Beyond Answers: How LLMs Can Pursue Strategic Thinking in Education

• URL: http://arxiv.org/abs/2504.04815v1
• Date: Mon, 07 Apr 2025 08:09:46 GMT
• Title: Beyond Answers: How LLMs Can Pursue Strategic Thinking in Education
• Authors: Eleonora Grassucci, Gualtiero Grassucci, Aurelio Uncini, Danilo Comminiello,
• Abstract summary: Large Language Models (LLMs) can act as both patient tutors and collaborative partners to enhance education delivery.<n>As tutors, LLMs personalize learning by offering step-by-step explanations and addressing individual needs.<n>As collaborators, they expand students' horizons, supporting them in tackling complex, real-world problems.
• Score: 9.836302410524842
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Artificial Intelligence (AI) holds transformative potential in education, enabling personalized learning, enhancing inclusivity, and encouraging creativity and curiosity. In this paper, we explore how Large Language Models (LLMs) can act as both patient tutors and collaborative partners to enhance education delivery. As tutors, LLMs personalize learning by offering step-by-step explanations and addressing individual needs, making education more inclusive for students with diverse backgrounds or abilities. As collaborators, they expand students' horizons, supporting them in tackling complex, real-world problems and co-creating innovative projects. However, to fully realize these benefits, LLMs must be leveraged not as tools for providing direct solutions but rather to guide students in developing resolving strategies and finding learning paths together. Therefore, a strong emphasis should be placed on educating students and teachers on the successful use of LLMs to ensure their effective integration into classrooms. Through practical examples and real-world case studies, this paper illustrates how LLMs can make education more inclusive and engaging while empowering students to reach their full potential.

Related papers

• LLM Agents for Education: Advances and Applications [49.3663528354802]
  Large Language Model (LLM) agents have demonstrated remarkable capabilities in automating tasks and driving innovation across diverse educational applications.<n>This survey aims to provide a comprehensive technological overview of LLM agents for education, fostering further research and collaboration to enhance their impact for the greater good of learners and educators alike.
  arXiv  Detail & Related papers  (2025-03-14T11:53:44Z)
• Position: LLMs Can be Good Tutors in Foreign Language Education [87.88557755407815]
  We argue that large language models (LLMs) have the potential to serve as effective tutors in foreign language education (FLE)<n> Specifically, LLMs can play three critical roles: (1) as data enhancers, improving the creation of learning materials or serving as student simulations; (2) as task predictors, serving as learner assessment or optimizing learning pathway; and (3) as agents, enabling personalized and inclusive education.
  arXiv  Detail & Related papers  (2025-02-08T06:48:49Z)
• Embracing AI in Education: Understanding the Surge in Large Language Model Use by Secondary Students [53.20318273452059]
  Large language models (LLMs) like OpenAI's ChatGPT have opened up new avenues in education.<n>Despite school restrictions, our survey of over 300 middle and high school students revealed that a remarkable 70% of students have utilized LLMs.<n>We propose a few ideas to address such issues, including subject-specific models, personalized learning, and AI classrooms.
  arXiv  Detail & Related papers  (2024-11-27T19:19:34Z)
• Towards the Pedagogical Steering of Large Language Models for Tutoring: A Case Study with Modeling Productive Failure [36.83786872708736]
  One-to-one tutoring is one of the most efficient methods of teaching. We develop StratL, an algorithm to optimize LLM prompts and steer it to follow a predefined multi-turn tutoring plan represented as a transition graph. As a case study, we create a prototype tutor for high school math following Productive Failure (PF), an advanced and effective learning design.
  arXiv  Detail & Related papers  (2024-10-03T16:15:41Z)
• Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs [49.18567856499736]
  We investigate whether large language models (LLMs) can be supportive of open-ended dialogue tutoring. We apply a range of knowledge tracing (KT) methods on the resulting labeled data to track student knowledge levels over an entire dialogue. We conduct experiments on two tutoring dialogue datasets, and show that a novel yet simple LLM-based method, LLMKT, significantly outperforms existing KT methods in predicting student response correctness in dialogues.
  arXiv  Detail & Related papers  (2024-09-24T22:31:39Z)
• I don't trust you (anymore)! -- The effect of students' LLM use on Lecturer-Student-Trust in Higher Education [0.0]
  Large Language Models (LLMs) in platforms like Open AI's ChatGPT, has led to their rapid adoption among university students.<n>This study addresses the research question: How does the use of LLMs by students impact Informational and Procedural Justice, influencing Team Trust and Expected Team Performance?<n>Our findings indicate that lecturers are less concerned about the fairness of LLM use per se but are more focused on the transparency of student utilization.
  arXiv  Detail & Related papers  (2024-06-21T05:35:57Z)
• Adapting Large Language Models for Education: Foundational Capabilities, Potentials, and Challenges [60.62904929065257]
  Large language models (LLMs) offer possibility for resolving this issue by comprehending individual requests. This paper reviews the recently emerged LLM research related to educational capabilities, including mathematics, writing, programming, reasoning, and knowledge-based question answering.
  arXiv  Detail & Related papers  (2023-12-27T14:37:32Z)
• Democratizing Reasoning Ability: Tailored Learning from Large Language Model [97.4921006089966]
  We propose a tailored learning approach to distill such reasoning ability to smaller LMs. We exploit the potential of LLM as a reasoning teacher by building an interactive multi-round learning paradigm. To exploit the reasoning potential of the smaller LM, we propose self-reflection learning to motivate the student to learn from self-made mistakes.
  arXiv  Detail & Related papers  (2023-10-20T07:50:10Z)
• Impact of Guidance and Interaction Strategies for LLM Use on Learner Performance and Perception [19.335003380399527]
  Large language models (LLMs) offer a promising avenue, with increasing research exploring their educational utility. Our work highlights the role that teachers can play in shaping LLM-supported learning environments.
  arXiv  Detail & Related papers  (2023-10-13T01:21:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.