When it comes to in-house professional development on technology, schools often lean on younger teachers, assuming they have more experience using technology in their personal lives. This same thinking can backfire when it comes to AI. Artificial intelligence is making waves at every level of education by offering new tools to support teaching and learning. However, the responsibility of training faculty members on AI shouldn’t automatically fall to younger teachers. A recent study on junior consultants’ experiences with generative AI in the workplace suggests that younger professionals may not yet have the depth of knowledge required to train their senior colleagues effectively. While this study wasn’t specific to education, I think we can easily make the connection.

Challenges of Relying on Younger Teachers for AI Training

Much like in management consulting, where junior consultants lack advanced experience with AI, younger teachers might not fully grasp the complexities of these AI technologies.

The paper suggests that, instead of relying on younger workers, organizations should focus on broader training strategies. Schools can apply this finding in a few key ways:

• Offer Comprehensive AI Training for All Staff: Schools should provide training programs that cover not just the “how” of using AI tools but also the “why” — focusing on ethical considerations, such as data privacy, the risk of bias in AI algorithms, and the importance of human oversight, as well as the technology’s limitations and its impact on student learning. Training should be multi-layered to ensure that experienced teachers, who might be hesitant or skeptical, feel fully informed and comfortable with the technology.

• Implement AI with Clear Guidelines and Oversight: School districts should establish guidelines on when and how to use AI tools. by emphasizing the importance of balancing AI tools with professional judgment. By doing so, they can help all teachers, regardless of experience level, use AI tools in ways that support, rather than replace, their expertise.

Why a Multi-Level Training Approach is Key

The paper’s authors recommend going beyond human-computer interaction training by urging organizations to consider interventions at the system and ecosystem levels. In a school context, this could mean:

• System-Level Support: Schools need more than just tool-specific training; they require support systems that help teachers adapt to AI thoughtfully. This might include having designated tech support staff or coaches who can answer questions, assist in troubleshooting, co-plan lessons with teachers to incorporate AI effectively, and provide ongoing workshops or one-on-one sessions to ensure teachers feel comfortable and supported in their use of AI tools.

• Policy and Ethical Considerations: School administrators should lead in setting policies that protect both students and teachers when using AI. For example, privacy concerns, data usage, and the potential for bias in AI tools should be addressed openly, with administrators ensuring that all staff members are aware of these issues.

• Focus on Transparent and Responsible AI: Developers of AI tools for schools should work closely with school districts to create tools that are safe, secure, and easy to bring into the classroom. This transparency will help teachers at all experience levels feel more confident in the technology.

Bringing AI into classrooms brings both exciting possibilities and complex challenges. However, expecting younger teachers to train experienced colleagues on this technology may not be the best approach. A successful AI integration strategy should prioritize multi-level training and provide clear guidelines and support. By taking a thoughtful, inclusive approach to AI training, schools can equip all teachers to use AI responsibly and effectively to ensure this technology enhances, rather than disrupts, the educational experience.