The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index; 9 of the 32 sources its claims cite were checked for support.
The Future of Artificial Intelligence in Special Education Technology
Open accessAbstract
This manuscript presents a preliminary discussion of Artificial Intelligence (AI) as a disruptive technology with the potential to significantly change special education practices. The article begins with a brief description of the development of AI. The authors recognize our assertions are subjective and require further research. Several references are not peer-reviewed because educational research takes years to conduct, analyze, and disseminate outcomes. In this manuscript, we discuss current software used for writing with students in special education and discuss similarities and differences with AI software. This discussion is followed by questions and examples related to implementation, ethical and policy considerations, and preservice special education teacher preparation. The article concludes with future considerations for how AI will impact the special education technology field.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This article is a preliminary discussion arguing that AI is a disruptive technology with potential to significantly change special education practices, especially writing, ethics, policy, and teacher preparation. The authors are excited but cautious, calling for further research, ethical safeguards, and inclusion of people with disabilities in AI development.
Key findings
- The authors contend AI can act as a cognitive prosthesis and support students with disabilities through tools such as speech-to-text, NLP, chatbots, computer vision, and personalized learning.
- In their writing example, ChatGPT produced a “technically accurate 1000-word essay in 36 seconds” from the same prompt used with the WeGotIT! graphic organizer.
- Ethical concerns include bias, surveillance, autonomy, privacy, and consent; the authors argue IEP teams should discuss AI and that students, educators, and parents should understand data collection, access, and ownership.
- Policy and future directions include calls for open-source AI policy, disclosure of data sources, risk assessment mechanisms, and a slowdown in development cycles to prioritize safety, equity, privacy, ethics, and access for people with disabilities.
Methods
- Design: preliminary discussion/commentary, not an empirical study; the authors state their assertions are subjective and require further research.
- Data/sample: the authors experimented with AI writing software, interviewed a convenience sample of K-12 colleagues, and posed prompts to ChatGPT, including one asking for 10 questions about AI in education and one asking for considerations for preparing future educators.
- Analysis: no systematic analysis is reported; the article synthesizes examples, literature, ethical/policy discussion, and ChatGPT-generated text.
Limitations
- The authors state that their assertions are subjective and require further research, and that several references are not peer-reviewed because educational research takes years to conduct, analyze, and disseminate.
- The text makes evident that this is not an empirical study: there is no controlled sample, intervention, or outcome data, and the authors repeatedly note they have more questions than answers.
- The K-12 colleague input came from a convenience sample, and several examples rely on ChatGPT outputs or popular-press reactions rather than systematic evidence.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
17 Own interpretation · 11 Sources not found · 8 No full text of the sources · 6 Partly supported · 3 Supported · 1 Not checked yet · 1 Own result · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
In the paper
“Artificial intelligence can be used to support students with disabilities in a variety of ways (Barua et al., 2022).”
§ The Future of Artificial Intelligence in Special Education Technology > Is AI a Prosthesis, or is it Something More?
What the sources say
- Supportedcites Barua et al., 2022 source
The Discussion of Main Findings describes AI applications and robots supporting students with ASD in social, communication, learning, and daily-living skills. It also describes tools for students with dyslexia and ADHD, supporting the claim that AI can help students with disabilities in a variety of ways. (LLM, conf=0.90)
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“The use of AI can make tutoring more efficient by providing personalized content that matches the learner's ability (Bhutoria, 2022).”
§ The Future of Artificial Intelligence in Special Education Technology > Future Directions of AI in Education
What the sources say
- Supportedcites Bhutoria, 2022 source
Section 5.2.2 says AI-powered learning customizes study material to individual needs and takes account of each student's learning ability; it also says personalization enhances the system's overall efficiency. Section 5.2.3 reports that adapting learning paths to a learner's abilities can improve learning, supporting the claim's qualified wording. (LLM, conf=0.88)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Artifical intelligence is increasingly replacing traditional teaching methods of content delivery (Popenici & Kerr, 2017).”
§ The Future of Artificial Intelligence in Special Education Technology > Considerations for Higher Education and Personnel Preparation
What the sources say
- Partly supportedcites Popenici & Kerr, 2017 sourceExperimental — accuracy not yet measured
In Results and discussion, under “Past lessons, possibilities, and challenges of AI solutions” (page unavailable), the paper says teacherbots handling content delivery are already a disruptive alternative to traditional teaching assistants. It describes traditional information-transmission methods as open to disruption, but does not establish that AI is increasingly replacing those methods in practice. (LLM, conf=0.87)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“ChatGPT has been shown to generate accurate results using given data points and parameters, which caused fears of its potential to generate predictions that are accurate and comprehensive, often well beyond what can be achieved with existing technologies (Chan, 2022).”
§ The Future of Artificial Intelligence in Special Education Technology > Implications for Policy
What the sources say
- Partly supportedcites Chan, 2022 sourceExperimental — accuracy not yet measured
Section 2, “A background to GPT-3 and InstructGPT” (page unavailable), reports gains in the quality, accuracy, and breadth of GPT-3’s generated content and says those gains prompted interest and concern. It does not establish the claim about ChatGPT producing accurate results from given data points and parameters, or the cited sentence’s further claim about accurate, comprehensive predictions beyond existing technologies. (LLM, conf=0.83)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
In the paper
“Artificial intelligence has the potential to act as a cognitive prosthesis, a construct first identified by Edyburn (2006), to assist students with disabilities by providing new opportunities to learn and develop skills.”
§ The Future of Artificial Intelligence in Special Education Technology > Is AI a Prosthesis, or is it Something More?
What the sources say
- Not checked — no full text of the sourcecites Edyburn (2006) source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: AI will continue to emerge and be adopted in K-12 special education.
None of the sources cited for this claim have been checked yet.
In the paper
“Furthermore, AI-enabled artificial instructors can deliver personalized learning experiences tailored to the needs of the individual learners by providing adaptive experiences tailored to individual needs (Chen et al., 2020;Devi et al., 2022).”
§ The Future of Artificial Intelligence in Special Education Technology > Is AI a Prosthesis, or is it Something More?
