The use of large language models (LLMs) can influence the critical thinking of university students in both positive and negative ways. On the one hand, LLMs can help students access diverse perspectives and information quickly, potentially fostering deeper analysis and expanding their understanding. On the other hand, over-reliance on AI tools may reduce students’ ability to critically evaluate sources, develop their own arguments, or engage in independent problem-solving. It’s essential to balance the use of LLMs with guidance on critical thinking skills to ensure students remain active, reflective learners.

The influence of Large Language Models (LLMs) on the critical thinking of university students is profound, complex, and paradoxical. LLMs act simultaneously as catalysts for intellectual growth and as potential inhibitors of deeper cognitive engagement. The impact largely depends on the manner of their use, the awareness of their limitations, and the academic culture surrounding their integration.

The use of large language models (LLMs) has a significant impact on the critical thinking abilities of university students. One of the key ways LLMs influence critical thinking is by serving as a resource for students to explore diverse perspectives on complex topics. When students engage with LLMs, they can access a wealth of information and viewpoints that may challenge their existing beliefs or assumptions. This exposure encourages them to analyze, evaluate, and synthesize various arguments, fostering a deeper understanding of the subject matter.

Additionally, LLMs can facilitate the development of critical questioning skills. As students interact with these models, they learn to formulate more precise queries to obtain relevant information. This process not only sharpens their ability to identify important issues but also promotes a habit of inquiry that is central to critical thinking. By evaluating the responses provided by LLMs, students are encouraged to discern credible information from less reliable sources, honing their analytical skills in the process.

Moreover, the use of LLMs can enhance collaborative learning experiences. Students can leverage these tools in group discussions or projects, where they can compare and contrast insights generated by the model with their own thoughts and those of their peers. This collaboration fosters dialogue and debate, prompting students to articulate their reasoning and defend their positions, which are vital components of critical thinking. Through such interactions, they learn to appreciate the complexity of issues and the necessity of considering multiple viewpoints before arriving at conclusions.

However, while LLMs can be powerful tools for enhancing critical thinking, it is essential for educators to guide their use. Without proper oversight, students may become overly reliant on these models, potentially stifling their independent thinking and creativity. Therefore, integrating LLMs into the curriculum should be accompanied by instruction on how to critically assess the information they provide. By balancing the use of LLMs with traditional critical thinking exercises, educators can ensure that university students develop robust analytical skills that will serve them well beyond their academic careers.

The pros and cons of the growing use of LLMs has its parallel in the use of calculators at certain education levels. Both can be great tools to enhance a user's understanding of and overall interaction with diverse material. These tools, however, if overly relied upon, can become an intellectual crutch which could lead to the degradation of the individual faculty of reason, critical thinking skills, and internal source assessment competence. As many ELA and other education professionals have pointed out, there is an over-arching need for strict guidance and oversight in the integration of new media technologies and there use in order to avoid an unintentional march toward academic complacency.

Gonzalo Briceño-Mayorga The use of large language models (LLMs) in higher education has a multifaceted influence on the critical thinking of university students. On one hand, LLMs can enhance critical thinking by serving as tools for exploration, debate, and the analysis of complex topics. When used effectively, they provide students with instant access to diverse perspectives, structured arguments, and synthesized information, which can stimulate curiosity and deeper inquiry. Students can use these models to test their hypotheses, generate counterarguments, and reflect on the logic of various positions, thus refining their own reasoning. However, there is also a concern that overreliance on LLMs may reduce the need for independent analysis and original thought if students use them merely to generate answers without engaging in reflective questioning. The convenience of LLMs might encourage surface-level learning if critical engagement is not emphasized by educators. Therefore, the influence of LLMs on critical thinking largely depends on how they are integrated into the learning process—whether they are used as a scaffold for deeper understanding or as a shortcut to bypass cognitive effort. For positive outcomes, it is essential that instructors design tasks that require students to critique, compare, and justify the outputs of LLMs rather than passively accept them, thereby ensuring that these tools become partners in learning rather than substitutes for critical thought.

