Announcing the Revised Value Add of Technology on Teaching Framework

Education technology has changed significantly in the last two years with the emergence of widely accessible generative artificial intelligence tools. Now, AI is not only influencing the learning experience but also reshaping educators’ understanding of their role in cognition and knowledge creation.

For educators and leaders, these dynamics raise a deeper question: How do we make sound, ethical instructional decisions when the tools are powerful, the pace is relentless, and the stakes for learning are real?

Our new edition of the Value Add of Technology on Teaching framework aims to clarify what must happen next.

The Bigger Picture for Educators and Leaders

When we launched the first version of the Value Add of Technology on Teaching with Google for Education in spring 2023, the edtech landscape was overwhelming.

Researchers estimated that:

• There were more than 8000 verified tools in the edtech marketplace
• School systems were using more than 1,400 tools per month
• Teachers and students were using more than 140 individual tools per year.

Despite unmatched levels of edtech use and growing investment in developing new tools, there was an urgent need to better understand the impact technology could have on teacher practice and student learning.

Now that tools are beginning to participate in drafting, revising, and generating ideas, teachers are no longer the sole instructional intermediary between students and knowledge. That shift carries relational implications.

Teachers are navigating new questions about authorship, ownership, and voice.

1. When AI contributes to a draft, who owns the thinking?
2. When feedback is automated, what happens to the relational loop between teacher and student?
3. When efficiency becomes the default goal, how do we sustain the moments of dialogue, struggle, and human connection that anchor deep learning?

These questions emerge directly from the evolving role of the educator. When AI mediates parts of the thinking process, it also mediates parts of the relationship. The patterns of interaction between students and teachers, and among students themselves, begin to change in subtle ways.

That is the real leadership challenge. AI will not improve teaching and learning by default. Its impact depends on the quality of the decisions leaders make about how it is used and on how those decisions shape the culture of classrooms and systems.

Success requires three important leadership moves:

1. Start with Instructional Value, Not Tools.

Before expanding platforms or drafting policy, clarify what high-quality learning looks like and how AI is expected to support rigor, student thinking, and productive struggle. When instructional intent is clear, technology decisions become more coherent and less reactive.

2. Invest in Educator Judgment as a Core Capacity.

AI increases the need for professional discernment. Educators need shared language, time, and models to evaluate whether AI is strengthening or substituting for thinking. Capacity building is not optional; it is foundational to sustaining trust and maintaining instructional authority in a teacher–AI–student dynamic.

3. Establish Guardrails That Build Trust and Enable Thoughtful Use.

Clear expectations around privacy, ethics, and instructional boundaries reduce uncertainty and enable responsible experimentation. Guardrails should address not only compliance, but also community trust: how AI use is communicated to families, how students are taught to disclose and reflect on AI use, and how access is monitored across classrooms. When governance reinforces human judgment rather than replacing it, trust grows.

The revised Value Add of Technology on Teaching framework was designed to provide the frame for this thinking and leadership. It offers leaders a shared language and practical lens for evaluating how AI is showing up in classrooms and whether it is truly strengthening teaching and learning.

Why We Revised Our Guidance

Over the past year, we stepped back to listen. In conversations with teachers and leaders across districts, we heard a consistent call for clarity.

• Nearly 9 in 10  educators said they want stronger decision support for technology integration, not simply access to more platforms.
• More than half asked for concrete examples of how AI can strengthen instruction without replacing professional judgment.

Those insights made clear that our original framework needed to evolve to better support coherent, instruction-centered decision-making in the age of AI.

While the core tenets of the original framework remain relevant, this moment demands sharper coherence in how AI tools are selected, used, and governed. The second edition is anchored in a simple but critical shift: AI must be positioned not primarily as an efficiency tool, but as a driver of instructional coherence.

Rather than asking what tools can do faster, the framework invites leaders to ask what kind of instructional value technology is adding—whether it is expanding capacity (Do More), strengthening instructional quality (Do Better), or enabling learning experiences that were previously difficult to design (Do New).

Ultimately, the framework is designed to help leaders move from reactive adoption to intentional, learning-centered decision-making.

Take Action

In a moment defined by speed and uncertainty, clarity and judgment are the most valuable tools we have. We invite you to explore the framework, test it against your own context, and use it as a thinking partner as you make decisions that will shape teaching and learning in the era of AI.

Take a step toward more intentional, instruction-centered AI leadership today.