What the sources say
- Not checked — no full text of the sourcecites Chen et al., 2020 source
- Not matchedcites Devi et al., 2022
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“A lack of diversity has been present throughout modern AI's development (Adams & Khomh, 2020).”
§ The Future of Artificial Intelligence in Special Education Technology > Future Directions of AI in Education
What the sources say
- Not checked — no full text of the sourcecites Adams & Khomh, 2020 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“While AI systems can potentially help students learn more effectively, students may not adequately understand the implications of the technology, such as what data the AI system is collecting, how it is being stored, and who has access (Ali et al., 2019).”
§ The Future of Artificial Intelligence in Special Education Technology > Ethical Concerns of Using AI in Education
What the sources say
- Not matchedcites Ali et al., 2019
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
In the paper
“Moreover, ChatGPT could be applied to existing datasets that already contain sensitive and confidential information, creating a unique and potentially high risk of unauthorized access and misuse of that data (Katrak, 2022).”
§ The Future of Artificial Intelligence in Special Education Technology > Implications for Policy
What the sources say
- Not checked — no full text of the sourcecites Katrak, 2022 source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“In addition, AIenabled tools can identify new approaches to personalize learning (Molenaar et al., 2022).”
§ The Future of Artificial Intelligence in Special Education Technology > Future Directions of AI in Education
What the sources say
- Not matchedcites Molenaar et al., 2022
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
- It may state two separate claims as one. 14
- Probably not a substantial claim. 28
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“These AI applications are especially salient for individuals with disabilities, many of whom use assistive technology features (e.g., speech-totext) within their smartphones and other technologyenhanced devices to engage with their environment (Fernandez-Batanero et al., 2022).”
§ The Future of Artificial Intelligence in Special Education Technology > Introduction
What the sources say
- Partly supportedcites Fernandez-Batanero et al., 2022 sourceExperimental — accuracy not yet measured
The Discussion and conclusions (page not specified in our copy) says that smartphones are used as assistive technology because they offer accessibility features, and that relevant applications can be installed for students with disabilities. It does not identify those applications as AI, give speech-to-text as an example, or establish the claim for individuals with disabilities beyond the students studied. (LLM, conf=0.86)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“These types of AI tools using NLP can alleviate the need for reinforcement from adults, while helping increase self-regulation skills (Hughes et al., 2022).”
§ The Future of Artificial Intelligence in Special Education Technology > AI for Writing
What the sources say
- Partly supportedcites Hughes et al., 2022 sourceExperimental — accuracy not yet measured
The Introduction (section 1) says the AI companion is intended to support students’ self-regulation instead of requiring constant monitoring by a teacher or instructional assistant. But the paper does not establish this as an achieved effect of an NLP tool: section 4.2.3 says the classroom phase still uses a human in the loop and its language patterns have yet to be analyzed for fully AI-driven behavior. (LLM, conf=0.87)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“AI systems have the potential to collect and analyze student data, such as academic performance, learning preferences, and social interactions.This multi-modal data can provide personalized instruction leading to increased student engagement while improving academic performance (Liao & Wu, 2022).”
§ The Future of Artificial Intelligence in Special Education Technology > Ethical Concerns of Using AI in Education
What the sources say
- Partly supportedcites Liao & Wu, 2022 sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“For example, the technology could be used to gain insight into an individual's behavior, opinions, and activities, leading to manipulation and unethical exploitation (Illia et al., 2022;Gibney, 2022).”
§ The Future of Artificial Intelligence in Special Education Technology > Implications for Policy
What the sources say
- Partly supportedcites Illia et al., 2022 sourceExperimental — accuracy not yet measured
Section 2.1 (page unavailable) says AI agents learn patterns from user-generated data and describes the collection and exploitation of information from users’ digital activities. Section 4 (page unavailable) says accumulated personal information could enable AI agents to mislead individuals. The paper discusses GPT-3 and AI agents, but does not specifically say ChatGPT can infer an individual’s behavior, opinions, and activities. (LLM, conf=0.82)
- Not checked — no full text of the sourcecites Gibney, 2022 source
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“In a systematic review of the AI literature in education, tools reviewed included intelligent tutoring, machine learning, language processing, robotics, data mining, and deep learning (Zafari et al., 2022).”
§ The Future of Artificial Intelligence in Special Education Technology > AI in Formal K-12 Settings
What the sources say
- Supportedcites Zafari et al., 2022 source
The Conclusion lists intelligent tutoring systems, machine learning, deep learning, natural language processing, data mining, and robotics among the AI systems examined. The review specifically concerns K–12 education; the citing sentence does not claim otherwise. (LLM, conf=0.95)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The AI software produced a technically accurate 1000-word essay in 36 seconds (See Figure 4).”
§ The Future of Artificial Intelligence in Special Education Technology > AI for Writing
plainSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“For example, The National Science and Technology Council (2022) publication, “The Networking and information technology R&D Program and the National Artificial Intelligence Office Supplement to the President's FY 2023 Budget” does not have any representation from the Department of Education and any representation of people with disabilities is not disclosed.”
§ The Future of Artificial Intelligence in Special Education Technology > Future Directions of AI in Education
What the sources say
- Not matchedcites National Science and Technology Council (2022)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The application of AI during writing instruction can enhance both output and outcomes during the writing process.”
§ The Future of Artificial Intelligence in Special Education Technology > AI for Writing
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: AI will continue to emerge and be adopted in K-12 special education.
In the paper
“Artificial intelligence has the potential to amplify, complement, and enhance the cognitive potential of learners with disabilities, providing an adaptable tool to overcome diverse challenges across formal and informal learning environments (van Boxtel, 2018).”
§ The Future of Artificial Intelligence in Special Education Technology > Is AI a Prosthesis, or is it Something More?
What the sources say
- Not matchedcites van Boxtel, 2018
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, AI-enabled chatbots can respond to simple student queries, freeing up teachers and educational assistants to focus on increasingly complex student needs.”
§ The Future of Artificial Intelligence in Special Education Technology > Is AI a Prosthesis, or is it Something More?
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We contend single point and generative AI are disruptive technologies with the potential to alter and enhance the performance of students with disabilities across all subject areas, from the performance arts to English, history, math, and science.”
§ The Future of Artificial Intelligence in Special Education Technology > AI for Writing
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: AI will continue to emerge and be adopted in K-12 special education.
