The Advancement of Personalized Learning Potentially Accelerated by Generative AI

• URL: http://arxiv.org/abs/2412.00691v2
• Date: Wed, 26 Feb 2025 08:54:04 GMT
• Title: The Advancement of Personalized Learning Potentially Accelerated by Generative AI
• Authors: Yuang Wei, Yuan-Hao Jiang, Jiayi Liu, Changyong Qi, Linzhao Jia, Rui Jia,
• Abstract summary: The rapid development of Generative AI (GAI) has sparked revolutionary changes across various aspects of education.<n>This study investigates GAI's potential to enhance various facets of personalized learning through a thorough analysis of existing research.<n>We find that GAI demonstrates exceptional capabilities in providing adaptive learning experiences tailored to individual preferences and needs.
• Score: 2.0107124571925583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid development of Generative AI (GAI) has sparked revolutionary changes across various aspects of education. Personalized learning, a focal point and challenge in educational research, has also been influenced by the development of GAI. To explore GAI's extensive impact on personalized learning, this study investigates its potential to enhance various facets of personalized learning through a thorough analysis of existing research. The research comprehensively examines GAI's influence on personalized learning by analyzing its application across different methodologies and contexts, including learning strategies, paths, materials, environments, and specific analyses within the teaching and learning processes. Through this in-depth investigation, we find that GAI demonstrates exceptional capabilities in providing adaptive learning experiences tailored to individual preferences and needs. Utilizing different forms of GAI across various subjects yields superior learning outcomes. The article concludes by summarizing scenarios where GAI is applicable in educational processes and discussing strategies for leveraging GAI to enhance personalized learning, aiming to guide educators and learners in effectively utilizing GAI to achieve superior learning objectives.

