat, Y. (2023). ChatGPT: Revolutionizing student achievement in the 
electronic magnetism unit for eleventh-grade students in Emirates schools. Contemporary 
Educational Technology, 15(4), Article ep448. https://doi.org/10.30935/cedtech/13417  

Alvari, G., Furlanello, C., & Venuti, P. (2021). Is smiling the key? Machine learning analytics 
detect subtle patterns in micro-expressions of infants with ASD. Journal of Clinical 
Medicine, 10(8), Article 1776. https://doi.org/10.3390/jcm10081776    

American Speech-Language-Hearing Association. (2020). Autism spectrum disorder. 
https://www.asha.org/Practice-Portal/Clinical-Topics/Autism/  

Arciuli, J., & Bailey, B. (2021). The promise of comprehensive early reading instruction for 
children with autism and recommendations for future directions. Language Speech and 
Hearing Services in Schools, 52(1), 225–238. https://doi.org/10.1044/2020_lshss-20-
00019   

Bannur, C. (2023). VQAutism: Leveraging visual question answering to analyse autism spectrum 
disorder [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-3782339/v1  

Barrios-Fernández, S., Delgado, M., Díaz-González, B., & Gómez, A. (2020). A complementary 
sensory tool for children with autism spectrum disorders. Children, 7(11), Article 244. 
https://doi.org/10.3390/children7110244  

Barua, P., Vicnesh, J., Gururajan, R., Oh, S., Palmer, E., Azizan, M., & Acharya, U. (2022). 
Artificial intelligence enabled personalised assistive tools to enhance education of children 
with neurodevelopmental disorders—A review. International Journal of Environmental 
Research and Public Health, 19(3), Article 1192. https://doi.org/10.3390/ijerph19031192  

Cardon, P., Ma, H., & Fleischmann, C. (2021). Recorded business meetings and AI algorithmic 
tools: Negotiating privacy concerns, psychological safety, and control. International 
Journal of Business Communication, 60(4), 1095–1122. https://doi.org/10.1177/
23294884211037009 

Chen, T., Chen, Y., Yuan, M., Gerstein, M., Li, T., Liang, H., & Lu, L. (2020). The development 
of a practical artificial intelligence tool for diagnosing and evaluating autism spectrum 
disorder: Multicenter study. JMIR Medical Informatics, 8(5), Article e15767. https://doi.
org/10.2196/15767 

https://doi.org/10.1101/2023.07.13.23292624
https://doi.org/10.30935/cedtech/13417
https://doi.org/10.3390/jcm10081776
https://www.asha.org/Practice-Portal/Clinical-Topics/Autism/
https://doi.org/10.1044/2020_lshss-20-00019
https://doi.org/10.1044/2020_lshss-20-00019
https://doi.org/10.21203/rs.3.rs-3782339/v1
https://doi.org/10.3390/children7110244
https://doi.org/10.3390/ijerph19031192
https://doi.org/10.1177/23294884211037009
https://doi.org/10.1177/23294884211037009
https://doi.org/10.2196/15767
https://doi.org/10.2196/15767


76 Georgia Journal of Literacy 47(1)  
 

Choudhry, H. (2023). Perception of race and sex diversity in ophthalmology by artificial 
intelligence: A DALL-E 2 study. Clinical Ophthalmology, 17, 2889–2899. 
https://doi.org/10.2147/opth.s427296  

Dai, Y., Chai, C., Lin, P., Jong, M., Guo, Y., & Jian-jun, Q. (2020). Promoting students’ well-
being by developing their readiness for the artificial intelligence age. Sustainability, 
12(16), Article 6597. https://doi.org/10.3390/su12166597  

Derevyanko, N. (2023). Comparative analysis of neural networks Midjourney, Stable Diffusion, 
and DALL-E and ways of their implementation in the educational process of students of 
design specialities. Scientific Bulletin of Mukachevo State University Series “Pedagogy 
and Psychology,” 9(3), 36–44. https://doi.org/10.52534/msu-pp3.2023.36 

