By now, many educators have recognized the potential of using generative artificial intelligence (AI) in key teaching functions. Although opinions are still forming, there’s no denying that this technology has the potential to streamline many of the tasks teachers do each day. Whether it’s generating content, adjusting assessments, modifying materials to meet various instructional needs, or providing support in student feedback, AI can be a valuable assistant.
When teachers are first introduced to teacher-facing platforms like School.ai and Magic School, they gain access to even more specific ways AI can assist. Many tools on these platforms focus on content generation, such as creating multiple-choice assessments, writing exemplars, or crafting text-dependent questions. What once took hours to create can now be accomplished in seconds. While it might be tempting to limit AI use to these tasks—after all, exploring just these tools seems like more than enough—the opportunity to harness AI for enhancing teachers’ thinking processes cannot be overlooked. In fact, teams can use AI to lighten their collective load and improve the quality of the decisions they make to move students’ learning forward.
Professional learning communities (PLCs) offer valuable spaces for teachers and school leaders to collaborate, discussing instructional practices, innovating new ideas, developing content knowledge, and engaging in data-driven decision-making. PLCs allow educators to step away from the classroom and reflect alongside colleagues. When done right, they show significant benefits for both teachers and students (Serviss, 2022). And when enhanced by AI, PLC work can be even more productive.
AI as a Conversation Starter
As part of an ongoing project to understand overall patterns of teacher collaboration, we analyzed over 100 hours of video footage from grade-level meetings in schools across the U.S. These teams are using AI in various ways. For example, a group of 3rd grade teachers generated a first draft of their analysis of standards and sample success criteria for each lesson. Meanwhile, a high school history team used a chatbot to draft quiz questions, ranging from literal to inferential, that they could randomize and assign to students for practice in the learning management system.
As we have observed, when AI is used during meetings to assist in developing learning intentions and success criteria, unpacking standards, generating ideas for responding to student needs, or creating formative assessment options, the conversations teachers have are consistently positive, collaborative, and reflective. In the meetings where humans share the same types of ideas, there is less discussion and more hesitancy from other members to speak up if they don’t agree or if they feel they have better ideas.
In this ongoing analysis, it seems that team members would rather not disrupt the community by challenging ideas presented by their colleagues. When AI is used as a thought partner, the results are profoundly different. The end products are stronger because the team spends more time refining ideas, engaging with student data, and developing content based on the rich discussions had.
The opportunity to harness AI for enhancing thinking processes cannot be overlooked.
As educators have started to explore the many capabilities of artificial intelligence and its impact on teaching and learning, like in the instances above, it has become clear that it can be a beneficial and worthwhile thought partner during PLCs. Of course, while chatbots are capable of producing human-like responses that can enhance the level of discourse, they cannot replace the insights of experienced educators. That said, pairing human insights alongside AI-generated output has enhanced, and will continue to enhance, the productivity and depth of PLC conversations and outcomes.
Although there are various models for professional learning communities, they tend to focus on some essential questions that allow teams to engage in collaborative conversations. Here are several ways AI can be integrated into this work.
Where Are We Going?
Whether it’s analyzing standards or outlining general content to cover, planning for upcoming lessons or units is a familiar task for K–12 educators. As part of their meetings, teacher teams traditionally plan what will be taught and solidify a vision for where a unit or lesson will go. These discussions are generally focused on identifying the standards to teach, unpacking what the standards mean, and possibly looking up and down a grade level to get a sense of how the skill progresses.
AI can enhance these conversations by not only assisting in the analysis of the standard but also helping move the conversation along by giving initial answers to questions like these:
- What prior knowledge do students need according to this standard?
- Which skills are crucial for mastering this standard?
- What should students know and be able to do after mastering this standard?
- What would a progression of this skill look like?
- What academic vocabulary should be emphasized with this standard?
- What challenges might arise when teaching this standard?
- How can we best support students working toward this standard?
- How will we know when students understand this standard?
Although not all responses will be perfect or accurate, the insights generated through these questions can foster deeper conversations about the actual content to be covered. Engaging in this level of discussion will provide those involved in the learning community with a richer understanding of the learning objectives and a clearer vision of where to go next.
How Do We Move Learning Forward?
When teams discuss the instructional support they can provide to students, educators share and discuss how they will teach specific skills and concepts. During this stage, teachers start planning for how to respond to where students currently are in relation to where they are going and plan for research-based practices that will advance learning. This could mean looking to existing curriculum resources, revisiting past instructional strategies, or generating new ideas. While asking AI for suggestions is a starting point, not all new ideas lead to better outcomes. Being strategic in how we leverage this technology for this part of the process is important.
