AI's Inability to Observe Actions And Behaviors Hinders Comprehensive Feedback
- Brian Woods
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- Dec 1, 2023
- 2 min read
Updated: Nov 4, 2024
AI systems face several significant limitations in providing classroom feedback as they generally lack the ability to observe how students engage with material, peers, and the teacher in a classroom setting. Teachers gather crucial insights from body language, facial expressions, and group interactions, student discussions, tone of voice, or other verbal cues —none of which an AI system can easily access.
The lack of AI’s capacity to assess engagement and personal development hinders its capability to offer context for feedback. In other words, AI lacks the ability to measure how engaged someone is or how much growth they have achieved. On the other hand, a teacher can gauge understanding or emotional states from the way students speak, which helps in adjusting instruction or feedback. AI systems, without access to these visual and auditory elements, are essentially blind to these subtle but critical aspects of communication.
Implementing systems that would enable AI to collect and analyze both visual and audio data in classrooms would be expensive, especially for K-12 schools with limited budgets. Installing cameras, microphones, and the infrastructure to process this data would be cost-prohibitive, making it impractical for widespread adoption.
Even if AI had access to classroom data, its ability to evaluate performance beyond text-based assignments is still limited. For example, AI struggles assessing art, projects and physical activity and creative expression. Human teachers can provide feedback on practical, subjective tasks in ways that AI cannot yet replicate, as current systems are focused predominantly on text or numerical data.
AI systems lack the ability to read and respond to the needs of students such as student backgrounds, learning preferences and personal challenges. Teachers can also adjust their feedback based on a student's frustration, excitement, or confusion—emotions AI cannot yet perceive or respond to effectively. This context allows for tailored feedback and interventions, something an AI system may struggle to replicate without deeper data integration and human judgment. While AI is proving helpful in many ways, its ability to assess participation, work ethic, and other critical components associated with personal development are still ways beyond its current abilities.
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