Dixon, D., Miyake, C., Nohelty, K., Novack, M., & Granpeesheh, D. (2019). Evaluation of an 
immersive virtual reality safety training used to teach pedestrian skills to children with 
autism spectrum disorder. Behavior Analysis in Practice, 13(3), 631–640. https://doi.org/
10.1007/s40617-019-00401-1 

Fiok, K., Farahani, F., Karwowski, W., & Ahram, T. (2021). Explainable artificial intelligence for 
education and training. The Journal of Defense Modeling and Simulation Applications 
Methodology Technology, 19(2), 133–144. https://doi.org/10.1177/15485129211028651 

Ghilain, C. S., Parlade, M. V., McBee, M. T., Coman, D. C., Owen, T., Gutierrez, A., Boyd, B., 
Odom, S., & Alessandri, M. (2016). Validation of the Pictorial Infant Communication 
Scale for preschool-aged children with autism spectrum disorder. Autism, 21(2), 203–216. 
https://doi.org/10.1177/1362361316636757  

Gillon, G., Hyter, Y. D., Fernandes, F. D. M., Ferman, S., Hus, Y., Petinou, K., Segal, O., 
Tumanova, T., Vogindroukas, I., Westby, C., & Westerveld, M. F. (2017). International 
survey of speech-language pathologists’ practices in working with children with autism 
spectrum disorder. Folia Phoniatrica Et Logopaedica, 69(1–2), 8–19. https://doi.org/
10.1159/000479063  

Hansen, S. (2024). Beyond the hype–the actual role and risks of AI in today’s medical practice: 
Comparative-approach study. JMIR AI, 3, Article e49082. https://doi.org/10.2196/49082 

Hemment, D. (2023). AI in the public eye: Investigating public AI literacy through AI art. In 
FAccT ‘23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and 
Transparency (pp. 931–942). Association for Computing Machinery. https://doi.org/
10.1145/3593013.3594052 

Hung, Y. (2023). Comparison of patient education materials generated by Chat Generative Pre-
trained Transformer versus experts. Annals of Plastic Surgery, 91(4), 409–412. 
https://doi.org/10.1097/sap.0000000000003634  

Iannone, A. (2023). Breaking barriers—The intersection of AI and assistive technology in autism 
care: A narrative review. Journal of Personalized Medicine, 14(1), Article 41. 
https://doi.org/10.3390/jpm14010041 

Janson, J., Underriner, J., & Jacob, R. (2013). Revitalizing languages through place-based 
language curriculum. In E. Mihas, B. Perley, G. Rei-Doval, & K. Wheatley (Eds.), 
Responses of language endangerment: In honor of Mickey Noonan (pp. 221–242). John 
Benjamins Publishing Company. 

https://doi.org/10.2147/opth.s427296
https://doi.org/10.3390/su12166597
https://doi.org/10.52534/msu-pp3.2023.36
https://doi.org/%E2%80%8C10.1007/s40617-019-00401-1
https://doi.org/%E2%80%8C10.1007/s40617-019-00401-1
https://doi.org/10.1177/15485129211028651
https://doi.org/10.1177/1362361316636757
https://doi.org/%E2%80%8C10.1159/000479063
https://doi.org/%E2%80%8C10.1159/000479063
https://doi.org/10.2196/49082
https://doi.org/%E2%80%8C10.1145/3593013.3594052
https://doi.org/%E2%80%8C10.1145/3593013.3594052
https://doi.org/10.1097/sap.0000000000003634
https://doi.org/10.3390/jpm14010041


Lawton 77 
 

Knight, E., Blacher, J., & Eisenhower, A. (2019). Predicting reading comprehension in young 
children with autism spectrum disorder. School Psychology, 34(2), 168–177. 
https://doi.org/10.1037/spq0000277 

Kreinsen, M., & Schulz, S. (2023). Towards the triad of digital literacy, data literacy and AI 
literacy in teacher education—A discussion in light of the accessibility of novel generative 
AI [Preprint]. EdArXiv. https://doi.org/10.35542/osf.io/xguzk 