LLM, or Large Language Model, is a type of artificial intelligence that focuses on understanding and generating text in natural language. And these models are trained with large amounts of textual data to learn linguistic patterns and relationships, allowing them to perform tasks such as translation, text production, answering questions and creating content. I understand that their use by students (for example ChatGPT) can stimulate the development of skills in written production. To this end, it is important to "train" students in its use, in a classroom context, so that they can perceive the tool's potential. We have to give them good prompts, to be a good starting point, or to improve a text. Therefore, I believe it can help students carry out tasks and projects related to academic and pedagogical activities, saving time. . The responsible use of these tools can contribute to the formation of more agile and prepared students in the production of texts in an academic manner. The tool is hallucinatory and has no critical capacity, but when students practice the tool in a classroom context, they can assess its strengths and weaknesses and, if they want to produce good content, they can test the information produced, confirm it, adjust it, etc. (highlights the importance of prompts).

Thank you to all contributors for these insightful perspectives. A common thread across responses is the dual-edged nature of LLMs: they provide vast opportunities for exploration and critical engagement but also pose risks of superficial learning if not used judiciously. It seems timely to consider a multicenter study assessing how structured pedagogical frameworks can integrate LLMs as active partners in learning, ensuring that critical thinking is enhanced rather than diminished. I am open to collaborating on a conceptual framework for such a study—please reach out if interested.

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The rise of Large Language Models (LLMs), such as ChatGPT and other generative AI tools, has transformed the academic landscape in higher education. These tools offer unprecedented access to information, writing assistance, and problem-solving support, making them valuable companions for university students. However, their influence on students’ critical thinking skills is double-edged: while LLMs have the potential to enhance analytical reasoning when used effectively, they may also pose risks of overreliance and intellectual passivity if not integrated thoughtfully into learning environments.

LLMs as Catalysts for Critical Thinking

Used wisely, LLMs can serve as interactive platforms for inquiry, helping students refine their questions, explore multiple perspectives, and synthesize complex information. For example, a student researching ethical dilemmas in medical science can use an LLM to explore diverse viewpoints, uncover philosophical frameworks, and generate critical questions for further exploration. When students evaluate the output, compare it to other sources, and make independent judgments, the process encourages higher-order thinking.

Moreover, LLMs can act as intellectual sparring partners. By engaging in dialogue with AI, students are prompted to articulate their thoughts, challenge assumptions, and clarify their reasoning. In this sense, LLMs may serve a similar function to Socratic questioning—pushing students to think deeper, justify their claims, and consider counterarguments.

Risks of Diminished Critical Engagement

However, LLMs also present a significant risk of cognitive complacency. If students rely on these tools to generate answers without questioning the reasoning behind them, critical thinking may be eroded rather than enhanced. LLMs produce fluent, convincing responses that may contain inaccuracies, bias, or shallow reasoning. Without the habit of verifying, evaluating, and reflecting on AI-generated content, students may accept information at face value—undermining the very essence of critical thinking.

Additionally, outsourcing tasks such as essay writing or problem solving to LLMs may discourage students from wrestling with difficult concepts on their own. Learning often involves grappling with uncertainty, making mistakes, and developing intellectual resilience. Overdependence on AI shortcuts can deprive students of these formative experiences and weaken their academic growth.

The Role of Educators and Institutions

To harness the benefits of LLMs while safeguarding critical thinking, educators must guide students in using these tools responsibly. This involves teaching digital literacy, source evaluation, and AI ethics. Assignments can be designed to encourage critical engagement with LLM outputs, such as asking students to critique or compare AI responses, identify gaps in reasoning, or revise generated content based on evidence.

Universities can also promote reflective practice, encouraging students to document their thought process, decision-making steps, and learning reflections when using AI tools. This helps students remain intellectually active rather than passive consumers of information.

Conclusion

The influence of LLMs on the critical thinking of university students is shaped not by the technology itself, but by how it is used. When treated as a tool for exploration, reflection, and dialogue, LLMs can strengthen critical thinking. When used uncritically or passively, they risk weakening it. As AI becomes increasingly integrated into academic life, the challenge for educators and students is to embed intentionality, reflection, and ethical awareness into its use—ensuring that LLMs serve as catalysts, not substitutes, for critical thought.