AI’s Limitations in Teaching: A Special Education Perspective
In a recent article, Peter Greene articulates the significant shortcomings of generative language algorithms, often marketed as AI, in the educational field. Greene highlights the crucial aspects of teaching that AI fails to address, emphasising the irreplaceable value of human interaction, adaptability, and relationship-building in effective education. Reflecting on these points, I am reminded of my own experiences and insights shared in my Universal Design for Learning (UDL) series last year. As a Resource Specialist Program (RSP) teacher, I have firsthand experience with the complexities of supporting diverse learners, particularly those with Specific Learning Disabilities (SLD). Building upon Greene’s thesis, today’s article will delve into how these issues are addressed within the UDL framework and from a special education perspective.
Greene’s Main Points
Greene’s article underscores several key limitations of AI in the context of teaching:
Human Interaction and Understanding: Greene argues that AI lacks the capability to engage in meaningful, adaptive dialogue with students. Human teachers can ask probing questions to understand a student’s thought process and provide tailored feedback, something AI cannot replicate.
Lesson Planning: AI-generated lesson plans often fall short because they rely on summarising information without understanding the pedagogical strategies and student needs that effective lesson planning requires.
Addressing Student Mistakes: Effective teaching involves understanding the underlying reasons for a student’s mistakes and guiding them through their misconceptions. AI can identify incorrect answers but struggles to provide the nuanced guidance needed to correct these errors.
Complexity of Teaching: Teaching is not merely about delivering content but also about assessing student progress, understanding their learning processes, and adapting instruction accordingly. AI can handle simple tasks but fails with more complex educational challenges.
Relationship Building: Greene emphasises the importance of relationships in education. Students benefit from human interaction, which helps them develop critical thinking and self-teaching skills. AI lacks the ability to form these relational connections and provide emotional support.
Addressing the Issue in My UDL Series
In my UDL series last year, I addressed many of the issues Greene highlights, specifically in the context of supporting students with IEPs. UDL, or Universal Design for Learning, is a framework designed to improve and optimise teaching and learning for all students, based on scientific insights into how humans learn. Its core principles—providing multiple means of representation, action and expression, and engagement—align closely with the human-centered approach Greene advocates.
Multiple Means of Representation: In my series, I discussed the importance of presenting information in various ways to account for diverse learning styles. For instance, in the featured geometry lesson, providing visual aids, models, and diagrams helps students with SLD grasp complex concepts that verbal explanations alone might not convey.
Multiple Means of Action and Expression: Allowing students to demonstrate their knowledge in different ways—through writing, speaking, drawing, or designing—removes barriers to learning. For students with SLD, this flexibility is crucial. For example, offering speech-to-text tools can help students with language processing difficulties express their understanding more effectively.
Multiple Means of Engagement: Stimulating interest and motivation through options for self-regulation and sustaining effort is essential. In the series, I highlighted strategies to engage students actively, such as using manipulatives in math lessons or incorporating interests and hobbies into learning activities.
Building Upon Greene’s Thesis from a Special Education Perspective
As an RSP teacher, my role involves working closely with general education teachers to provide structures, supports, and accommodations for students with IEPs. This collaborative approach is essential for creating an inclusive learning environment where all students can succeed. Building upon Greene’s thesis, here’s how the principles of UDL and the unique perspective of special education further underscore the limitations of AI in teaching.
Personalized Instruction and Adaptive Support: One of the primary roles of an RSP teacher is to offer personalised instruction tailored to each student’s needs. This involves not only understanding their academic challenges but also their social and emotional needs. For example, students with SLD might struggle with multi-step procedures or abstract concepts. By breaking down instructions into smaller, manageable steps and providing concrete examples, teachers can help these students build confidence and competence. AI lacks the ability to adapt instruction dynamically based on real-time feedback and nuanced understanding of a student’s needs.
Relationship Building and Emotional Support: Building strong relationships with students is a cornerstone of effective teaching, particularly in special education. Students with learning disabilities often face significant challenges and frustrations. Having a trusted adult who understands their struggles and believes in their potential can make a profound difference. This relational aspect of teaching is something AI cannot replicate. For instance, a student who feels overwhelmed by a difficult task might benefit from a teacher’s encouragement and reassurance, helping them to persevere and succeed.
Understanding and Addressing Misconceptions: Effective teaching involves diagnosing and addressing misconceptions. When a student makes an error, it is crucial to understand why. Is it due to a misunderstanding of the concept, a procedural mistake, or a gap in foundational knowledge? As an RSP teacher, I spend considerable time analysing student work, observing their problem-solving processes, and asking questions to uncover the root cause of errors. This diagnostic process is complex and nuanced, requiring a level of insight and adaptability that AI currently lacks.
Creating Inclusive and Accessible Learning Environments: UDL emphasises the importance of designing learning environments that are accessible to all students from the outset. This proactive approach contrasts with the reactive nature of AI-generated accommodations, which often require students to struggle and fail before supports are provided. By anticipating potential barriers and incorporating supports into lesson plans, teachers can create a more equitable learning environment. For example, providing written instructions along with verbal explanations can help students with auditory processing difficulties.
Professional Judgment and Flexibility: Teaching requires professional judgment and flexibility. Educators must continually assess the effectiveness of their instructional strategies and make adjustments as needed. This iterative process of reflection and adaptation is central to effective teaching. AI, with its reliance on pre-programmed algorithms and data, lacks the ability to exercise professional judgment and respond flexibly to the unique and evolving needs of students.
Final Thoughts …
Greene’s critique of AI in education highlights the irreplaceable value of human teachers. Whilst AI can assist with certain tasks, it cannot replicate the complex, adaptive, and relational nature of effective teaching. As an RSP teacher, my experiences align with Greene’s arguments. The principles of UDL and the specialised strategies used in special education underscore the importance of personalised, human-centred approaches to teaching. By building strong relationships, providing adaptive support, and creating inclusive learning environments, educators can help all students, particularly those with SLD, succeed. AI may have its place in education, but it cannot replace the essential human elements that make teaching a profoundly impactful profession.