Li, G., Zarei, M. A., Alibakhshi, G., & Labbafi, A. (2024). Teachers and educators’ experiences 
and perceptions of artificial-powered interventions for autism groups. BMC Psychology, 
12, Article 199. https://doi.org/10.1186/s40359-024-01664-2 

Lund, B. (2023). The prompt engineering librarian. Library Hi Tech News, 40(8), 6–8. 
https://doi.org/10.1108/lhtn-10-2023-0189 

Lurtz, M. (2021). The effect of risk literacy and visual aids on portfolio choices among professional 
financial planners. Financial Services Review, 29(3), 209–225. https://doi.org/10.61190/
fsr.v29i3.3457 

McDowell, K. (2024). Teaching data storytelling as data literacy. Information and Learning 
Sciences, 125(5/6), 321–345. https://doi.org/10.1108/ils-06-2023-0068 

Mukherjee, D., Bhavnani, S., Lockwood Estrin, G., Rao, V., Dasgupta, J., Irfan, H., Chakrabarti, 
B., Patel, V., & Belmonte, M. K. (2022). Digital tools for direct assessment of autism risk 
during early childhood: A systematic review. Autism, 28(1), 6–31. https://doi.org/10.1177/
13623613221133176 

Pangriptaningrum, A., & Hermanto. (2022). The use of Microsoft Teams during distance learning 
and its impact on increasing students’ reading interest. Jurnal Ilmiah Sekolah Dasar, 6(3), 
407–415. https://doi.org/10.23887/jisd.v6i3.45590 

Pitt, E., & Carless, D. (2021). Signature feedback practices in the creative arts: Integrating 
feedback within the curriculum. Assessment & Evaluation in Higher Education, 47(6), 
817–829. https://doi.org/10.1080/02602938.2021.1980769 

Popenici, S., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and 
learning in higher education. Research and Practice in Technology Enhanced Learning, 
12, Article 22. https://doi.org/10.1186/s41039-017-0062-8 

Provenzani, U., Fusar-Poli, L., Brondino, N., Damiani, S., Vercesi, M., Meyer, N., Rocchetti, M., 
& Politi, P. (2019). What are we targeting when we treat autism spectrum disorder? A 
systematic review of 406 clinical trials. Autism, 24(2), 274–284. https://doi.org/10.1177/
1362361319854641 

Rose, V., Trembath, D., Keen, D., & Paynter, J. (2016). The proportion of minimally verbal 
children with autism spectrum disorder in a community‐based early intervention 
programme. Journal of Intellectual Disability Research, 60(5), 464–477. https://doi.org/
10.1111/jir.12284 

Rovagnati, V., Pitt, E., & Winstone, N. (2021). Feedback cultures, histories and literacies: 
International postgraduate students’ experiences. Assessment & Evaluation in Higher 
Education, 47(3), 347–359. https://doi.org/10.1080/02602938.2021.1916431 

https://doi.org/10.1037/spq0000277
https://doi.org/10.35542/osf.io/xguzk
https://doi.org/10.1186/s40359-024-01664-2
https://doi.org/10.1108/lhtn-10-2023-0189
https://doi.org/10.61190/%E2%80%8Cfsr.v29i3.3457
https://doi.org/10.61190/%E2%80%8Cfsr.v29i3.3457
https://doi.org/10.1108/ils-06-2023-0068
https://doi.org/10.1177/13623613221133176
https://doi.org/10.1177/13623613221133176
https://doi.org/10.23887/jisd.v6i3.45590
https://doi.org/10.1080/02602938.2021.1980769
https://doi.org/10.1186/s41039-017-0062-8
https://doi.org/10.1177/1362361319854641
https://doi.org/10.1177/1362361319854641
https://doi.org/10.1111/jir.12284
https://doi.org/10.1111/jir.12284
https://doi.org/10.1080/02602938.2021.1916431