In the paper
“For example, one increasingly popular use of AI is natural language processing (NLP), which could be considered a natural extension of the speech-to-text assistive technology students with disabilities have been using for writing tasks for decades (Ok et al., 2022).”
§ The Future of Artificial Intelligence in Special Education Technology > AI for Writing
What the sources say
- Not matchedcites Ok et al., 2022
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“Artificial intelligenceenabled computer vision systems can detect a variety of physical behaviors, including emotional expressions and gestures, which can be beneficial for students with autism or emotional impairments (Asthana & Gupta, 2019).”
§ The Future of Artificial Intelligence in Special Education Technology > Is AI a Prosthesis, or is it Something More?
What the sources say
- Not matchedcites Asthana & Gupta, 2019
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This type of tool can be life changing for people with disabilities much like standardized accessibility features found on the Internet meeting W3C (http://www.w3.org) standards today.”
§ The Future of Artificial Intelligence in Special Education Technology > AI for Writing
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: AI will continue to emerge and be adopted in K-12 special education.
In the paper
“the codebase and algorithmic design of ChatGPT (2023) have been used in many applications related to data analytics (Dale, 2021).”
§ The Future of Artificial Intelligence in Special Education Technology > Implications for Policy
What the sources say
- Not matchedcites Dale, 2021
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“The concerns with AI are following the trends anticipated by authors who considered the future of these technologies in special education (e.g., Basham et al., 2020a;Basham et al., 2020b).”
§ The Future of Artificial Intelligence in Special Education Technology > Considerations for Higher Education and Personnel Preparation
What the sources say
- Not matchedcites Basham et al., 2020a
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Basham et al., 2020b
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“It can provide personalized writing and grading systems involving handwritten tests (Gaillat et al., 2022).”
§ The Future of Artificial Intelligence in Special Education Technology > Future Directions of AI in Education
What the sources say
- Not matchedcites Gaillat et al., 2022
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If a class of 30 submits the same prompt for writing, the AIenabled tool will produce similar, but not identical, output for all of them.”
§ The Future of Artificial Intelligence in Special Education Technology > Who is the Author?
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: AI will continue to emerge and be adopted in K-12 special education.
In the paper
“For those of who have studied the role of technology in education and society, the historical laws of Kranzberg seem to still stand true (Pitt et al., 2021).”
§ The Future of Artificial Intelligence in Special Education Technology > Considerations for Higher Education and Personnel Preparation
What the sources say
- Not checked — no full text of the sourcecites Pitt et al., 2021 source
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“Technological disruptions such as ChatGPT can make the world easier to navigate by reducing cognitive load associated with complex tasks such as gathering, evaluating, and summarizing copious amounts of research (Mayer, 2014).”
§ The Future of Artificial Intelligence in Special Education Technology > AI in Formal K-12 Settings
What the sources say
- Not matchedcites Mayer, 2014
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“These cognitive agents and AI assistants are particularly relevant as special education teacher shortages and inadequate pay continue to plague the profession, resulting in unmanageable caseloads and underserved students (Billingsley & Bettini, 2019).”
§ The Future of Artificial Intelligence in Special Education Technology > Is AI a Prosthesis, or is it Something More?
What the sources say
- Not checked — no full text of the sourcecites Billingsley & Bettini, 2019 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Technologies hold promise and even untapped potential to assist students with disabilities, to increase access, and to impact student achievement.”
§ The Future of Artificial Intelligence in Special Education Technology > Conclusion
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI technologies such as robotic teachers, interactive learning platforms, and intelligent tutoring systems help students with disabilities learn more efficiently.”
§ The Future of Artificial Intelligence in Special Education Technology > Ethical Concerns of Using AI in Education
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: AI will continue to emerge and be adopted in K-12 special education.
In the paper
“Recently, Big Science Large Open science Open access Multilingual Language Model (BLOOM) was released as a collaboration of hundreds of researchers for an open-source response to OpenAI and similar movements (Gibney, 2022).”
§ The Future of Artificial Intelligence in Special Education Technology > Implications for Policy
What the sources say
- Not checked — no full text of the sourcecites Gibney, 2022 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For example, AI provides more efficient search results on websites to aid assessment and grading (Tomić et al., 2022).”
§ The Future of Artificial Intelligence in Special Education Technology > Future Directions of AI in Education
What the sources say
- Not checked — no full text of the sourcecites Tomić et al., 2022 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Artificial Intelligence also provided dashboards tailored for administrators, special education teachers, and parents for better monitoring of student performance (Aleven et al., 2022).”
§ The Future of Artificial Intelligence in Special Education Technology > Future Directions of AI in Education
What the sources say
- Not checked — no full text of the sourcecites Aleven et al., 2022 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“ChatGPT changes the type of analysis that could be used, the kind of feedback produced, and how the student could improve.”
§ The Future of Artificial Intelligence in Special Education Technology > Who is the Author?
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: AI will continue to emerge and be adopted in K-12 special education.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Using AI, teachers can provide students with options in their approach because a linear progression during writing is no longer necessary.”
§ The Future of Artificial Intelligence in Special Education Technology > AI in Formal K-12 Settings
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: AI will continue to emerge and be adopted in K-12 special education.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The impact of AI for teaching a student with a disability to write verses how it might be used to scaffold creative thinking is unknown at the time of this publication.”
§ The Future of Artificial Intelligence in Special Education Technology > AI in Formal K-12 Settings
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We have not yet begun to examine the impact of this disruptive technology.”
§ The Future of Artificial Intelligence in Special Education Technology > Considerations for Higher Education and Personnel Preparation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
- Rests on an assumption: AI will continue to emerge and be adopted in K-12 special education.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, when these tools are developed by a select group of people with similar backgrounds and histories the result is serious limitations in who can and will benefit from these tools.”
§ The Future of Artificial Intelligence in Special Education Technology > Future Directions of AI in Education
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: The current world of AI development is filled with graduates from many of the same universities and backgrounds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The sheer accuracy of ChatGPT's predictive capabilities has created a serious challenge for policy lobbyists and legislators.”
§ The Future of Artificial Intelligence in Special Education Technology > Implications for Policy
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, some AI systems are designed to analyze and detect patterns in student data, which can lead to bias and limit perceptions of student performance.”
