The Promise and Perils of AI in Special Education: A Critical Analysis

The integration of Artificial Intelligence (AI) into special education has been heralded as a potential game-changer, promising personalised learning experiences and reduced administrative burdens. However, the road to effective implementation is far more complex than many realise. As an autistic, non-verbal special education teacher, I bring a unique perspective to this discussion. My background as a gestalt language processor, coupled with my academic qualifications provides me with a multifaceted view of the challenges and opportunities in this field.

Whilst AI undoubtedly shows promise in special education, current approaches are fraught with legal, ethical, and practical challenges that require careful consideration. Today’s article aims to critically examine these issues and propose a more nuanced, inclusive approach to AI integration in special education.

The Current Landscape of So-Called “AI” in Special Education

The special education sector is increasingly being targeted by tech companies peddling what they call “AI,” but which in reality comprises chatbots, predictive modelling, and large language models (LLMs). These tools range from adaptive learning software to automated systems claiming to streamline the Individualised Education Program (IEP) process. However, the reality often falls drastically short of the glossy marketing promises.

Recent reporting, such as the EdWeek article “Can AI Help With Special Ed.?,” tends to present an overly optimistic view, often penned by authors with limited expertise in special education or computational technologies. These pieces frequently overlook critical issues like data privacy, the limitations of current models, and the unique needs of diverse learners. More concerningly, such articles can serve as fodder for ‘evidence mills.’ creating a cycle of self-reinforcing, potentially unreliable information.

The gap between marketing promises and classroom realities is stark. Whilst tech companies tout revolutionary solutions, many special educators find themselves grappling with tools that are either too generic to be truly useful or so complex that they require significant time investment to implement effectively. Moreover, the focus on analytic processing in many of these tools overlooks the needs of gestalt processors (like me), who make up a significant portion of learners. This disconnect underscores the need for more critical, nuanced discussions about the role of these technologies in special education.

Legal and Privacy Concerns

The implementation of so-called “AI” technologies in special education is fraught with significant legal and privacy concerns. At the forefront are regulations such as the Family Educational Rights and Privacy Act (FERPA) and the Individuals with Disabilities Education Act (IDEA), which mandate strict protection of student data, particularly for those with disabilities.

The use of chatbots, predictive models, and LLMs for paperwork and data management in special education poses substantial risks. These systems often require vast amounts of data to function effectively, potentially compromising student privacy. For instance, using an LLM to draft IEPs could inadvertently expose sensitive student information to the model’s training data, violating FERPA regulations.

Moreover, the black-box nature of many of these technologies makes it challenging to ensure compliance with IDEA’s requirements for individualised, ‘evidence-based’ interventions. There’s a real danger that reliance on these tools could lead to cookie-cutter approaches that fail to meet the unique needs of each student, thereby contravening the spirit and letter of IDEA.

The need for stringent data protection in special education cannot be overstated. Students with disabilities are often particularly vulnerable, and their data requires extra safeguarding. Yet, many ed-tech companies’ data practices are opaque at best, raising questions about data ownership, usage rights, and long-term storage.

Educational institutions must approach these technologies with extreme caution, ensuring that any implementation fully complies with all relevant regulations and prioritises student privacy above all else. The potential benefits of these tools must not come at the cost of compromising our ethical and legal obligations to protect our most vulnerable students.

The Data Dilemma

The efficacy of machine learning models, predictive algorithms, and LLMs in special education is fundamentally limited by the data they’re trained on. Currently, these datasets suffer from a severe lack of diversity and representation, particularly when it comes to students with disabilities and neurodivergent learners.

The training data for these systems predominantly represent neurotypical, English-speaking populations, embedding ableist biases into the core of these technologies. These biases often reflect a medical model of disability, which views differences as deficits to be ‘fixed’ rather than as natural variations in human neurology and functioning. Consequently, the resulting tools risk perpetuating harmful stereotypes and ineffective practices. Instead of supporting students with disabilities, these technologies may inadvertently amplify the challenges they face, working against the very goals they purport to achieve.

The needs of gestalt processors and other neurodivergent learners are often completely overlooked in these datasets. As a gestalt processor myself, I can attest to the frustration of encountering educational tools that cater exclusively to analytic processing styles, despite gestalt processors comprising approximately 40% of the population.

Compounding this issue is the fragmented nature of educational data within school systems. In my role as a special education teacher, I must navigate multiple siloed databases: one for grades and attendance, another for IEPs and services, and two more for standardised test scores. This fragmentation not only complicates the work of educators but also presents significant challenges for any attempt to create comprehensive, representative datasets for machine learning models.

The result is a technological landscape ill-equipped to address the diverse needs of special education students. Until we can develop more inclusive, comprehensive datasets that accurately reflect the diversity of learners - including neurodivergent students - and integrate our fragmented data systems, the promise of these technologies in special education will remain unfulfilled.

The Economics of Machine Learning in Education

The economic model underpinning the push for machine learning and LLMs in education is deeply problematic. Tech companies often employ a “free data for premium services” approach, seeking to acquire valuable educational data at no cost whilst charging substantial fees for their products. This is not altruism; it’s a calculated business strategy.

None of these developers are offering their products for free. Even when initial access is provided without charge, the long-term goal is to create dependency and extract profit. This model has the potential to exacerbate existing educational inequalities. Well-funded schools may gain access to these tools, whilst under-resourced institutions - often serving the most vulnerable students - are left behind (… and it’ll get worse under a Project 2025 regime).

