Investigating the Impact of Personalized AI Tutors on Language Learning Performance

• URL: http://arxiv.org/abs/2505.02443v1
• Date: Mon, 05 May 2025 08:11:20 GMT
• Title: Investigating the Impact of Personalized AI Tutors on Language Learning Performance
• Authors: Simon Suh,
• Abstract summary: I will conduct a quasi experiment with paired sample t test on 34 students pre and post use of AI tutors in language learning platforms like Santa and Duolingo.<n>I will examine the relationship between students engagement, academic performance, and students satisfaction during a personalized language learning experience.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Driven by the global shift towards online learning prompted by the COVID 19 pandemic, Artificial Intelligence has emerged as a pivotal player in the field of education. Intelligent Tutoring Systems offer a new method of personalized teaching, replacing the limitations of traditional teaching methods. However, concerns arise about the ability of AI tutors to address skill development and engagement during the learning process. In this paper, I will conduct a quasi experiment with paired sample t test on 34 students pre and post use of AI tutors in language learning platforms like Santa and Duolingo to examine the relationship between students engagement, academic performance, and students satisfaction during a personalized language learning experience.

Related papers

• Exploring LLMs for Predicting Tutor Strategy and Student Outcomes in Dialogues [48.99818550820575]
  Recent studies have shown that strategies used by tutors can have significant effects on student outcomes.<n>Few works have studied predicting tutor strategy in dialogues.<n>We investigate the ability of modern LLMs, particularly Llama 3 and GPT-4o, to predict both future tutor moves and student outcomes in dialogues.
  arXiv  Detail & Related papers  (2025-07-09T14:47:35Z)
• INSIGHT: Bridging the Student-Teacher Gap in Times of Large Language Models [0.7499722271664147]
  INSIGHT is a proof of concept to combine various AI tools to assist teaching staff and students in the process of solving exercises.<n>We analyze students' questions to an LLM by extracting keywords, which we use to dynamically build an FAQ from students' questions.
  arXiv  Detail & Related papers  (2025-04-24T15:47:20Z)
• Inclusive Education with AI: Supporting Special Needs and Tackling Language Barriers [0.0]
  AI offers innovative tools to help educators create more inclusive learning environments.<n>It is discussed AI-driven language assistance tools that enable real-time translation and communication in multilingual classrooms.<n>It is explored assistive technologies powered by AI that personalize learning for students with disabilities.
  arXiv  Detail & Related papers  (2025-04-19T00:41:58Z)
• AI in Education: Rationale, Principles, and Instructional Implications [0.0]
  Generative AI, like ChatGPT, can create human-like content, prompting questions about its educational role.<n>The study emphasizes deliberate strategies to ensure AI complements, not replaces, genuine cognitive effort.
  arXiv  Detail & Related papers  (2024-12-02T14:08:07Z)
• YODA: Teacher-Student Progressive Learning for Language Models [82.0172215948963]
  This paper introduces YODA, a teacher-student progressive learning framework. It emulates the teacher-student education process to improve the efficacy of model fine-tuning. Experiments show that training LLaMA2 with data from YODA improves SFT with significant performance gain.
  arXiv  Detail & Related papers  (2024-01-28T14:32:15Z)
• Collaborative Learning with Artificial Intelligence Speakers (CLAIS): Pre-Service Elementary Science Teachers' Responses to the Prototype [0.5113447003407372]
  The CLAIS system is designed to have 3-4 human learners join an AI speaker to form a small group, where humans and AI are considered as peers participating in the Jigsaw learning process. The CLAIS system was successfully implemented in a Science Education course session with 15 pre-service elementary science teachers.
  arXiv  Detail & Related papers  (2023-12-20T01:19:03Z)
• Progressively Efficient Learning [58.6490456517954]
  We develop a novel learning framework named Communication-Efficient Interactive Learning (CEIL) CEIL leads to emergence of a human-like pattern where the learner and the teacher communicate efficiently by exchanging increasingly more abstract intentions. Agents trained with CEIL quickly master new tasks, outperforming non-hierarchical and hierarchical imitation learning by up to 50% and 20% in absolute success rate.
  arXiv  Detail & Related papers  (2023-10-13T07:52:04Z)
• Implementing Learning Principles with a Personal AI Tutor: A Case Study [2.94944680995069]
  This research demonstrates the ability of personal AI tutors to model human learning processes and effectively enhance academic performance. By integrating AI tutors into their programs, educators can offer students personalized learning experiences grounded in the principles of learning sciences.
  arXiv  Detail & Related papers  (2023-09-10T15:35:47Z)
• Opportunities and Challenges in Neural Dialog Tutoring [54.07241332881601]
  We rigorously analyze various generative language models on two dialog tutoring datasets for language learning. We find that although current approaches can model tutoring in constrained learning scenarios, they perform poorly in less constrained scenarios. Our human quality evaluation shows that both models and ground-truth annotations exhibit low performance in terms of equitable tutoring.
  arXiv  Detail & Related papers  (2023-01-24T11:00:17Z)
• Iterative Teacher-Aware Learning [136.05341445369265]
  In human pedagogy, teachers and students can interact adaptively to maximize communication efficiency. We propose a gradient optimization based teacher-aware learner who can incorporate teacher's cooperative intention into the likelihood function.
  arXiv  Detail & Related papers  (2021-10-01T00:27:47Z)
• Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience [76.9910678786031]
  We propose a novel paradigm of explainable active learning (XAL), by introducing techniques from the recently surging field of explainable AI (XAI) into an Active Learning setting. Our study shows benefits of AI explanation as interfaces for machine teaching--supporting trust calibration and enabling rich forms of teaching feedback, and potential drawbacks--anchoring effect with the model judgment and cognitive workload.
  arXiv  Detail & Related papers  (2020-01-24T22:52:18Z)

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.