Early-career teachers in England are embracing artificial intelligence for classroom tasks at significantly higher rates than their experienced peers, according to new data from Teacher Tapp collected during the 2025-26 academic year. The findings reveal a generational divide in AI adoption that extends far beyond simple lesson planning, raising questions about how teaching practices are evolving and whether newer educators are reshaping what classroom technology looks like.

How Much More Are Early-Career Teachers Using AI Than Experienced Educators?

The gap is substantial across multiple school tasks. In June, 68% of early-career teachers (ECTs) said they had used AI for lesson planning or preparation during the previous month, compared with 54% of classroom teachers who were not in the Early Career Framework. The difference extended well beyond planning documents.

• Lesson Planning: 68% of ECTs versus 54% of other teachers used AI for this task, the largest gap observed
• Professional Development: Early-career teachers were twice as likely to use AI for continuing professional development or training, at 10% compared with 5%
• Scheduling and Seating: ECTs were three times as likely to use AI for timetables and seating plans, at 6% versus 2%
• Parent Communication: 18% of ECTs used AI to communicate with parents about individual students, compared with 11% of other teachers
• Marking and Feedback: 12% of ECTs used AI for marking or feedback, versus 7% of experienced teachers

The data came from responses by 359 early-career teachers and 2,739 non-ECT teachers, weighted to reflect national teacher and school demographics. Not every task showed a significant divide. AI use for curriculum development was level at 15% across both groups, and 33% of ECTs and 32% of non-ECT teachers used it to write pupil reports.

What Does This Generational Shift Mean for Schools?

The higher adoption rates among newer teachers suggest that AI is becoming normalized earlier in teaching careers. Early-career teachers are also less likely to report no AI use at all, at 18% compared with 23% of experienced teachers. This pattern hints at a broader cultural shift in how educators view technology as a tool for their own work, not just for student-facing activities.

However, the story becomes more complex when you consider the broader context of how schools are thinking about AI in classrooms. A significant debate is underway about whether AI should be used directly with students or primarily to support teacher work. Miguel Guhlin, a technology administrator and educator, has highlighted the importance of grounding AI decisions in evidence-based instructional strategies rather than simply adopting tools because they are available. He points out that many high-effect-size teaching strategies, such as reciprocal teaching and the jigsaw method, do not require screens at all.

The American Federation of Teachers, led by Randi Weingarten, released a 10-point plan in May 2026 that recommends no student-facing generative AI in elementary schools, with educator supervision required everywhere else. This guidance suggests that while teachers may be using AI to prepare lessons and manage their own work, the question of how AI should interact with students remains contested.

Are Early-Career Teachers Actually Happier With AI?

Interestingly, the data shows that early-career teachers report higher contentment than experienced teachers across the academic year, though the reasons are complex. Using a contentment scale from one to seven, Teacher Tapp tracked the proportion of teachers rating themselves at five or above. In June, 61% of ECTs reported higher contentment compared with 50% of other classroom teachers. The gap ranged from nine to 14 percentage points across the entire academic year.

Teacher Tapp attributes this difference partly to additional support available to early-career teachers, including reduced teaching timetables, mentoring, and structured support programs. The data does not establish whether AI use itself caused the higher contentment, or whether the broader support structures made the difference.

One persistent challenge affected both groups equally: student behavior. In July, 48% of early-career teachers said teaching and learning had largely stopped because of poor behavior during their most recent lesson, compared with 41% of non-ECT teachers. This 7-point gap narrowed significantly from earlier in the year, when it stood at 16 points in December. The data suggests that experience matters; teachers with more than 20 years in the classroom reported disruption in only 31% of their previous lessons, compared with 49% for those with less than five years of experience.

What Are Schools Learning From Data-Driven Decision Making?

Beyond teacher adoption patterns, schools are also using AI to analyze their own data more effectively. The challenge, according to Matt Jubelirer, general manager of education marketing at Microsoft, is aggregating data from disparate systems into a single place and making something of it. "The goal isn't more data collection. It's helping educators spend less time gathering information and more time using it to support student success," Jubelirer explained.

"The goal isn't more data collection. It's helping educators spend less time gathering information and more time using it to support student success," said Matt Jubelirer.

Matt Jubelirer, General Manager of Education Marketing at Microsoft

Districts that have implemented strong data governance policies are seeing measurable results. Weehawken Township School District in New Jersey, led by Superintendent Eric Crespo, ranked higher than 81% of school districts nationwide in reading performance and in the 99th percentile nationwide for rate of improvement in reading. Crespo attributes these results to adjusting instruction in the moment rather than waiting until the end of the year to identify struggling students.

"Data is never allowed to make decisions on its own; instead, it acts more like a check engine light. We triangulate it, we disaggregate it, and, at the end of the day, a human being still has to look a kid in the eye and tell a student, 'We believe in you,' regardless of what the numbers say," said Eric Crespo.

Eric Crespo, Superintendent of Weehawken Township School District, New Jersey

The district's approach emphasizes that AI-powered tools can help schools identify patterns and surface insights, but human expertise remains essential for interpreting results and making informed decisions. Crespo notes that the district was able to keep high-needs special education students in their own schools by using data to prove it had safe, specialized programming available, saving hundreds of thousands of dollars per year in external tuition and transportation.

How Should Schools Implement Data Governance and AI Responsibly?

Microsoft recommends that districts follow standard data governance policy best practices when implementing AI-driven insights:

• Collect Purposefully: Gather only the information needed for educational purposes, avoiding unnecessary data collection
• Limit Access: Restrict access to authorized staff only, with vendors given access to specific data rather than entire systems
• Use Aggregated Data: Employ aggregated or de-identified data wherever possible to protect student privacy
• Maintain Security: Implement strong security controls to protect sensitive information
• Be Transparent: Communicate clearly with families about how their data is used and stored

Weehawken Township's data governance policy was formed with assistance from a law firm and approved by multiple levels of stakeholders, including a technology administrator filling in district-specific details and a public board vote making it official. This multi-layered approach ensures that policies reflect both regulatory requirements and community values.

The broader picture emerging from these sources suggests that AI in education is not a single story but multiple stories happening simultaneously. Early-career teachers are adopting AI tools for their own work at higher rates, schools are using AI to analyze student data more effectively, and educators continue to debate what role AI should play in direct student instruction. The key difference between schools that succeed and those that fall behind, according to Crespo, is whether they treat data and AI "like a compliance checkbox instead of a compass."