The long-term implications of data ownership by tech companies are concerning. As these firms amass vast amounts of educational data, they gain significant power to influence educational practices and policies. This trend towards monopoly capitalism in educational technology poses serious risks. It could lead to a homogenisation of educational approaches, stifling innovation and diversity in teaching methods.

Moreover, the concentration of data and decision-making power in the hands of a few tech giants raises serious questions about privacy, accountability, and the future of education. We must critically examine whether we want profit-driven entities shaping the educational experiences of our most vulnerable students.

Research Challenges and Limitations

The field of special education technology faces significant research challenges that limit our understanding of its efficacy and impact. Chief among these is a chronic lack of funding for necessary studies, particularly those focusing on diverse learner populations and long-term outcomes.

The concept of “evidence-based” practices in education, often touted by tech companies, deserves scrutiny. Many of these practices are based on small-scale studies or action research that have never been successfully replicated. The reproducibility crisis in educational research casts doubt on the reliability of much of this ‘evidence.’

A stark example of these limitations is evident in my recent search of the What Works Clearinghouse for ‘evidence-based’ literacy development programmes for older English Language Learners. Shockingly, this search yielded zero results, highlighting a significant gap in our knowledge base for serving this crucial student population (I’ve addressed this in my book, Holistic Language Instruction).

There is an urgent need for more comprehensive, inclusive research that accounts for the full spectrum of learners, including neurodivergent students and those from diverse linguistic and cultural backgrounds. Without this, we risk developing and implementing technologies based on incomplete or biased data, potentially doing more harm than good.

Furthermore, research must extend beyond short-term academic outcomes to consider the broader, long-term impacts of these technologies on students’ social, emotional, and cognitive development. Only through such holistic research can we truly assess the value and appropriateness of these tools in special education settings.

Potential Benefits and Proper Implementation

Despite the challenges, certain applications of machine learning and LLMs in special education do show promise, particularly in the realm of personalised learning. For instance, autistic students might benefit from the ability to deep-dive into topics of interest. However, it’s crucial to implement safeguards to prevent learning from becoming too narrow in scope.

The importance of teacher expertise in implementing these technologies cannot be overstated. Educators must be empowered to use these tools as supplements to, not replacements for, their professional judgment. This is particularly crucial given that most current models still ‘hallucinate’ or generate inaccurate information quite frequently. For students who may lack the background knowledge to identify these errors, this poses a significant risk.

Strategies for ethical and effective use of these technologies in special education should include:

1. Rigorous vetting of tools for accuracy and bias

2. Continuous monitoring and adjustment of implementation

3. Prioritising data privacy and security

4. Ensuring tools are adaptable to diverse learning styles, including gestalt processing

5. Regular training for educators on both the capabilities and limitations of these technologies

Ultimately, any implementation should enhance, not diminish, the crucial role of skilled educators in supporting students with diverse needs.

The Role of Special Educators in Shaping AI Development

The input of experienced special education professionals is crucial in the development of educational technologies. However, the current approach of many tech companies is deeply problematic. As an experienced special educator and researcher, I’ve been contacted several times by developers seeking my expertise. Yet, when I inquired about compensation for my time or mentioned my hourly rate, I was quickly dismissed. This pattern reveals a troubling trend: companies want our participation and knowledge, but they’re unwilling to pay for it.

This exploitative approach not only devalues the expertise of special educators but also results in technologies that fail to meet the diverse needs of our students. We must advocate for the inclusion of diverse learning styles in the development process, ensuring that tools are designed with consideration for both analytic and gestalt processors, as well as the full spectrum of neurodivergent learners.

Special educators should demand a seat at the table - a paid seat - in the development of these technologies. Our expertise is valuable and should be compensated accordingly. Only through genuine collaboration between educators and developers can we ensure that these tools truly serve the needs of all learners.

Moreover, we must push for the development of technologies that support and enhance our teaching practices, rather than attempting to replace them. This includes tools that can adapt to different learning styles, provide meaningful data to inform our instruction, and respect the privacy and dignity of our students.

The role of special educators in this process is not just to provide input, but to be active, respected, and fairly compensated partners in shaping the future of educational technology.

Final thoughts …

The integration of machine learning, LLMs, and other technologies into special education presents both opportunities and significant challenges. We’ve explored the legal and privacy concerns, data dilemmas, economic implications, research limitations, and the crucial role of special educators in shaping these technologies.

As we look to the future, we must remain vigilant. Initiatives like Project 2025, which may gain traction depending on U.S. election results, could exacerbate existing inequalities. Its ‘performance-based’ approaches, coupled with the uneven distribution of educational technologies, risk further disadvantaging already marginalised students.

We must adopt a more critical, inclusive approach to these technologies in special education. This means demanding rigorous research, prioritising student privacy, compensating educators fairly for their expertise, and ensuring tools serve all learners, including gestalt processors and neurodivergent students.

My vision for the future of these technologies in special education is one where they augment, rather than replace, skilled teaching. Where they’re developed with input from diverse educators and learners, respect privacy, and adapt to various learning styles. Most importantly, where they serve to level the playing field, not widen the gap between the privileged and the disadvantaged.

The path forward requires vigilance, advocacy, and a commitment to equity. As special educators, we must lead this charge, ensuring that technological advancements serve all our students, not just a select few.