As students lean on LLMs, they tend to enter an isolated world filled with immediate answers, validation, and ease of “learning.” While this feels nice in the short term, research shows that this interaction with AI limits deep comprehension of material. It also limits memory of the material, cognitive engagement, and weakens critical thinking skills. Collaboration is essential because it fosters a holistic learning process and it is a key skill for the future of work.
Collaborative learning involves joint intellectual effort by students, or students and teachers together, and is very beneficial to student’s learning, especially in this AI Era. I love how Smith and MacGregor explain collaboration: “These acts of intellectual processing – of constructing meaning or creating something new – are crucial to learning.” Collaborative learning promotes an environment where students must actively explain their thought-process, requiring them to have a deeper understanding of the topic. Additionally, when students explain their own approach or make a reasonable argument against an approach, they are thinking metacognitively and developing critical thinking.
Dr. Tom Chatfield highlights another dimension of this process: when students feel their perspectives are respected and their struggles understood, engagement follows. The classroom becomes a space not just for individual achievement, but for shared sense-making.
Smith and MacGregor extend this further, arguing that collaboration is foundational not only to learning, but to civic life. Dialogue, deliberation, and the ability to build understanding across differences are essential skills both in classrooms and in society.
If collaboration is essential, the question becomes how to design for it in an AI-mediated environment. How to practically foster collaboration in the AI Era:
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Establish clear AI boundaries Clarity in a teacher’s AI policy is essential. When students understand that AI use is valid within defined structures, they are more likely to engage with it intentionally. Teachers should model—and invite students to model—their thought process, prompting strategies, and evaluation of AI outputs. This transparency encourages students to apply critical thinking during AI interactions rather than passively accept responses. If possible, create a shared space such as an “AI Findings” channel where students post insights, patterns, and challenges. This turns individual AI use into collective learning.
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Use AI as a thought partner to enrich student-first thinking The quality of collaboration is fundamentally influenced by the context and activity in which it is embedded. Collaborative learning often begins with problems, for which students must marshal pertinent facts and ideas. AI should be scaffolded into this process—not as a shortcut, but as a tool that extends thinking.AI should be scaffolded into this collaborative process, serving as a thought partner for students, in order to enrich conversation by providing additional insights and connections. Teachers should provide clear guidelines about when students should consult AI, and when discussion should be limited to their peers or class.
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Discuss strategic AI delegation Pioneers in cooperative learning, David and Roger Johnson at the University of Minnesota, Robert Slavin at Johns Hopkins University, and Elizabeth Cohen at Stanford, explain that cooperative learning benefits students social skill development just as much as academic. They explain how cooperative strategies often involve students assigning roles and delegating tasks, in order to “ensure the positive interdependence of group participants and to enable students to practice different teamwork skills.” In the AI Era, students must also be discerning in what they delegate to AI. Discussion around strategic decision making about when, why, and how to involve AI in work should be an essential component in cooperative learning projects.
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Foster reflection and debriefing After collaborative, AI-supported tasks, reflection is essential. Students can be asked:
- What did AI help you understand?
- When did AI fall short?
- How did you adapt your thinking or prompting?
- What did you learn about the math—not just the answer? Reflection consolidates learning and reinforces metacognitive habits.
Collaboration is not just a support structure; it is a design decision. Chatfield explains how collaboration is one of the three fundamental dimensions of learning. When students work in isolation, their thinking can remain hidden, unchallenged, and easily replaced. Collaborative environments spur deeper understanding and connection, diversify perspectives and approaches, and build engagement and confidence, as thinking becomes visible. This approach changes roles: students become the pioneers of their learning, using AI as a tool, while teachers are pushed to the sidelines in order to guide their students. Maintaining collaboration in AI-forward interactions ensures that students still engage in their thinking, an essential skill in the AI Era.
Appendix: Classroom Applications for the AI Era
Below are two practical AI-forward lesson plans that intentionally foster collaboration.
Multiple Solution Paths
Summary: Solve one problem in multiple ways using AI as a thought partner. Learning Goals:
- Mathematical flexibility, sense-making, and deeper conceptual understanding: As students explore and articulate multiple representations of the same idea (symbolic, graphical, numerical, contextual), they gain strategies to verify a solution is logical. This supports sense-making, retention, and conceptual understanding.
- Strategic mathematical thinking: Students will compare solution methods, analyze their strengths and limitations, and justify which approach is most useful in a given context.
- Productive AI collaboration: Students will engage with AI as a structured reasoning partner that prioritizes student thinking first, helps students understand the multiple approaches, and supports reflection. This ensures students extend and clarify their reasoning rather than delegate it.
Data-Driven Math
Summary: Students observe data, notice patterns, make modeling choices, and justify their reasoning.
Learning Goals:
- Develop Mathematical Sense-Making: Interpret real-world data to identify patterns, trends, and relationships before selecting or applying a mathematical model.
- Practice Mathematical Judgment: Choose which mathematical concepts describe the data and justify those choices in context.
- Guarded AI Collaboration: Engage a customGPT as a thought partner to surface questions, clarify thinking, and refine explanations. AI Role: Supports exploration, pattern identification, and explanation—not final answers