Problem Solving

Problem solving — the process of formulating, analyzing, and resolving novel or complex challenges — is a core 21st-century competency that AI tools both amplify and threaten. Across the knowledge base's articles, generative AI functions as a scaffold, a dialogic partner, and an answer engine, and its educational value hinges on whether learners remain the primary decision-makers or outsource their reasoning to the machine. This tension between efficiency and deeper cognitive and regulatory engagement defines current research on AI and problem solving.

Questions to Consider

• Think of a genuinely hard problem you solved well. What made the struggle productive, and what would have been lost if someone had simply handed you the answer?
• The page describes a 'cognitive debt': delegating reasoning to AI produces the right product at the cost of understanding. Have you ever got the right answer without really understanding it? What was the cost later?
• AI tools help students explore multiple perspectives and generate their own problems—but can also encourage 'metacognitive laziness.' How do you tell the difference between AI as a thinking partner and AI as a substitute for thinking?
• One experiment found reflective and hybrid feedback outperformed direct AI feedback on delayed, AI-free transfer. Why might feedback that makes you do more work lead to learning that lasts longer?
• How does teaching students to pose their own problems (rather than only solve given ones) support transfer and self-study? When has generating a question taught you more than answering one?
• If AI errors are treated as provocations to question and verify, a limitation becomes a learning opportunity. Have you ever learned more from an AI's mistake than from its right answer?

Introduction

Generative AI offers clear support for problem solving. LLM tools help students explore alternative solutions, incorporate interdisciplinary perspectives, and simulate authentic real-world scenarios — such as clinical and ethical dilemmas or prototype testing in STEM — while delivering scalable, timely feedback and shifting assessment toward scenario-based, competency-based evaluation. In design-based research, AI positioned as an "intelligent learning partner" within structured collaborative tasks produced substantial gains in problem-solving performance, with students increasingly using AI to generate multiple perspectives rather than seek single answers.

Yet the same tools can erode the very skill they claim to support. Research on human-AI collaborative problem solving shows a striking trade-off: the interaction profile that achieves the highest task performance also shows the lowest self-AI Regulation in Education, as students delegate reasoning to the AI and reap what the authors call a "cognitive debt" — obtaining the right product at the cost of the self-constructive process of understanding. Studies of collaborative learning similarly warn of "metacognitive laziness," where outsourcing cognitive effort undermines independent analysis and self-regulated learning.

Problem solving in AI education research

The knowledge base's articles approach problem solving through distinct but converging lenses. A PRISMA systematic review finds that LLMs often produce incomplete or incorrect responses, prompting students to question, verify, and improve information — turning model imperfections into validation-and-correction cycles that strengthen critical thinking and mental independence. Rather than treating problems as given, other work foregrounds problem posing: training students to generate their own physics problems supports transfer and self-study, and GenAI chatbots improved primary students' problem-posing quality and reduced cognitive load in inquiry-based learning.

Scaffolding and assessment are also central. Adaptive AI scaffolds derived from sequence-mining individual students' process patterns boost on-task performance in collaborative mathematics problem solving — though maximal scaffolding also increased scripting behavior. On the measurement side, LLMs can mirror human raters in detecting growth in computational thinking across large physics courses, while computational thinking is proposed as the explicit conceptual glue that makes educational robotics foster genuine problem solving rather than isolated technical exercises.

How AI both helps and hinders

The dual character of AI for problem solving is consistent across studies. On the helping side, chatbots outperform search engines for problem posing by improving question quality and integrating cognitive networks; adaptive scaffolds improve performance; and collaborative AI designs raise both critical thinking and problem-solving scores while students remain primary decision-makers. On the hindering side, over-delegation reduces regulatory engagement, direct feedback without reflection encourages passive uptake, and heavy reliance on AI to build consensus threatens interpersonal skill development and intrinsic motivation. Notably, a multisite randomized experiment found that reflective and hybrid feedback designs outperformed direct AI feedback on delayed, AI-free transfer — evidence that AI's educational value depends less on access than on preserving student agency, evaluative judgment, and ownership during revision. Design principles emerging from the literature include prioritizing dialogic tension over seamless efficiency, enabling proactive co-regulation, and keeping metacognitive scaffolding and "mind-in-the-loop" oversight.

