Can Large Language Models Foster Critical Thinking, Teamwork, and Problem-Solving Skills in Higher Education?: A Literature Review

Abstract

Over the last two years, with the rapid development of artificial intelligence, Large Language Models (LLMs) have obtained significant attention from the academic sector, making their application in higher education attractive for students, managers, faculty, and stakeholders. We conducted a Systematic Literature Review on the adoption of LLMs in the higher education system to address persistent issues and promote critical thinking, teamwork, and problem-solving skills. Following the PRISMA 2020 protocol, a systematic search was conducted in the Web of Science Core Collection for studies published between 2023 and 2024. After a systematic search and filtering of 203 studies, we included 22 articles for further analysis. The findings show that LLMs can transform traditional teaching through active learning, align curricula with real-world demands, provide personalized feedback in large classes, and enhance assessment practices focused on applied problem-solving. Their effects are transversal, influencing multiple dimensions of higher education systems. Consequently, LLMs have the potential to improve educational equity, strengthen workforce readiness, and foster innovation across disciplines and institutions. This systematic review is registered in PROSPERO (2025 CRD420251165731).

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