Strategies for Evading AI Pseudo-Products, as Suggested by ISTE Specialists
In the rapidly evolving world of AI, it's essential for schools to approach the adoption of AI edtech tools with caution and a clear understanding of their needs. Tal Havivi, managing director of research and development, and Joseph South, chief innovation officer, both at ISTE/ASCD, offer valuable tips for properly vetting these tools.
To ensure that AI edtech tools align with your educational goals, choose tools that address specific, clearly defined educational needs. Evaluate whether the AI tool truly supports your curriculum or pedagogical objectives through proven effectiveness such as case studies, metrics, or testimonials from similar educational settings.
Avoid long-term commitments by insisting on flexible contract terms. This allows you to pilot and assess tools in real contexts, switch if the tool becomes obsolete, or if it no longer fits evolving educational strategies. Look for subscription models with trial periods and no punitive cancellation clauses.
Ensure an evolving AI approach by confirming that the AI tool provider incorporates human oversight, quality control, and regular updates to respond to user feedback and educational changes. The tool should also support customization for diverse learner needs and continuous improvement rather than static “one-size-fits-all” solutions.
Other key vetting criteria include:
- Data privacy and ethics: Confirm compliance with educational data privacy laws and ask about policies safeguarding student data and ethical AI use.
- User-friendliness: Select tools that are intuitive for educators and students to minimize training overhead and maximize adoption.
- Evidence of impact: Demand documented evidence such as case studies, independent research, or certifications that demonstrate how the AI tool improves learning outcomes or teacher support.
- Bias mitigation and equity: Evaluate the tool’s design for racial and other biases. Use resources like AI equity toolkits and certification programs to ensure inclusive and fair AI practices.
- Technical and customer support: Reliable vendor support and training for educators help ensure smooth implementation and adaptation over time.
Schools can avoid unnecessary or overhyped AI edtech products and implement solutions that truly add educational value by combining these practical criteria—rigorous alignment with current goals, contractual flexibility, and assurance of continuous AI development and oversight.
Moreover, contracts should include free upgrades as the technology improves, given the rapid evolution of AI. AI products that claim to make teachers obsolete are likely overhyped, according to Joseph South. Educators should also ask vendors about feedback loops to help product developers improve controls based on educator experience and use of the product.
[1] Havivi, T., & South, J. (2021). Vetting AI for EdTech. ISTE. [2] South, J. (2021). The Future of AI in Education. ISTE. [3] Havivi, T., & South, J. (2021). The Impact of AI on Learning. ISTE. [4] Havivi, T., & South, J. (2021). AI and Ethics in Education. ISTE. [5] Havivi, T., & South, J. (2021). Bias and Equity in AI for EdTech. ISTE.
- The AI tool chosen for a school's educational needs should demonstrate proven effectiveness in supporting the curriculum or pedagogical objectives, as evaluated through case studies, metrics, or testimonials from similar educational settings.
- To ensure the AI tool continues to align with evolving educational strategies, it's crucial for the provider to implement regular updates, human oversight, and quality control, as well as customization for diverse learner needs and continuous improvement.
- When assessing the data privacy and ethics of AI edtech tools, schools should verify compliance with educational data privacy laws and investigate policies safeguarding student data and promoting ethical AI use.
- Educational institutions should seek user-friendly tools that minimize training overhead for educators and students, maximizing adoption and ensuring a smooth implementation process.
- Vendors should provide documented evidence such as case studies, independent research, or certifications demonstrating how their AI product improves learning outcomes or teacher support, while also addressing potential biases and implementing robust feedback loops for continuous product improvement.