For example, if a group of teachers is discussing how to help students develop arguments, instead of the prompt, “Give me ideas to help teach students how to build an argument,” they might write, “I want students to engage in a formal classroom debate to help them understand what it means to build an argument. Help me think about how I can do this in my 6th grade classroom.” The second prompt references an evidence-based instructional strategy (classroom discussion) and seeks assistance in generating ideas for implementing that strategy for a specific grade level. Including in the prompt some teaching techniques that we know have a high impact on students’ learning is a way to lean on this technology to take our instructional methods and the how of our teaching to the next level.
When teams use AI in their meetings, the conversations teachers have are consistently positive, collaborative, and reflective.
Of course, the support AI can provide teachers in planning is not limited to the instances above. Below are additional ways that educators we collaborate with, and those whose team meetings have been analyzed, have successfully integrated AI throughout the PLC process:
Writing success criteria and learning intentions. Creating these statements can be tricky, even when there is solid understanding of the content being taught. Teaching the chatbot the structure for strong success criteria and learning intentions in the prompt and then working off generated content can be a great way to make these as clear and approachable as possible. One way to ensure that the generated content matches the structure of existing success criteria and learning intentions is to add an example to the prompt saying something such as: “Below is an example of how to structure success criteria and learning intentions. Follow the same structure and generate possibilities to be used when teaching students to ___ and ___.”
Creating proficiency scales. With some guidance from the user, AI can generate a solid starting point for proficiency scales, progressions, or rubrics—tools used to measure student progress. Users can input a standard and engage in a back-and-forth with an AI platform to define criteria for different levels of learning. Beyond just creating content for a proficiency scale, AI can format this content to meet specific user requirements with a simple prompt. For example, instructing the chatbot to organize the data into a table, include certain descriptors, or break down the content into sections are just some of the ways to efficiently achieve the desired end product.
Initial analysis of student data. Analyzing student data is a crucial part of the PLC process and should always be a primary consideration when planning a learning path forward. Using AI to sort data is one way it can assist. On many platforms, users can input a standard or even a rubric, along with some student responses, and ask the chatbot to sort based on one aspect of the standard or criteria from the rubric. AI can also analyze data using an open-ended prompt such as, “What is this student already doing well, and what should they do next?” While the response might not be exactly what a teacher would notice, the discussion around the AI’s suggestions can lead to a powerful, data-driven conversation.
Adjusting content with student data in mind. One of the simplest ways to begin integrating AI in PLC meetings, or in teaching in general, is to build off existing resources that have proven successful. Teachers can input these resources, examples, or assignments into the system and prompt the chatbot to make adjustments based on current student data and specific needs. For example, a useful prompt might be, “How should this assignment be adjusted based on data showing ___?” This process of using AI to adjust content allows for a focused exploration of potential changes, enhancing the educational impact.
Expanding Human Potential
These ideas represent just a few of the many ways AI can contribute to academic conversations, whether in formal PLCs, department meetings, professional learning sessions, or even informal gatherings of teachers planning for the weeks ahead. Integrating this technology into these aspects of teaching will allow for more focused discussions around refining, clarifying, and critically evaluating ideas. These are the discussions that will enhance the quality and impact of teacher collaboration.
In his book Co-Intelligence: Living and Working with AI, Ethan Mollick emphasizes the importance of always inviting AI to the table, suggesting that “AI can assist us as a thinking companion to improve our own decision-making” (2024). Using AI to expand human potential in generating thoughts and ideas does not mean relinquishing control to technology; instead, it’s about leveraging this tool to surpass our current capabilities.
When we invite AI to the table, we’re not stepping back. Instead, we are actively engaging with its insights and contributions, acknowledging that this interaction can lead to new ideas and powerful collaboration that will enhance our collective creativity and drive innovation.
Guidelines When ΑI Joins the Conversation
Just like there are norms that groups follow when engaging in collaborative meetings, there should be norms for inviting AI to the table. Keeping the following things in mind when using AI in PLC meetings will ensure the technology remains a productive support and assistant.
Stay in control of content: Keeping a human in the loop is essential. Teachers using AI are responsible for the content used and must be sure it aligns with classroom needs and grade-level standards.
Analyze output: Educators should always carefully check AI output for accuracy, bias, and missing information.
Avoid deep dives into unrelated content: Staying focused on the meeting topic and structure is important, especially when AI can tempt us to jump from idea to idea. Keeping discussions on track is crucial.
Limit the AI drivers: When AI joins an existing conversation, having one person in the driving seat can help maintain the focus of the group conversation and avoid confusion.
Be mindful of the developing technology: Generative AI tools are constantly evolving—what isn’t possible to do today might become an option tomorrow. Stay open to advancements as you explore and use these platforms.
Talk back to the technology: If you disagree with the output or have questions about what is generated, remember that one of the key features of generative AI is the ability to engage in a back-and-forth. This interaction can enhance understanding and refine outputs.
Reflect & Discuss
➛ Do you think using AI in team meetings could help depersonalize discussions? Would your team be more willing to challenge an idea generated by AI than by a colleague?
➛ What is one way you could integrate AI into your next PLC meeting?
