Synthesis: Can Large Language Models Foster Critical Thinking, Teamwork, and Problem-Solving Skills in Higher Education?: A Literature Review
Key Findings
Systematic literature review following the PRISMA 2020 protocol, searching the Web of Science Core Collection for studies published 2023–2024; of 203 studies screened, 22 articles were included (10 from search string A, 12 from search string B), and the review is registered in PROSPERO (CRD420251165731).LLMs often produce incomplete or incorrect responses, prompting students to question, verify, and improve the information — creating validation-and-correction cycles that the reviewed studies link to enhanced critical thinking and mental independence.LLMs act as catalysts for collaboration: supporting idea generation, organization, and peer feedback, simulating rubric-based evaluations and expert reviews, and democratizing access to knowledge — promoting more equitable collaborative learning, especially in diverse and large-class settings.For problem-solving, LLMs help students explore alternative solutions, incorporate interdisciplinary perspectives, and simulate authentic real-world scenarios — e.g., clinical and ethical dilemmas in health sciences, and prototype testing, debugging, and refinement in STEM.LLMs deliver scalable, timely, personalized feedback in large courses (near-instant responses reduce delay and dependence on instructor availability) and can reduce faculty workload by automating feedback on routine tasks such as drafts, quizzes, and structured assignments.LLMs support assessment reform: generating rubrics and varied assessment items aligned with learning outcomes, and shifting assessment from factual recall toward scenario-based, competency-based evaluation.About the Review
The review addressed four research questions (RQ1–RQ4) on how LLMs can foster critical thinking, collaborative skills, and problem-solving; bridge theory and practice; deliver personalized feedback in large courses; and support assessment development. Search criteria limited records to peer-reviewed English-language journal articles published between January 2023 and December 2024 (IC1–IC4), with exclusions for duplicates, non-journal document types, non-English language, and early access (EC1–EC4). Of 165 records entering the inclusion/exclusion stage, 117 were excluded and 48 papers were retrieved for eligibility assessment against five criteria (C1–C5): explicit teaching–learning focus, direct use of LLMs by students, pedagogical or subject-specific application in higher education, practical or empirical evidence of learning outcomes, and structured data or evaluation. The 48 eligible documents spanned 35 countries — led by the United States (n = 9), Germany (n = 8), Australia (n = 6), and England (n = 6) — with Frontiers Media SA and MDPI each publishing seven of the analyzed documents. Methodological rigor was enforced through the PRISMA 2020 protocol, the structured eligibility criteria, and PROSPERO registration.
Main Findings
Critical thinking: students learn to evaluate the accuracy, consistency, and trustworthiness of LLM-generated content; effectively framing prompts improves their understanding and analytical skills, engaging them in comparison, synthesis, and assessment.Collaboration: LLMs enrich teamwork by aiding idea generation and peer feedback, simulating expert or peer roles from various fields, and lowering participation barriers in large and diverse classes.Theory–practice gap: simulations, hands-on project work, and instant alternative solutions on error accelerate learning cycles and support self-management and independent learning via anytime/anywhere access.Feedback at scale: LLM-supported feedback extends beyond one-way correction to become a social, iterative process, and enables students to generate practice tests and self-assessments that strengthen metacognitive skills.Assessment: scenario-based and problem-focused evaluation lets students demonstrate applied skills in realistic settings (engineering, health care, business), shifting the assessment culture from knowledge reproduction to applied understanding.Systemic implications: the authors argue LLMs can transform curricula, feedback, and assessment at institutional and policy levels — improving educational equity, workforce readiness, and innovation — provided institutions establish ethical, equitable, and sustainable adoption frameworks.Implications for AI in Education
The review positions LLMs not merely as instructional tools but as catalysts for systemic improvement in Higher Ed: embedding LLMs in curricula and assessment can move teaching beyond memorization toward reasoning, hands-on learning, and competency-based evaluation. For instructors, the evidence supports using LLM imperfections deliberately — having students verify, critique, and refine AI outputs turns the technology's limitations into Critical Thinking exercises. For institutions, the review calls for policies that incorporate Generative AI literacy into curricula, prepare faculty for responsible use, and protect academic integrity. The findings on personalized, scalable feedback connect to Assessment and feedback research in large-enrollment courses, while the collaborative and problem-solving benefits align with Collaborative Learning frameworks. The authors caution that impact depends on faculty, managers, and policymakers adopting the tools within broader educational reform.
Limitations
The authors explicitly list four limitations: (a) the search relied on the Web of Science Core Collection without considering other databases; (b) inclusion was restricted to scientific journal publications, excluding other document types; (c) the included documents did not focus on challenges of LLM adoption in teaching and learning, such as ethical concerns or privacy; and (d) the review covered only 2023 and 2024, the first two years of the technology's emergence. No formal risk-of-bias or quality-assessment instrument was applied to the included studies beyond the PRISMA 2020 protocol and the structured eligibility criteria.
Connected Concepts
Higher EdCollaborative LearningCritical ThinkingAssessmentHuman In The Loop AIFormative AssessmentAutomated Essay ScoringPlagiarism DetectionConnected Articles
AI Assisted Collaborative Learning Model Dbr — Design-Based Research for Developing an AI-Assisted Collaborative Learning Model to Enhance Critical Thinking and Problem-Solving Skills in Higher EducationAI Collaborative Learning Skills Impacts — Artificial Intelligence and Collaborative Learning: Impacts on Creativity, Critical Thinking, and Problem-SolvingAI Collaborative Learning Systematic Review — A systematic review of AI-powered collaborative learning in higher education: Trends and outcomes from the last decadeGenAI Higher Education Systematic Review 2026 — Generative AI in Higher Education: A Systematic Review of Opportunities, Challenges, and Pedagogical Innovations (2022–2025)Polished Artifacts Fragile Engagement 2026 — Polished Artifacts, Fragile Engagement? Tackling the Challenge of Reduced Epistemic Effort in Human-AI Knowledge ConstructionTeaching Intro AI Course Redesign Bill Of Rights 2026 — Teaching Intro AI When the Tools Can Do the Homework: A Course Redesign and a Student Bill of RightsCitation
Martínez-Peláez, R., Mena, L. J., Toral-Cruz, H., Ochoa-Brust, A., González Potes, A., Flores, V., Ostos, R., Ramírez Pacheco, J. C., Félix, R. A., & Félix, V. G. (2025). Can large language models foster critical thinking, teamwork, and problem-solving skills in higher education? A literature review.