78 Georgia Journal of Literacy 47(1)  
 

Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Seyed Ghasemipour, S. K., 
Karagol Ayan, B., Mahdavi, S. S., Gontijo Lopes, R., Salimans, T., Ho, J., Fleet, D. J., & 
Norouzi, M. (2022). Photorealistic text-to-image diffusion models with deep language 
understanding [Preprint]. arXiv. https://doi.org/10.48550/arxiv.2205.11487 

Sallam, M. (2023). ChatGPT utility in healthcare education, research, and practice: Systematic 
review on the promising perspectives and valid concerns. Healthcare, 11(6), Article 887. 
https://doi.org/10.3390/healthcare11060887 

Sohl, K., Kilian, R., Brewer Curran, A., Mahurin, M., Nanclares-Nogués, V., Liu-Mayo, S., 
Salomon, C., Shannon, J., & Taraman, S. (2022). Feasibility and impact of integrating an 
artificial intelligence–based diagnosis aid for autism into the extension for community 
health outcomes autism primary care model: Protocol for a prospective observational 
study. JMIR Research Protocols, 11(7), Article e37576. https://doi.org/10.2196/37576 

Solari, E., Henry, A., Grimm, R., Zajic, M., & McGinty, A. (2021). Code-related literacy profiles 
of kindergarten students with autism. Autism, 26(1), 230–242. https://doi.org/10.1177/
13623613211025904 

Song, C., & Song, Y. (2023). Enhancing academic writing skills and motivation: assessing the 
efficacy of ChatGPT in AI-assisted language learning for EFL students. Frontiers in 
Psychology, 14, Article 1260843. https://doi.org/10.3389/fpsyg.2023.1260843 

Truong, H. (2023). ChatGPT in education—A global and Vietnamese research overview 
[Preprint]. EdArXiv. https://doi.org/10.35542/osf.io/r4uhd 

Wiggins, L. D., Durkin, M., Esler, A., Lee, L.-C., Zahorodny, W., Rice, C., Yeargin-Allsopp, M., 
Dowling, N. F., Hall-Lande, J., Morrier, M. J., Christensen, D., Shenouda, J., & Baio, J. 
(2019). Disparities in documented diagnoses of autism spectrum disorder based on 
demographic, individual, and service factors. Autism Research, 13(3), 464–473. 
https://doi.org/10.1002/aur.2255  

Yim, I. (2024). Artificial intelligence (AI) learning tools in K–12 education: A scoping review. 
Journal of Computers in Education, 12, 93–131. https://doi.org/10.1007/s40692-023-
00304-9 

Zhou, X., Van Brummelen, J., & Lin, P. (2020). Designing AI learning experiences for K–12: 
Emerging works, future opportunities and a design framework [Preprint]. arXiv. 
https://doi.org/10.48550/arxiv.2009.10228 

 
Received: November 30, 2024 | Accepted: April 6, 2025 | Published: May 20, 2025 

https://doi.org/10.48550/arxiv.2205.11487
https://doi.org/10.3390/healthcare11060887
https://doi.org/10.2196/37576
https://doi.org/10.1177/13623613211025904
https://doi.org/10.1177/13623613211025904
https://doi.org/10.3389/fpsyg.2023.1260843
https://doi.org/10.35542/osf.io/r4uhd
https://doi.org/10.1002/aur.2255
https://doi.org/10.1007/s40692-023-00304-9
https://doi.org/10.1007/s40692-023-00304-9
https://doi.org/10.48550/arxiv.2009.10228

	Author’s Perspective
	Target Audience and Relevancy
	Method
	AI Tools for Literacy Development in Students with Autism Spectrum Disorder: Literature Review and Practical Applications
	AI-Powered Notetaking Tools
	Natural Language Processing (NLP)
	Image Generation
	AI-Powered Tutoring
	Table 1: Overview of Generative AI Tools for Literacy


	Limitations and Future Directions
	Table 2: Summary of Generative AI Tools

	Conclusion
	References

