Evaluation questions for choosing AI tools

Social and Technical Evaluation of AI Educational Tools

Social Aspects

• Equity & Accessibility: Do the tools cater to diverse learners, including those with limited digital literacy?
• Bias & Fairness: Are AI-driven recommendations unbiased, or do they reinforce social and linguistic biases?
• Teacher & Learner Engagement: Does AI enhance or replace human interaction?

Technical Aspects

• Scalability: Can the tool handle different class sizes and learning levels?
• Interoperability: Does it integrate well with existing educational platforms?
• Reliability & Performance: How stable is the AI model in real-time learning environments?

User Experience Assessment

• Ease of Use: Is the interface intuitive for educators and learners, including those with limited tech skills?
• Language Support: Does the tool support multiple languages, including less commonly spoken migrant languages?
• Adaptability: Can the tool personalize learning experiences based on users’ needs?
• Teacher Workload: Does AI reduce or add to educators’ workload?

Explainability of AI Tools for Education

• Transparency: Do educators and learners understand how AI decisions (e.g., learning recommendations) are made?
• User Control: Can teachers override AI decisions if needed?
• Feedback Mechanisms: Does the tool provide clear explanations for its suggestions or results?

Cultural Appropriateness of AI Tools

• Content Sensitivity: Are educational materials culturally inclusive and free from stereotypes?
• Learning Context: Does the AI tool account for different educational backgrounds and learning styles of migrants?
• Community Involvement: Are local educators and migrant communities involved in the tool’s development or adaptation?

Implementation Cost Analysis

• Upfront Costs: Licensing, hardware, and software expenses.
• Maintenance & Training: Costs of teacher training, updates, and IT support.
• Long-Term ROI: Does AI improve learning outcomes and efficiency enough to justify the investment?

Privacy and Security Evaluation

• Data Protection: How does the tool handle personal and sensitive learner data?
• Compliance: Does it meet GDPR, FERPA, or other relevant privacy laws?
• Risk of Misuse: Could the AI system be exploited for surveillance or discrimination?
• User Consent: Are learners and educators fully informed about data usage?