§ The Future of Artificial Intelligence in Special Education Technology > Ethical Concerns of Using AI in Education
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, comprehension of the benefits and limitations of AI will only occur when research examines the implementation protocols and outcomes thoroughly.”
§ The Future of Artificial Intelligence in Special Education Technology > Considerations for Higher Education and Personnel Preparation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
- Rests on an assumption: AI will continue to emerge and be adopted in K-12 special education.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Ethical issues could be exacerbated by disclosing a disability.”
§ The Future of Artificial Intelligence in Special Education Technology > Ethical Concerns of Using AI in Education
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
In the paper
“During October 2022, the White House Office of Science and Technology Policy developed a blueprint for an AI bill of rights (White House, 2022).”
§ The Future of Artificial Intelligence in Special Education Technology > Future Directions of AI in Education
What the sources say
- Not matchedcites White House, 2022
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper relies on this without support.
The historical analogies to the calculator, computer, automobile, and Internet are valid guides for anticipating AI's impact on special education.
“Interestingly, these reactions mirror what occurred with the introduction of the calculator and the computer.”
The paper relies on this without support.
AI will continue to emerge and be adopted in K-12 special education.
“Understanding how AI can be leveraged during the learning process is crucial as AI continues to emerge in the field of special education.”
The paper relies on this without support.
Most Large Language Models such as ChatGPT are developed by resource-rich organizations and are frequently kept from the public for intellectual property protection reasons.
“Most Large Language Models such as ChatGPT are developed by resource-rich organizations and are frequently kept from the public for intellectual property protection reasons.”
The paper relies on this without support.
The current world of AI development is filled with graduates from many of the same universities and backgrounds.
“The current world of AI development is filled with graduates from many of the same universities and backgrounds.”
The paper relies on this without support.
Most investors and companies are seeking rapid iterative development.
“Most investors and companies are seeking rapid iterative development.”
The paper relies on this without support.
When delivering return on investment takes precedence, developers are under constant pressure to deliver 'brand new, shiny products and services' on a tight schedule coordinated with shareholder meetings.
“When delivering return on investment takes precedence, developers are under constant pressure to deliver “brand new, shiny products and services” on a tight schedule coordinated with shareholder meetings.”
The paper relies on this without support.
Currently no incentive exists to prioritize safety, equity, privacy, and ethical concerns and access for people with disabilities, over rapid development cycles.
“Currently no incentive exists to prioritize safety, equity, privacy, and ethical concerns and access for people with disabilities, over rapid development cycles.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Importantly, AI tools can also create pathways to access for disabled and other students.”
From Evolving intersections: AI, disability, and academic integrity in higher education (2025) · cites this paper as Marino et al. (2023)
The cited source says what the claim attributes to it.
In the citing paper
“Marino et al. (2023) highlighted AI's transformative potential to support disabled students with daily tasks that may otherwise be difficult due to impairments or injuries.”
From Evolving intersections: AI, disability, and academic integrity in higher education (2025) · cites this paper as Marino et al. (2023)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The Future of Artificial Intelligence in Special Education Technology > Future Directions of AI in Education
“The National Science and Technology Council (2022) publication, "The Networking and information technology R&D Program and the National Artificial Intelligence Office Supplement to the President's FY 2023 Budget"”
§ The Future of Artificial Intelligence in Special Education Technology > References
“National Science and Technology Council. (2022). Supplement to the President's FY2022 Budget.”
Citations
Cites
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611 · cited 1×
“However, when introducing AI into classrooms, ethical implications associated with the technology are essential considerations (Dwivedi et al., 2021).”
Artificial Intelligence in Education: A Review
Chen, L., Chen, P., & Lin, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2988510 · Cited by 4,133 · cited 1×
“Furthermore, AI-enabled arti fi cial instructors can deliver personalized learning experiences tailored to the needs of the individual learners by providing adaptive experiences tailored to individual needs (Chen et al., 2020; Devi et al., 2022).”
Exploring the impact of artificial intelligence on teaching and learning in higher education
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Engaged to a Robot? The Role of AI in Service
Huang, M.-H., & Rust, R. T. (2021) · Journal of Service Research · doi:10.1177/1094670520902266 · Cited by 1,185
What ChatGPT and generative AI mean for science
Stokel-Walker, C., & Van Noorden, R. (2023) · Nature · doi:10.1038/d41586-023-00340-6 · Cited by 828
Bhutoria, A. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100068 · Cited by 677 · cited 1×
“The use of AI can make tutoring more ef fi cient by providing personalized content that matches the learner ’ s ability (Bhutoria, 2022).”
Dale, R. (2020) · Natural Language Engineering · doi:10.1017/s1351324920000601 · Cited by 509
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Cited by
Sorted by citations
- Retracted
ChatGPT in education: Transforming digital literacy practices
Ciampa, K., Wolfe, Z. M., & Bronstein, B. (2023) · Journal of Adolescent & Adult Literacy · doi:10.1002/jaal.1310 · Cited by 145
Artificial Intelligence: An Untapped Opportunity for Equity and Access in STEM Education
Kohnke, S., & Zaugg, T. (2025) · Education Sciences · doi:10.3390/educsci15010068 · Cited by 99
Meylani, R. (2024) · Journal of Computer and Education Research · doi:10.18009/jcer.1477709 · Cited by 113
Using AI to Support Special Education Teacher Workload
Goldman, S. R., Taylor, J., Carreon, A., & Smith, S. J. (2024) · Journal of Special Education Technology · doi:10.1177/01626434241257240 · Cited by 66
“…including natural language processors (e.g. predictive text; TextHelp, 2024), machine learning (i.e., Turnitin ’ s Revision Assistant; Nichols et al., 2021), adaptive learning platforms (e.g. Achieve 3000; Murphy, 2019) and generative pre-trained transformers (e.g. ChatGPT; Marino et al., 2023).”
Improving writing feedback for struggling writers: generative AI to the rescue?
Evmenova, A. S., Regan, K., Mergen, R., & Hrisseh, R. (2024) · TechTrends · doi:10.1007/s11528-024-00965-y · Cited by 51
“Its integration is fast growing in all areas of education, including special education (Marino et al., 2023).”
