Friends and colleagues,

This year, I am conducting the AI in Action Learning Tour to see student-facing AI solutions in classrooms and to learn about what the product developers are wrapping around the large language models in the design of the apps. I am doing this because I believe, though educators will always be instrumental in teaching and learning, student-facing AI can make it easier for teachers to be great. Earlier this year, I shared early patterns from what I am learning and committed to sharing more as I go. 

After completing another round of product conversations, I see more clearly three defining instructional choice points that I predict will drive trends in the year to come. 

1. Product designers either presume teacher autonomy or design for common implementation at a district or school level. The products that presume teacher autonomy are largely selling to teachers with freemium models, then encouraging districts to purchase enterprise accounts. Teacher-facing tools include functions like lesson planning, text selection, and materials development. The student-facing sides of these products are designed for flexible use-cases. Other products presume that districts are giving teachers direction about what materials they should be using, with day-by-day or week-by-week pacing expectations. Their products are designed to become part of that model. They are selling, not classroom-based, but district-wide solutions. Products are designed to fit into a defined instructional model; they intentionally do not add features that would encourage modification. Currently, more AI products on the market are designed around teacher autonomy than common implementation. 
   
   
2. Product designers either design features to “meet students where they are” or anchor to grade-level instruction. Many educators and parents hold the belief that good teachers target instruction to the student’s current academic level. Fueled by reports like the Opportunity Myth and analysis of the difference between acceleration and remediation, many state and district leaders have been trying to replace that belief with the idea that all students can meet high expectations and will make more academic progress if they’re given access to grade-level instruction. Product developers seem to subscribe to one belief or the other—those designed to support teachers in “meeting students where they are” offer tools like text releveling. Those designed to provide grade-level instruction steer away from such choices and launch all instruction from a grade-level starting point. There are currently more products that try to “meet students where they are” than anchor to grade-level instruction. 
   
   
3. Products are either built to do a single instructional job or designed to have multiple coherent touch points. Some products try to do one job well, usually across grades and subjects—for example, nailing exit tickets. Other solutions try to help with a full stack of instructional jobs—for example, using Tier I instruction and assessment with teacher PL in middle school ELA. Most products start with a lean design but end up adding more functionality. 

In 2026, I predict that student learning gains will correlate with the quality and specificity of instructional leadership and teacher support more than product selection; however, it will be a year that shapes AI’s future role in teaching and learning. Here is what I suspect we will see:

1. I predict that the teacher autonomy tools will win the early adoption surge but will begin to lose market share to products designed for district-wide use. It will be much harder for teacher autonomy tools to prove impact than common use solutions—and the more comprehensive instructional model-based tools will start to show outcomes more clearly. AI has some superpowers in analysis and personalization that can improve coherence when used across classrooms. Common applications can also give leaders visibility into what students are doing and learning across the school or system. Districts will only be able to leverage those insights if everyone is using the same products in similar ways. Ultimately, districts hold the power of the purse. I suspect we will see more muscular district guidance on what tools they expect teachers to use and which tools they don’t want to see influencing instruction.
   
   
2. I predict that existing curriculum, assessment, and intervention providers will start offering AI-powered features. I sense many companies have been waiting to see how AI unfolds, but they’ll begin increasing pressure to implement their own AI solutions—as well as a sense of possibility about how AI can improve their services. They will have a leg up with an existing client base, but I don’t know how much districts will trust evolutions versus purpose-built products. I predict some will allow for the “meet students where they are” functionalities, while others will resist demand for these features and this, eventually, becomes a flash point in selection.
   
   
3. I predict that instructional super products will begin to emerge that transcend categories (e.g., serving as curriculum, assessment, and intervention with embedded PL all in one). I predict that district and state leaders will get most excited about the way these superproducts can improve coherence, reduce the volume of testing, and displace cost categories rather than just adding more log-ins or budget line items. 
   
   
4. I predict that some districts, teachers, and parents will opt out of AI entirely. The pressure against screen time is real, and the risks of cheating that come with access to any browser will likely lead to bifurcation of interest and use. I think some parents will choose schools based on technology use or disuse. I predict this will become a factor in teacher job interest—some teachers will favor districts that give them access to AI tools, and others teachers will favor districts where they can teach “old school.” Ultimately, I do not think this will be a large group because the benefits of strong tools to student learning and the teacher job will be so evident and significant. 

My record of predictions is wrong as often as it is right. Either way, I look forward to learning more together in 2026. I wish you and your families a healthy and happy holiday season.

One step at a time, together,

Emily