Human education systems have evolved to integrate just about every type of student into the classroom and learning environment; however, there still remains considerable scope in making education more accessible to pupils with special requirements. While special education exists as an independent discipline that is often at the forefront of providing inputs to educational policymakers, the progress of edtech might be able to address outstanding challenges and concerns. Assistive technology has already made considerable changes to the way specially-abled students approach education. The increased availability of assistive learning aids has made course content adaptable to these students’ unique requirements. The arrival of artificial intelligence and machine learning in education further enhances the prospects of scaling assistive technologies and opens up the prospects of achieving better educational equity. Integrating AI learning options such as adaptive learning tools into existing special education curriculums can further provide support to students with unique learning requirements.
Apart from the inherent nature of AI technologies like analytics to point educators in the direction of what students need, some of these tools might also be able to mold content in ways better suited to students with special requirements. Enhancing AI learning adoption in special education strategies might be able to bridge existing gaps in the classroom and also make up for frequent teacher and expert shortages that the field often faces. While digital tools and AI tutors are not replacements for human teachers, they can still make for potent learning aids that several specially-abled students might benefit from.
Special Education and the Role of AI Learning
Special education and assistive technologies can benefit from AI tools such as analytics and personalized learning.
Students with special needs form a diverse group and fall under three broad categories that span developmental disabilities, learning disabilities, and physical disabilities. Special education aims to provide fair learning opportunities to these students and attempts to create parity in the learning environment. While several strategies and tools to achieve this have been developed over the years, technological advances have had a tremendous impact. Tools like intelligent braille printing systems, speech-to-text converters, and read-aloud technologies have improved accessibility for students that avail of special education. These frameworks are existing examples of assistive technology in the classroom. Interestingly, all of these tools have been influenced and directly aided by the proliferation of artificial intelligence and machine learning tools. With AI learning gaining prominence in the aftermath of language models becoming globally popular, the prospects of artificial intelligence enhancing assistive technologies are now greater than ever before. Alongside bringing attention to important deficiencies in current strategies, generative artificial intelligence can also become an active part of future solutions in solving challenges in special education.
Special needs students on the autism spectrum tend to interact with simplistic bots and elicit positive responses when provided with these tools. This is merely one example of the impact AI learning tools can have on students with social disabilities and special learning requirements. Similarly, accessibility can be enhanced by utilizing AI-generated content to create more student-centric material. Analytical approaches and big data, when combined with assistive learning tools, also act as potential diagnostic tools to zero in on student performance. More often than not, teachers might fail to notice learning disabilities in students and chalk it up to behavioral issues. However, when equipped with intuitive capabilities, intelligent special learning tools can be leveraged to detect these concerns in students. This ultimately results in early identification along with prompt care being provided to these students. The inclusivity brought about by AI assistive technologies in the classroom will simplify the approach for both educators and the overall institutional framework in their efforts to provide top-notch learning experiences for their students.
Integrating AI in Special Education: Prospects
A synergistic approach between existing special education protocols and AI tech will aid the creation of a robust learning environment.
Effective integration of AI into special education protocols will require the practical and classroom-level incorporation of assistive technologies. By balancing the components of human intelligence and machine learning protocols, teachers can create an amalgamated approach to provide a comfortable learning experience for students with special needs. Professional training programs and symposiums can allow educators to become acquainted with the intuitive methods offered by these technologies. Moreover, the human component is far more essential in the domain of educating students with special needs. Dehumanization of education needs to be avoided and addressed at every step of the integration process. Moreover, AI is also prone to its own set of shortcomings such as bias and hallucination, which, when overlooked, can seriously impact learning outcomes in students.
Continued research and development in these domains might provide more insights for combining both AI tools and education to aid specially-abled students. Among the areas for pointed inquiry is the possibility of AI being a potent tool in tracking and collating crucial student progress insights that can be of great use to academicians. Artificial intelligence algorithms have the potential to decipher patterns and predict outcomes more precisely than their human counterparts, and teachers leveraging these tools to aid student development will be an ideal use case for AI learning.
Assisted AI and the Future of Special Education
Retaining the human component in special education will be key to successful AI integration in the niche.
Though natural language processing and machine learning algorithms have come a long way in aiding humans’ approach to a more equitable learning environment, there is still much left to be achieved. Along with competing firms that are keen on developing the best AI chatbots in the market, there also exist several open-source options that are pushing the boundaries of democratization when it comes to AI. These efforts will be especially important as AI learning picks up pace and equitability garners center stage in education. Special education will be of specific concern as AI is adapted to fit the needs of students. Personalized learning and student analytics will possibly become the key demonstrators of artificial intelligence capabilities in the classroom. However, before these developments see the light of day, key considerations such as those surrounding academic integrity and the framing of regulations surrounding these novel technologies will have to be addressed comprehensively.