Yang, Y., Chen, L., He, W., Sun, D., & Salas‐Pilco, S. Z. (2024) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-024-00422-0 · Cited by 74
“However, the influence of AI on SNS’s writing abilities and creative thinking remains uncertain (Khare et al., 2023; Marino et al., 2023).”
Harnessing the Power of Generative AI to Support ALL Learners
Evmenova, A. S., Borup, J., & Shin, J. K. (2024) · TechTrends · doi:10.1007/s11528-024-00966-x · Cited by 47
“But as Marino et al. (2023) note, “these reactions mirror what occurred with the introduction of the calculator and the computer” (p. 407).”
InfluentialKelley, M. J., & Wenzel, T. (2025) · Education Sciences · doi:10.3390/educsci15060659 · Cited by 44
“AI can also further personalize learning (Crompton & Burke, 2023), offering tutoring support, research assistance, and specialized help for students with disabilities (Marino et al., 2023).”
Kumar, S., Gunn, A., Rose, R., Pollard, R., Johnson, M. C., & Ritzhaupt, A. D. (2024) · Online Learning · doi:10.24059/olj.v28i3.4501 · Cited by 70
“This aligns with some of the benefits that Marino et al. (2023) envisioned would be possible with AI.”
BackgroundArtificial Intelligence in Special Education
Harkins-Brown, A. R., Carling, L. Z., & Peloff, D. C. (2025) · Encyclopedia · doi:10.3390/encyclopedia5010011 · Cited by 41
“Additionally, it is important to obtain explicit permission before leveraging AI-driven technologies, particularly in cases where students or parents and guardians have limited knowledge about AI and how it could be used to influence educational decisions [31].”
InfluentialFu, C., Silalahi, A. D. K., Huang, S.-C., Phuong, D. T. T., Eunike, I. J., & Yu, Z.-H. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2365028 · Cited by 43
“However, considering the rapid learning acceleration facilitated by Chat-GPT (Marino et al., 2023), could this not be seen as an advantage for the academic realm, expanding the reach of literature and fostering scholarly discussions?”
Alsolami, A. (2024) · Research in Developmental Disabilities · doi:10.1016/j.ridd.2024.104884 · Cited by 44
Waterfield, D. A., Watson, L., & Day, J. (2024) · Journal of Special Education Technology · doi:10.1177/01626434241257237 · Cited by 40
“The ef fi ciency and precision of decisions made by machines advanced in tandem with the development of faster central processing units and increased random access memory capacity (Marino et al., 2023), including the emergence of generative AI.”
Ho, C. S. M. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105517 · Cited by 43
Goldman, S. R., Carreon, A., & Smith, S. J. (2024) · Journal of Special Education Preparation · doi:10.33043/6zx26bb2 · Cited by 34
“Artificial Intelligence (AI) is one of the most disruptive technologies in education, with the potential to shift how students with and without disabilities are taught and how general and special education teachers (SETs) are prepared (Marino et al., 2023).”
BackgroundIEPs in the Age of AI: Examining IEP Goals Written with and Without ChatGPT
Waterfield, D. A., Coleman, O. F., Welker, N. P., Kennedy, M. J., McDonald, S. D., & Cook, B. G. (2025) · Journal of Special Education Technology · doi:10.1177/01626434251324592 · Cited by 33
“More recently, arti fi cial intelligence (AI) has emerged as a promising technology that has the potential to support special education teachers with their heavy workloads (Goldman et al., 2024; Marino et al., 2023; Water fi eld et al., 2024).”
InfluentialAI Tools Like ChatGPT for People with Neurodevelopmental Disorders
Moraiti, I., & Drigas, A. S. (2023) · International Journal of Online and Biomedical Engineering (iJOE) · doi:10.3991/ijoe.v19i16.43399 · Cited by 26
ChatGPT in early childhood STEM education: Can it be an innovative tool to overcome challenges?
Uğraş, H., & Uğraş, M. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12960-0 · Cited by 23
“This abil - ity includes utilizing external data, learning through training, and achieving specific goals (Guo, 2023; Kasneci et al., 2023; Marino et al., 2023; Montenegro-Rueda et al., 2023).”
Abulibdeh, A. O. (2025) · Sustainable Futures · doi:10.1016/j.sftr.2025.101033 · Cited by 39
Hong, H., & Kim, Y. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12809-6 · Cited by 25
“AI tools designed to support comprehension, organization, and application of learning further facilitate individual learning goals for students with learning disabilities (Barua et al., 2022), potentially transforming and enhancing academic performance across various subjects (Marino et al., 2023).”
Charles, K. A., Yousuf, A., Chua, H. C., Matthews, S., Harnett, J., & Hinton, T. (2025) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2025.177508 · Cited by 17
Generative AI for Inclusive Special Education
Prabagar, S., Mothkur, R., Swetha, C. B., Kurian, A., Pavithra, K., & Aarthi, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3745-6.ch008 · Cited by 16
GenAI, Robots, and Inclusive Special Education
Sağdıç, A., Elumar-efe, E., & Sani-Bozkurt, S. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-1351-0.ch015 · Cited by 17
Use of AI to improve the teaching-learning process in children with special abilities
Torres, E. R., Rodríguez, R. C., & Briñez, E. T. (2023) · LatIA · doi:10.62486/latia202321 · Cited by 32
Remixing Special Education Practices with Artificial Intelligence: UDL, EBP, and HLPs
McMahon, D. D., & Firestone, J. B. (2024) · Journal of Special Education Preparation · doi:10.33043/3dq2d32d · Cited by 19
“However, the increasing use of AI in education also raises important ethical considerations around data privacy, bias, transparency, and the changing roles of educators (Marino et al., 2023; U.S. Department of Education, 2024).”
BackgroundWang, M., Tlili, A., Khribi, M. K., Lo, C. K., & Huang, R. (2025) · Information Development · doi:10.1177/02666669251335655 · Cited by 34
Sakowicz, M., & Hamidi, F. (2025) · American Journal of STEM Education · doi:10.32674/yeyaaq64 · Cited by 29
Luo, Y. (2024) · Applied and Computational Engineering · doi:10.54254/2755-2721/64/20241427 · Cited by 15
“Furthermore, the integration of A/B testing frameworks allows for comparative evaluations of different interface designs in real-world scenarios [9].”
