Lademann, Julia ORCID: 0009-0009-3033-4021, Henze, Jannik ORCID: 0000-0003-0218-757X and Becker-Genschow, Sebastian ORCID: 0000-0002-2461-0992 (2025). Augmenting learning environments using AI custom chatbots: Effects on learning performance, cognitive load, and affective variables. Physical Review Physics Education Research, 21 (1). pp. 1-13. American Physical Society (APS). ISSN 2469-9896

[thumbnail of PhysRevPhysEducRes.21.010147.pdf] PDF
PhysRevPhysEducRes.21.010147.pdf
Bereitstellung unter der CC-Lizenz: Creative Commons Attribution.

Download (685kB)
Identification Number:10.1103/PhysRevPhysEducRes.21.010147

Abstract

[Artikel-Nr. 010147] This work explores the integration of artificial intelligence (AI) custom chatbots in educational settings, with a particular focus on their applicability in the context of mathematics and physics. In view of the increasing deployment of AI tools such as ChatGPT in educational contexts, the present study explores their potential in generating topic-related learning material. The study assesses the impact of learning with AI-generated explanations as Supplemental Material on the learning experiences and performance of sixth-grade students, with a particular focus on proportional relationships in mathematical and physical contexts. The randomized controlled study with N = 2 1 4 students compared supplementary learning material in the form of traditional textbook material with explanations previously generated by an AI custom chatbot. The results demonstrated that while the AI-generated materials had an indefinite impact on learning outcomes, they significantly enhanced positive-activating emotions, situational interest, and self-efficacy while reducing intrinsic and extrinsic cognitive load. These findings underscore the potential of AI to transform educational practices by fostering a superior learning experience. However, further research is required to clarify its impact on learning performance and long-term learning outcomes. The study highlights the importance of careful integration and customization of AI tools to maximize their benefits in physics education.

Item Type: Article
Creators:
Creators
Email
ORCID
ORCID Put Code
Lademann, Julia
UNSPECIFIED
UNSPECIFIED
Henze, Jannik
UNSPECIFIED
UNSPECIFIED
Becker-Genschow, Sebastian
UNSPECIFIED
UNSPECIFIED
URN: urn:nbn:de:hbz:38-798254
Identification Number: 10.1103/PhysRevPhysEducRes.21.010147
Journal or Publication Title: Physical Review Physics Education Research
Volume: 21
Number: 1
Page Range: pp. 1-13
Date: 7 May 2025
Publisher: American Physical Society (APS)
ISSN: 2469-9896
Language: English
Faculty: Faculty of Mathematics and Natural Sciences
Divisions: Faculty of Mathematics and Natural Sciences > Department of Mathematics and Science Education > Institute of Physics Education
Subjects: Education
Natural sciences and mathematics
Physics
['eprint_fieldname_oa_funders' not defined]: Publikationsfonds UzK
Refereed: Yes
URI: http://kups.ub.uni-koeln.de/id/eprint/79825

Downloads

Downloads per month over past year

Altmetric

Export

Actions (login required)

View Item View Item