AI-based Learning Assistants: Enhancing Math Learning for Migrant Students in German Schools

被引:0
|
作者
Kretzschmar, Vivian [1 ]
Seitz, Jurgen [1 ]
机构
[1] Stuttgart Media Univ, Inst Appl Artificial Intelligence IAAI, Stuttgart, Germany
关键词
AI; AI learning assistant; AI education; video-based learning; self-learning; migration; migrant students; TUTORIALS;
D O I
10.1109/CAI59869.2024.00035
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Increasing migration leads the education landscape to face the challenge of integrating children into the school system, accounting for inherent disadvantages. On average, as nearly half of them face socio-economic disadvantages, a persistent performance disparity was observed compared to domestic students. Remarkably, even after mitigating the influence of socioeconomic status, immigrant students' scores remained inferior. From March to May 2023, the AI Education (AIEDN) research project investigated how an AI-based learning assistant can compensate for existing impediments by enabling a better understanding through video-based learning. For this study, 275 students were selected from two secondary schools (N=137) and two grammar schools (N=138) in Baden-Wurttemberg, Germany. The experiment tested the extent to which learners solve more tasks, build broader (transfer) knowledge, and retain it. Students were repeatedly tested on mathematical problems, where the control group did not have access to the AI assistant. In contrast, the other group used the AI assistant to solve the problems. The students' performances from both school groups were statistically tested using t-tests. Within the test group of grammar schools, a significant change in the results between the two test days existed (T(41)=-2,28; p<.01) when the AI tool was used. In contrast, the control group showed no significant change T(37)=-.42; p>.05; d=.07) in performance. However, the results were insignificant for the secondary school students, as the given tasks and video content were considered too demanding. Further research is needed to determine AIEDN's performance for other target groups.
引用
收藏
页码:144 / 149
页数:6
相关论文
共 50 条
  • [31] A Comparative Study on AI-Based Learning Behaviors: Evidence from Vietnam
    Duong, Nam Tien
    Thi, Thuy Dung Pham
    Pham, Van Kien
    INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION, 2024,
  • [32] A reinforcement learning model for AI-based decision support in skin cancer
    Barata, Catarina
    Rotemberg, Veronica
    Codella, Noel C. F.
    Tschandl, Philipp
    Rinner, Christoph
    Akay, Bengu Nisa
    Apalla, Zoe
    Argenziano, Giuseppe
    Halpern, Allan
    Lallas, Aimilios
    Longo, Caterina
    Malvehy, Josep
    Puig, Susana
    Rosendahl, Cliff
    Soyer, H. Peter
    Zalaudek, Iris
    Kittler, Harald
    NATURE MEDICINE, 2023, 29 (08) : 1941 - +
  • [33] Twitter users perceptions of AI-based e-learning technologies
    Stracqualursi, Luisa
    Agati, Patrizia
    SCIENTIFIC REPORTS, 2024, 14 (01):
  • [34] Hierarchical Pathfinding and AI-Based Learning Approach in Strategy Game Design
    Duc, Le Minh
    Sidhu, Amandeep Singh
    Chaudhari, Narendra S.
    INTERNATIONAL JOURNAL OF COMPUTER GAMES TECHNOLOGY, 2008, 2008
  • [35] AI-Based Sensor Information Fusion for Supporting Deep Supervised Learning
    Leung, Carson K.
    Braun, Peter
    Cuzzocrea, Alfredo
    SENSORS, 2019, 19 (06)
  • [36] Incentive Mechanism for AI-Based Mobile Applications with Coded Federated Learning
    Saputra, Yuris Mulya
    Nguyen, Diep N.
    Dinh Thai Hoang
    Dutkiewicz, Eryk
    2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2021,
  • [37] Verification of the Factor Structure and Validation of the" Questionnaire on Parents' and Teachers' Social Acceptance of AI-Based Learning Systems in Schools"(SAELKIS)
    Mesenhoeller, Janne
    Boehme, Katrin
    DIAGNOSTICA, 2024, 70 (04): : 162 - 173
  • [38] A reinforcement learning model for AI-based decision support in skin cancer
    Catarina Barata
    Veronica Rotemberg
    Noel C. F. Codella
    Philipp Tschandl
    Christoph Rinner
    Bengu Nisa Akay
    Zoe Apalla
    Giuseppe Argenziano
    Allan Halpern
    Aimilios Lallas
    Caterina Longo
    Josep Malvehy
    Susana Puig
    Cliff Rosendahl
    H. Peter Soyer
    Iris Zalaudek
    Harald Kittler
    Nature Medicine, 2023, 29 : 1941 - 1946
  • [39] The Item Response Theory Model for an AI-based Adaptive Learning System
    Cui, Wei
    Xue, Zhen
    Shen, Jun
    Sun, Geng
    Li, Jianxin
    2019 18TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY BASED HIGHER EDUCATION AND TRAINING (ITHET 2019), 2019,
  • [40] Teaching Tool for Fun Learning of AI-based Banknote Detection Technology
    Yeh, Cheng-Yu
    Lin, Chun-Cheng
    Hsu, Kuan-Chun
    SENSORS AND MATERIALS, 2021, 33 (06) : 1767 - 1776