MethodUtepbayeva, A. (2024) · International Journal of Information and Education Technology · doi:10.18178/ijiet.2024.14.7.2119 · Cited by 17
“The disclosure of information about disability may aggravate ethical issues [20].”
BackgroundThe potential of Large Language Models for social robots in special education
Voultsiou, E., Vrochidou, Ε., Moussiades, L., & Papakostas, G. A. (2025) · Progress in Artificial Intelligence · doi:10.1007/s13748-025-00363-2 · Cited by 19
Liu, X., Lau, N. M. L., Chuin, A., Leung, W.-K., Ho, A.-T. T., Das, M., Liu, M., & Kwok, C. (2023) · International Symposium of Chinese CHI (Chinese CHI) · doi:10.1145/3629606.3629625 · Cited by 18
AI in Education: A Systematic Literature Review of Emerging Trends, Benefits, and Challenges
Adil, J. J. G. (2025) · Seminars in Medical Writing and Education · doi:10.56294/mw2025795 · Cited by 26
Impact of generative AI on educational sector
., & Singh, A. K. (2024) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem30724 · Cited by 12
Artificial Intelligence and the Intersectionality of Disability
Dieker, L., & Zaugg, T. (2024) · Springer eBooks · doi:10.1007/978-3-031-40858-8_121-1 · Cited by 11
Self-citationArtificial Intelligence in Higher Education: Examining the AI Policy Landscape at U.S. Institutions
Kim, D., & Wu, J. (2024) · The 25th Annual Conference on Information Technology Education · doi:10.1145/3686852.3687076 · Cited by 10
Educational Games and the Potential of AI to Transform Writing Across the Curriculum
Evmenova, A. S., Regan, K. S., Mergen, R. L., & Nawash, R. H. (2025) · Education Sciences · doi:10.3390/educsci15050567 · Cited by 28
“For example, text-to-speech programs, voice recognition systems, and AI-based tutoring systems provide necessary supports and personalized instruction resulting in new opportunities to learn and develop skills (Marino et al., 2023).”
Seung, Y., & Basham, J. D. (2026) · Learning Disability Quarterly · doi:10.1177/07319487261439132 · Cited by 12
Self-citationEducational Technology to Support Written Expression: A Systematic Literature Review
Goldman, S. R., Carreon, A., Smith, S. J., & Zimmerman, K. N. (2023) · Journal of Special Education Technology · doi:10.1177/01626434231210987 · Cited by 9
“As technology to support writing is rapidly changing (Graham, 2022) so to should the focus of research related to the types of educational technologies to support WE, including the potential for arti fi cial intelligence supports (Marino et al., 2023).”
Artificial Intelligence and Literacy Development in K–12 Schools
Maher, D., & Young, K. (2023) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-0205-7.ch004 · Cited by 8
Enhancing Special Education Using AI
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Artificial Intelligence to Support Writing Outcomes for Students With Disabilities
Goldman, S. R., Smith, S. J., & Carreon, A. (2025) · Journal of Special Education Technology · doi:10.1177/01626434251326328 · Cited by 9
“Natural language processing (NLP) models, that is, machine learning that understands, interprets, and can generate human language (Marino et al., 2023) are also often used in classrooms to support teacher and student output and outcomes.”
Seung, Y., Basham, J. D., Kim, T., & Lohoefener, J. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70144 · Cited by 7
Self-citationDodur, H. M. S. (2025) · European Journal of Education · doi:10.1111/ejed.70276 · Cited by 13
Rappa, N. A., Nonis, K. P., Tang, K., Cooper, G., Cooper, M. G., & Sims, C. (2026) · British Journal of Educational Technology · doi:10.1111/bjet.70048 · Cited by 10
Ebenbeck, N., & Gebhardt, M. (2024) · Journal of Special Education Technology · doi:10.1177/01626434241232117 · Cited by 17
“These advancements are particularly pertinent considering the current strides in machine learning and AI in education (Chen et al., 2022; Marino et al., 2023).”
Evolving intersections: AI, disability, and academic integrity in higher education
Jafry, A., & Vorstermans, J. (2025) · New Directions for Teaching and Learning · doi:10.1002/tl.20629 · Cited by 6
Eğitimde Yapay Zekâ Çalışmalarında Öğretmenlerin Rolü ve Bakış Açıları
Yurtsever, M., & Yurtsever, G. G. (2025) · Cumhuriyet International Journal of Education · doi:10.30703/cije.1453360 · Cited by 6
Naatz, A. J., & Ruppar, A. L. (2025) · Journal of Special Education Technology · doi:10.1177/01626434251379800 · Cited by 16
“We focus on generative AI due to its emergence as a promising tool for helping special education teachers by providing support in curriculum adaptation, lesson planning, individualized instruction, and accessibility enhancements (Dieker & Zaugg, 2024; Marino et al., 2023).”
InfluentialAI and Lifelong Learning in Developing Countries
Yadav, M., Singh, S. K., Sharma, P., & Hung, T. H. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-0884-4.ch003 · Cited by 6
The Future of Artificial Intelligence in Education 4.0
Polas, M. R. H., Jahanshahi, A. A., Ahamed, B., & Molla, M. O. F. (2023) · Advances in educational technologies and instructional design book series · doi:10.4018/978-1-6684-9103-4.ch001 · Cited by 6
Goldman, S. R., Carreon, A., & Smith, S. J. (2023) · Journal of Special Education Technology · doi:10.1177/01626434231211294 · Cited by 5
“There are several categories of AI (generative pre-trained transformers, natural language processors, machine learning, etc.; Marino et al., 2023).”
Self-Regulated Learning (SRL) with AI in Problem-Based Learning
Samsonovich, A. V., Kitsantas, A., Wahidi, S. T., & Dolgikh, A. A. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-76516-2_34 · Cited by 7
Lin, X., Cui, H., & Zhu, X. (2026) · Journal of Special Education Technology · doi:10.1177/01626434261435251 · Cited by 4
“At the same time, this literature also points to ongoing challenges related to equity, privacy, and teacher preparedness (Howorth et al., 2024; Marino et al., 2023).”
