While AI excels at evaluating structured data in traditional assessments like true/false, multiple-choice, and short-answer questions, it struggles with non-text based assessments with graphic resources, including diagrams, posters, maps, and artwork. These tasks require an understanding of creativity, spatial relationships, and aesthetics—areas AI finds difficult to interpret. The same challenges apply to three-dimensional projects like models and dioramas, where subjective judgment and contextual interpretation are key. Due to AI's limitations in recognizing visual cues and creative expression, meaningful evaluation of these works typically requires human insight.
AI’s ability to assess performance-based learning assignments is another example of where it comes up short. The complexity of classroom environments, with multiple speakers, background noise, and accent variations, presents significant challenges for AI in processing audio accurately. It struggles to assess verbal reasoning, presentations, and group work, which offer key insights into student comprehension and engagement. Even if AI could overcome the problems associated with interpreting a student’s performance-based tasks, it would still lack the holistic perspective needed to fully understand a student’s development. This is due to its reliance on structured inputs like written assignments or test scores.
Class activities like historical reenactments, presentations, and experiments involve complex skills—collaboration, creativity, and real-time problem-solving—that AI struggles to evaluate. These tasks often include non-verbal communication and peer interaction, which are difficult for AI to quantify. Performance-based activities, such as physical education, dance, or music, require assessing motor skills, timing, and creativity, areas where AI falls short. AI's inability to assess these varied tasks highlights its limitations in providing a comprehensive evaluation of student learning.
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