Related papers

• GuideAI: A Real-time Personalized Learning Solution with Adaptive Interventions [0.5833117322405447]
  Large Language Models (LLMs) have emerged as powerful learning tools, but they lack awareness of learners' cognitive and physiological states.<n>We introduce GuideAI, a multi-modal framework that enhances LLM-driven learning by integrating real-time biosensory feedback.
  arXiv  Detail & Related papers  (2026-01-28T09:06:45Z)
• Enhancing Online Learning by Integrating Biosensors and Multimodal Learning Analytics for Detecting and Predicting Student Behavior: A Review [0.0]
  This systematic review explores the integration of biosensors and Multimodal Learning Analytics.<n>Our study highlights the growing role of physiological signals, such as heart rate, brain activity, and eye-tracking.<n>The review identifies current research trends, limitations, and emerging directions in the field.
  arXiv  Detail & Related papers  (2025-09-09T13:41:40Z)
• Adaptive Learning Systems: Personalized Curriculum Design Using LLM-Powered Analytics [14.157213827899342]
  Large language models (LLMs) are revolutionizing the field of education by enabling personalized learning experiences tailored to individual student needs.<n>This paper introduces a framework for Adaptive Learning Systems that leverages LLM-powered analytics for personalized curriculum design.
  arXiv  Detail & Related papers  (2025-07-25T04:36:17Z)
• A Comprehensive Review of AI-based Intelligent Tutoring Systems: Applications and Challenges [0.4369550829556578]
  We use a systematic literature review method to analyze numerous qualified studies published from 2010 to 2025.<n>The results reveal a complex landscape regarding the effectiveness of ITS, highlighting both advancements and persistent challenges.
  arXiv  Detail & Related papers  (2025-07-25T01:43:07Z)
• Unveiling the Learning Mind of Language Models: A Cognitive Framework and Empirical Study [50.065744358362345]
  Large language models (LLMs) have shown impressive capabilities across tasks such as mathematics, coding, and reasoning.<n>Yet their learning ability, which is crucial for adapting to dynamic environments and acquiring new knowledge, remains underexplored.
  arXiv  Detail & Related papers  (2025-06-16T13:24:50Z)
• LLM-powered Multi-agent Framework for Goal-oriented Learning in Intelligent Tutoring System [54.71619734800526]
  GenMentor is a multi-agent framework designed to deliver goal-oriented, personalized learning within ITS. It maps learners' goals to required skills using a fine-tuned LLM trained on a custom goal-to-skill dataset. GenMentor tailors learning content with an exploration-drafting-integration mechanism to align with individual learner needs.
  arXiv  Detail & Related papers  (2025-01-27T03:29:44Z)
• Educational Personalized Learning Path Planning with Large Language Models [0.0]
  This paper proposes a novel approach integrating Large Language Models (LLMs) with prompt engineering to address these challenges. By designing prompts that incorporate learner-specific information, our method guides LLMs like LLama-2-70B and GPT-4 to generate personalized, coherent, and pedagogically sound learning paths.
  arXiv  Detail & Related papers  (2024-07-16T14:32:56Z)
• Revolutionising Role-Playing Games with ChatGPT [0.0]
  The aim of the study was to analyse the impact of AI-based simulations on students' learning experience. Based on Vygotsky's sociocultural theory, ChatGPT was used to give students a deeper understanding of strategic decision-making processes.
  arXiv  Detail & Related papers  (2024-07-02T08:21:40Z)
• Large Language Models for Education: A Survey and Outlook [69.02214694865229]
  We systematically review the technological advancements in each perspective, organize related datasets and benchmarks, and identify the risks and challenges associated with deploying LLMs in education. Our survey aims to provide a comprehensive technological picture for educators, researchers, and policymakers to harness the power of LLMs to revolutionize educational practices and foster a more effective personalized learning environment.
  arXiv  Detail & Related papers  (2024-03-26T21:04:29Z)
• Tailoring Education with GenAI: A New Horizon in Lesson Planning [0.21427777919040414]
  This study introduces a GenAI tool, designed as a digital assistant for educators, enabling the creation of customized lesson plans. The tool utilizes an innovative feature termed 'interactive mega-prompt,' a comprehensive query system that allows educators to input detailed classroom specifics. To evaluate the tool's effectiveness, a comprehensive methodology incorporating both quantitative (i.e., % of time savings) and qualitative (i.e., user satisfaction) criteria was implemented.
  arXiv  Detail & Related papers  (2024-02-12T17:30:05Z)
• Bringing Generative AI to Adaptive Learning in Education [58.690250000579496]
  We shed light on the intersectional studies of generative AI and adaptive learning. We argue that this union will contribute significantly to the development of the next-stage learning format in education.
  arXiv  Detail & Related papers  (2024-02-02T23:54:51Z)
• Integrating AI and Learning Analytics for Data-Driven Pedagogical Decisions and Personalized Interventions in Education [0.2812395851874055]
  This research study explores the conceptualization, development, and deployment of an innovative learning analytics tool. By analyzing critical data points such as students' stress levels, curiosity, confusion, agitation, topic preferences, and study methods, the tool provides a comprehensive view of the learning environment. This research underscores AI's role in shaping personalized, data-driven education.
  arXiv  Detail & Related papers  (2023-12-15T06:00:26Z)
• Multimodality of AI for Education: Towards Artificial General Intelligence [14.121655991753483]
  multimodal artificial intelligence (AI) approaches are paving the way towards the realization of Artificial General Intelligence (AGI) in educational contexts. This research delves deeply into the key facets of AGI, including cognitive frameworks, advanced knowledge representation, adaptive learning mechanisms, and the integration of diverse multimodal data sources. The paper also discusses the implications of multimodal AI's role in education, offering insights into future directions and challenges in AGI development.
  arXiv  Detail & Related papers  (2023-12-10T23:32:55Z)
• Towards Goal-oriented Intelligent Tutoring Systems in Online Education [69.06930979754627]
  We propose a new task, named Goal-oriented Intelligent Tutoring Systems (GITS) GITS aims to enable the student's mastery of a designated concept by strategically planning a customized sequence of exercises and assessment. We propose a novel graph-based reinforcement learning framework, named Planning-Assessment-Interaction (PAI)
  arXiv  Detail & Related papers  (2023-12-03T12:37:16Z)
• Exploring the Cognitive Knowledge Structure of Large Language Models: An Educational Diagnostic Assessment Approach [50.125704610228254]
  Large Language Models (LLMs) have not only exhibited exceptional performance across various tasks, but also demonstrated sparks of intelligence. Recent studies have focused on assessing their capabilities on human exams and revealed their impressive competence in different domains. We conduct an evaluation using MoocRadar, a meticulously annotated human test dataset based on Bloom taxonomy.
  arXiv  Detail & Related papers  (2023-10-12T09:55:45Z)
• Personalized Education in the AI Era: What to Expect Next? [76.37000521334585]
  The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to meet her desired goal. In recent years, the boost of artificial intelligence (AI) and machine learning (ML) has unfolded novel perspectives to enhance personalized education.
  arXiv  Detail & Related papers  (2021-01-19T12:23:32Z)

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.