Generative AI in disability-inclusive learning: a bibliometric and systematic literature analysis
C., A. K. U., Lathabai, H. H., Kanjilal, R., Raghu Raman, R., & Nedungadi, P. (2026) · Disability and Rehabilitation Assistive Technology · doi:10.1080/17483107.2026.2653073 · Cited by 6
Embracing Artificial Intelligence (AI) Tools to Enrich Special Education Teacher Preparation
Wallace, B. C. S., & Abel, Y. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-5538-1.ch012 · Cited by 4
Jimenez, B., Courtade, G. R., & Fosbinder, J. (2024) · Journal of Special Education Preparation · doi:10.33043/z8c694yb · Cited by 4
“By harnessing the power of AI, we can better equip special educators with the tools and resources (including time) they need to effectively support student learning and achievement (Marino et al., 2023), aligned with research.”
BackgroundEngineering education's Odyssey with ChatGPT: Opportunities, challenges, and theoretical foundations
Munir, M. T., Li, B., Carter, S., & Hussain, S. (2025) · International Journal of Mechanical Engineering Education · doi:10.1177/03064190251337477 · Cited by 6
Generative Artificial Intelligence as an Educational Collaborator: A Next-Generation Resource
Ewoldt, K. B., & Flanagan, S. M. (2025) · Teaching Exceptional Children · doi:10.1177/00400599251340611 · Cited by 3
“AI can reduce time and “increase, effect, automatic processes” (Marino et al., 2023, p. 404) by developing and delivering efficient instruction and other supports for their students with disabilities.”
Kavashev, Z. (2025) · Journal of Computing in Higher Education · doi:10.1007/s12528-025-09463-3 · Cited by 5
“In the same vein, Marino et al. (2023) emphasized that the implementation of generative AI tools such as ChatGPT implies ethical concerns in terms of developing learners’ awareness of cybersecurity and data privacy in special education teacher preparation programs.”
AI-Driven Learning Analytics for Applied Behavior Analysis Therapy
Wong, C. M. V., Yum, Y. N., & Chan, R. Y.-Y. (2025) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2025.3637864 · Cited by 5
“AIresearchhasincreasinglyfocusedonapplicationsinspe-cialeducation[40],[41],[42].”
Lawrence, W., Nesbitt, P., & Clay, P. H., Jr. (2024) · International Journal of Arts Humanities & Social Science · doi:10.56734/ijahss.v5n5a6 · Cited by 6
Tlili, A., Altınay, F., Huang, R., Bozkurt, A., Burgos, D., Shehata, B., Altınay, Z., Wang, H., & Sani-Bozkurt, S. (2024) · Lecture notes in educational technology · doi:10.1007/978-981-97-8638-1_3 · Cited by 4
Zhang, L., Carter, R. A., & Lim, S. N. (2025) · Journal of Special Education Technology · doi:10.1177/01626434251349405 · Cited by 4
“This growing capacity of LLMs provides promising avenues for improving access to general education curricula and creating personalized learning experiences for students with disabilities (SWDs; e.g., Marino et al., 2023).”
Prasetyanto, D., Suhardjanto, D., Probohudono, A. N., et al. (2024) · Journal of Ecohumanism · doi:10.62754/joe.v3i8.5450 · Cited by 3
On the logic of agent’s emotions
Wang, Y., Liu, Z., Liu, T., et al. (2024) · Cognitive Systems Research · doi:10.1016/j.cogsys.2024.101281 · Cited by 2
Istiawanto, Y., Hamdi, S., Zafrullah, Z., & Sembiring, Y. K. (2024) · Jurnal Penelitian Pendidikan IPA · doi:10.29303/jppipa.v10i12.8650 · Cited by 2
“Special Education also benefits greatly from these technologies, ensuring all students can learn according to their abilities (Marino et al., 2023).”
The experience of using AI-generated social stories as a behavioral intervention tool for children
Zaklas, N., & Gazit, T. (2026) · Computers & Education · doi:10.1016/j.compedu.2026.105723 · Cited by 2
Özel Eğitim Öğretmenlerinin Yapay Zeka Farkındalık Düzeylerinin İncelenmesi
Hocaoğlu, A. Y. (2025) · Journal of Individual Differences in Education · doi:10.47156/jide.1638417 · Cited by 2
Yetişensoy, O. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13814-z · Cited by 3
“5 Results, discussion, limitations, and suggestions”
Aydoğan, A., & Kaptanlar, Y. S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-96-8898-2_37 · Cited by 2
Fu, Y., & Weng, Z. (2026) · Teaching and Teacher Education · doi:10.1016/j.tate.2026.105594 · Cited by 3
Inclusive Education in the Artificial Intelligence Era
Salhab, R. A. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-7255-5.ch001 · Cited by 4
Mo, D., & Mo, B. (2024) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns-2024-3059 · Cited by 2
The Application of Artificial Intelligence in Classroom: Advantages, Challenges, and Suggestions
Pan, Y. (2024) · Journal of Education Humanities and Social Sciences · doi:10.54097/zmgq6m15 · Cited by 2
Reflections on AI-Augmented Professional Development in Special Education
King, S. A. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-5538-1.ch016 · Cited by 3
Zhang, L., Jackson, H., Yang, S., Qian, X., Carter, R. A., Diliberto, J., & Zhang, J. (2024) · Research Square · doi:10.21203/rs.3.rs-5339247/v1 · Cited by 4
Cunningham, W. F., & Zhang, K. (2024) · Journal of Teacher Education and Lifelong Learning · doi:10.51535/tell.1568767 · Cited by 1
Impact of Artificial Intelligence on Teaching Learning Among Lecturers in Special Education
OLADIMEJIO, O. O., & Ogunwale, O. R. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5550-4.ch002 · Cited by 1
Integrating Artificial Intelligence into High School STEM through Plant Identification
Limjumroonrat, R., Yasri, P., & Suthichayapipat, A. (2025) · International STEM Education Conference (iSTEM-Ed) · doi:10.1109/istem-ed65612.2025.11129452 · Cited by 1
“In special education, deep learning has enabled the creation of personalized assistive technologies that support diverse learning needs [5].”
