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Do People Learn Better with Large Language Models? Examining the Double Edged Sword and a Roadmap for Future Research

  • Syeda W.F. Rizvi*
  • , Ala Yankouskaya
  • , Sameha Alshakhsi
  • , Dena Al-Thani
  • , Raian Ali
  • *Corresponding author for this work
  • Hamad bin Khalifa University
  • Bournemouth University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The rapid integration of Large Language Models (LLMs) into educational settings represents a transformative shift in how knowledge is accessed, constructed and mediated. This position paper examines the cognitive, pedagogical and ethical dimensions of learning with LLMs. Learners increasingly use LLMs to externalize cognitive tasks, potentially enhancing efficiency but raising concerns about critical thinking and reasoning. Emerging evidence suggests that LLMs can serve as cognitive partners and collaborative mediators, supporting distributed cognition, metacognitive scaffolding and interactive learning. The paper further discusses how learning styles and learning motivation shape usage of LLMs in educational context. Beyond cognitive and pedagogical implications, the paper also highlights ethical and societal consequences of using LLMs for learning. It concludes with a roadmap for future research, emphasizing the need to design these tools such that they benefit education and learning.

Original languageEnglish
Title of host publication2025 3rd International Conference on Foundation and Large Language Models, FLLM 2025
EditorsKai Erenli, Christian Guetl, Yaser Jararweh, Jim Jansen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages262-271
Number of pages10
ISBN (Electronic)9798331594091
DOIs
Publication statusPublished - 2025
Event2025 3rd International Conference on Foundation and Large Language Models, FLLM 2025 - Vienna, Austria
Duration: 25 Nov 202528 Nov 2025

Publication series

Name2025 3rd International Conference on Foundation and Large Language Models, FLLM 2025

Conference

Conference2025 3rd International Conference on Foundation and Large Language Models, FLLM 2025
Country/TerritoryAustria
CityVienna
Period25/11/2528/11/25

Keywords

  • Cognitive Offloading
  • Collaborative Learning
  • GenAI
  • Large Language Models
  • Learning
  • Learning Engagement
  • Learning Styles

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