Why Problem-Finding Matters for Students in an AI-Powered Future

January 22, 2026

In many ways, traditional classroom instruction has focused on problem-solving, with students receiving neatly defined problems to solve either on their own or with one another. But as AI tools proliferate and students offload problem-solving to AI, students need to retain the ability to identify which problems are worth solving in the first place.

That’s one key finding of a recent literature review (subscription required) undertaken by WestEd in collaboration with and with funding from Google. Through this literature review, the research team sought to better understand which psychological skills will prepare students to solve meaningful problems using AI. They found that in a future when AI tools are a daily fixture in classrooms, a key to teaching students how to problem-solve with AI will be to teach them how to problem-find.

Drawing from the review, which was published in the proceedings of the 18th Annual International Conference of Education, Research and Innovation (ICERI), this blog post breaks down what education leaders need to know: what problem-finding is, why it matters for the future of learning, and what early research says about how to teach it in classrooms.

What Is Problem-Finding, and Why Does It Matter for Learning With AI?

Problem-finding is the capacity to identify problems to solve. It requires judgment about which problems matter, whether AI is an appropriate tool to address them, and how to frame the problems in AI prompts to elicit effective solutions. The review highlights problem-finding as a distinct and foundational skill that is supported by various competencies, such as creativity (generating original ideas), design thinking (matching needs to solutions), and metacognition (reflecting on one’s own thinking), among others.

According to the review, several international frameworks for AI literacy and future-ready skills emphasize problem-solving but rarely address the art and science of finding problems. In the classroom and workforce of tomorrow, it’s not enough to react to problems presented by others. Students must actively spot potential, hidden, and emergent problems and decide where AI can play a productive role in addressing them.

Teaching Problem-Finding: Classroom Strategies for the AI Era

Students must have the opportunity to explore open-ended situations and be creative to develop problem-finding skills. Teachers can prompt students to think critically about their individual needs and about the needs of their communities and broader society to identify problems they are interested in solving. For example, in an elementary social studies unit about local government, students could identify challenges they experience in their hometowns that relate to municipal services (e.g., traffic management, trash collection, park maintenance). Research provides guidelines for how to structure these activities to help them be more successful.

• Encourage Creativity to Support Problem-Finding. Teachers should explicitly direct and encourage students to be creative. Studies show that encouragement leads to novel ideas.
• Use Ill-Structured Problems to Build Inquiry Skills. Open-ended, ambiguous tasks requiring inquiry allow students to grapple with creative problem-finding.
• Give Students Time and Autonomy to Identify Meaningful Problems. Meaningful problem-finding, like real-world problem-solving, requires time to explore and iterate. Students should be given dedicated time and freedom to problem-find.
• Normalize Struggle and Ambiguity in Problem-Finding. Identifying problems isn’t an activity in which one can be correct or incorrect in the same way one can be when finding, say, the answer to a math program. To mirror real-world problem-finding, students should have to encounter ambiguity and complex thinking.
• Use Constraints to Scaffold Creative Thinking. Scaffolding helps students gain traction and prevents overwhelming them while encouraging their creativity.

How Problem-Finding Aligns With Major Education Standards

Major standards like Next Generation Science Standards, Common Core, and the K–12 Computer Science Framework hint at problem-finding through practices like inquiry, design, and computational thinking. However, these connections tend to be implied rather than explicit. To meet the needs of the AI era, educators and curriculum developers must deliberately elevate problem-finding, making it a distinct instructional goal with tailored teaching and assessment strategies.