Allag, I., Manukyan, A., Dinet, J., et al. (2025) · doi:10.1145/3746175.3746205 · Cited by 1
Adigun, O. T., Haihambo, C. K., Veii, K., Hengari, J. U., & Simasiku, E. L. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7508-3.ch001 · Cited by 2
García, G. Á., Téllez, L. S., Díaz, M. H. R., & Rangel, F. A. H. (2026) · Education Sciences · doi:10.3390/educsci16010153 · Cited by 4
“Furthermore, AI tools also enhance the adaptability of educational technology (Cardona et al., 2023) and assist in research (Marino et al., 2023).”
Addressing the Void of AI Policies in Education for Students With Specific Learning Disabilities
Shin, M., Deniz, F. K., Watson, L., Dieterich, C. A., Ewoldt, K. B., Johnson, F., Kong, J. E., Lee, S. H., & Whitehurst, A. (2026) · Learning Disability Quarterly · doi:10.1177/07319487251412879 · Cited by 1
“These tools process and generate natural language and images, enabling dialogue, summarization, and customized explanations and feedback in writing tasks for students with disabilities (Marino et al., 2023).”
Designing and Using Custom Chatbots to Support Students With Disabilities: An Educator’s Guide
Drelick, A. M., Woodfield, C., & Freedman, J. E. (2026) · Journal of Special Education Technology · doi:10.1177/01626434261420392 · Cited by 2
“Generative AI and custom AI tools provide limitless possibilities and advantages within special education (Harkins-Brown et al., 2025; Marino et al., 2023).”
Socio-Educational Impacts of Technology and Media on Youth Behavior and Learning
Ahmad, S. (2025) · VFAST Transactions on Education and Social Sciences · doi:10.21015/vtess.v13i4.2341 · Cited by 1
The Future of AI in Special Education
Kayyali, M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0101-1.ch007 · Cited by 2
Liu, X., Cao, T.-T., Kong, Y., et al. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1772998 · Cited by 1
“…al. 10.3389/fpsyg.2026.1772998 Frontiers in Psychology 10 frontiersin.org effect of empowerment by AI technology on the effectiveness of special education services (Marino et al., 2023; Hussein et al., 2025), the moderating effect of parental involvement has received little empirical examination.”
InfluentialTang, K., Rappa, N. A., Nonis, K. P., & Ilyas, K. (2026) · Journal of Special Education Technology · doi:10.1177/01626434261448832 · Cited by 1
Berns-Conner, M., Dieker, L., & Roberts, S. K. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1790777 · Cited by 1
Self-citationArtificial intelligence for engaging neurodiverse learners in K-12 education: a systematic review
Uddın, M. M., Luo, F., & Moon, J. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2679063 · Cited by 2
Retrieval Chatbot to Support Idea Generation in Opinion-Based Persuasive Writing
Evmenova, A. S., Regan, K. S., Lewis, V., & Gafurov, B. S. (2026) · Journal of Special Education Technology · doi:10.1177/01626434261456274 · Cited by 1
Sakowicz, M., & Hamidi, F. (2026) · Universal Access in the Information Society · doi:10.1007/s10209-026-01370-3 · Cited by 2
Akemoğlu, Y., Roberts, E. B., & Xiong, J. (2026) · Autism · doi:10.1177/13623613261470855 · Cited by 2
“As a result, the current evidence base offers limited guidance regarding which AI-supported autism interventions demonstrate methodologically sound effects and broader implementation (Marino et al., 2023).”
Direct/Explicit Instruction and Social Constructivist Practices in The Inclusive Classroom
Matthews, A. M. (2024) · in education · doi:10.37119/ojs2024.v29i1.738 · Cited by 3
Potapiuk, L. M., & Sasiuk, A. (2024) · Educological discourse · doi:10.28925/2312-5829/2024.4.4 · Cited by 1
Navigating the Ethical Frontier
Öztürk, H. İ., Doğuş, M., Şahin, V., Çakmak, M., & Gürses, İ. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0122-8.ch011 · Cited by 1
Development and application of red cultural education resources based on artificial intelligence
Pei, Y., & Wen, L. (2025) · Journal of Computational Methods in Sciences and Engineering · doi:10.1177/14727978251369636 · Cited by 1
ChatGPT és más nagy nyelvi modellek (LLM-ek) biztonsági kérdései, szervezeti és társadalmi hatásai
Nagy, P. (2024) · Rendvédelem · doi:10.53793/rv.2024.2.1 · Cited by 1
“Ezek a modellek rengeteg paraméterrel működnek, amelyek a képzési folyamat során finomodnak, hogy hatékonyan illeszkedjenek a nyelv komplex mintáihoz és szerkezeteihez (Marino et al. 2023).”
Ithriyah, S., & Meilana, S. F. (2025) · ISLAMIKA · doi:10.36088/islamika.v7i1.5561
“By addressing infrastructure challenges, teacher readiness, and parent involvement, this technology has the potential to be a very effective tool to improve the quality of education in Indonesia.”
Influential
Metadata
- Authors
- Marino, M. T., Vasquez, E., Dieker, L., Basham, J., & Blackorby, J.
- Affiliations
- University of Central Florida (US)
- University of Kansas (US)
- Special Education, WestED, San Francisco, CA, USA
- Corresponding author
- Marino, Matthew T.
- Venue
- Journal of Special Education Technology
- Publisher
- SAGE Publications
- Published
- 2023-05-19
- Volume
- 38
- Issue
- 3
- Pages
- 404-416
- ISSN
- 0162-6434 (ISSN-L)
- 2381-3121
- OpenAlex
- W4377097363
- Semantic Scholar
- e5a17b3bb700428309dbfc9b223c78e436918566
- Cited by
- 142
- References
- 34
- Citations by year
- 2026: 48 · 2025: 44 · 2024: 42 · 2023: 8
- FWCI
- 33.37
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Special Education Technology (publishedVersion, other-oa)
- Funders
- Office of Special Education Programs, Office of Special Education and Rehabilitative Services (#H327F200008)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Education › Educational Technology in Learning
- Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
- Keywords
- artificial intelligence, special education, assistive technology, writing software, AI writing tools, ethical considerations, education policy, initial teacher education, technology implementation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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