Collaborative Learning with Artificial Intelligence Speakers (CLAIS):
Pre-Service Elementary Science Teachers' Responses to the Prototype
- URL: http://arxiv.org/abs/2401.05400v1
- Date: Wed, 20 Dec 2023 01:19:03 GMT
- Title: Collaborative Learning with Artificial Intelligence Speakers (CLAIS):
Pre-Service Elementary Science Teachers' Responses to the Prototype
- Authors: Gyeong-Geon Lee, Seonyeong Mun, Myeong-Kyeong Shin, and Xiaoming Zhai
- Abstract summary: 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.
- Score: 0.5113447003407372
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This research aims to demonstrate that AI can function not only as a tool for
learning, but also as an intelligent agent with which humans can engage in
collaborative learning (CL) to change epistemic practices in science
classrooms. We adopted a design and development research approach, following
the Analysis, Design, Development, Implementation and Evaluation (ADDIE) model,
to prototype a tangible instructional system called Collaborative Learning with
AI Speakers (CLAIS). 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 development was carried
out using the NUGU AI speaker platform. The CLAIS system was successfully
implemented in a Science Education course session with 15 pre-service
elementary science teachers. The participants evaluated the CLAIS system
through mixed methods surveys as teachers, learners, peers, and users.
Quantitative data showed that the participants' Intelligent-Technological,
Pedagogical, And Content Knowledge was significantly increased after the CLAIS
session, the perception of the CLAIS learning experience was positive, the peer
assessment on AI speakers and human peers was different, and the user
experience was ambivalent. Qualitative data showed that the participants
anticipated future changes in the epistemic process in science classrooms,
while acknowledging technical issues such as speech recognition performance and
response latency. This study highlights the potential of Human-AI Collaboration
for knowledge co-construction in authentic classroom settings and exemplify how
AI could shape the future landscape of epistemic practices in the classroom.
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