Implications

For educators, the balance of evidence points to structured, process-oriented integration: prompt engineering training shifts students from unfocused to productive AI interaction, embedding AI within collaborative inquiry tasks yields durable gains, and deliberately using AI errors as provocations converts a limitation into a learning opportunity. For designers, the consistent finding that efficiency does not equal deep learning argues for AI that questions, challenges, and scaffolds rather than answers. For institutions, responsible integration requires pairing GenAI with explicit AI literacy training and assessment rubrics that reward reasoning and justification over correct products. The recurring theme across Cognitive Psychology-grounded work is that problem solving is learned through effortful engagement — and AI's role should be to preserve that effort, not erase it.

Connections to other concepts

Problem solving is the applied outcome of critical thinking and computational thinking, and is cultivated through problem-based learning and inquiry-based learning frameworks. It depends on Scaffolding that maintains cognitive demand, on self-regulation and metacognitive oversight to avoid cognitive offloading, and on collaborative structures in which human and AI work together. Cognitive psychology and the transfer of learning literature provide the theoretical grounding for why learner-generated problems and reflective feedback produce more durable problem-solving gains. The balance of worked examples versus practice for acquiring such skills is itself content-dependent: Rachatasumrit, Koedinger & Carvalho (2025) show generalizable problem-solving skills are best learned through example-integrated practice (alternating worked examples with problems), because worked examples supply the inductive information needed to acquire and generalize a correct rule, whereas pure practice risks strengthening spuriously correlated features rather than the general procedure.

Simulating Collaborative Problem Solving with LLM Agents

• Simulating collaborative problem solving with participant-specific Large Language Models (LLMs) agents. Fang (2026) trains individual LLM agents on real participants' dialogue to reproduce collaborative problem solving, and validates the simulations with Epistemic Network Analysis, showing simulated and real dialogues are statistically indistinguishable (ENA distance 0.17; permutation p = 0.65). This offers a scalable way to study and generate authentic collaborative problem-solving discourse — with implications for both research and the design of practice environments for this 21st-century skill.

Automated CPS Skill Coding

• Measuring collaborative problem solving (CPS) competence typically requires coding behavior from simulated-task process data into specific CPS skills. Context-aware prompting of pre-trained language models can automate this coding, modeling contextual dependencies and fusing cognitive and social abilities to achieve superior performance over strong baselines on CPS task datasets — addressing the labor-intensity of manual coding and enabling large-scale, real-time assessment.

Connected Concepts

• Critical Thinking
• Computational Thinking
• Collaborative Learning
• Problem-Based Learning
• Inquiry-Based Learning
• Creativity
• Scaffolding
• Self-Regulated Learning
• Cognitive Psychology

Connected Articles

• Unpacking Interaction Profiles and Strategies in Human-AI Collaborative Problem Solving: A Cognitive Distribution and Regulation Perspective — Interaction profiles (delegated reasoning vs concerted interpretation) and the efficiency–regulation trade-off
• Design-Based Research for Developing an AI-Assisted Collaborative Learning Model to Enhance Critical Thinking and Problem-Solving Skills in Higher Education — DBR model positioning generative AI as an intelligent learning partner
• Can Large Language Models Foster Critical Thinking, Teamwork, and Problem-Solving Skills in Higher Education?: A Literature Review — PRISMA review of LLMs fostering critical thinking, teamwork, and problem solving
• Towards an adaptive AI scaffold for developing student collaborative problem solving — Sequence-mined adaptive scaffolds for collaborative problem solving
• Exploring Students' Perceptions of Using Generative AI-Assisted Problem Posing — Problem posing as a self-regulated learning strategy with GenAI
• Human-centered GenAI feedback design in higher education: a multisite experiment on direct, reflective, and hybrid approaches to scientific argumentation — Reflective/hybrid feedback outperforms direct AI on delayed transfer
• Inquiry-Based Learning in STEM Education: The Impact of Generative AI-Based Chatbots on Primary School Students' Problem Posing Ability in Science — Chatbots improve primary students' problem posing in inquiry-based learning
• Using LLMs to Detect Growth in Computational Thinking in Introductory Physics — LLMs as scalable assessors of computational problem solving in physics
• Taming the Black Box: Design Principles for Rule-Integrated LLM Tutoring Systems in Primary School Mathematical Problem Solving — Rule-guided vs ad-hoc scaffolding in an LLM tutoring system for primary mathematics (Looi et al. 2026)
• Evidence and Theory for why the Best Example-Problem Ratio To Optimize Learning Gain Depends on Knowledge Content