Risks of AI Errors and Wrong Decisions

Risks of AI errors and wrong decisions threaten education quality and fairness in 2026. Types of errors: - Educational diagnosis errors - Wrong evaluation decisions - Misleading academic path recommendations - Wrong data analysis - Incorrect student classification Causes: 1. Incomplete or biased training data 2. Incomplete algorithms 3. Not updating models 4. Educational context complexity 5. Multiple factor interference Serious consequences: - Directing students to wrong paths - Unfair and unjust evaluations - Loss of educational opportunities - Negative impact on student futures - Waste of talents and abilities Correction difficulty: - Late error discovery - Difficulty tracing decision reasons - Permanent impact on records - Resistance to changing decisions - High correction costs