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.
Expert Commentary: Shaping the Future of Collaborative Learning with AI
In recent years, there has been growing interest in exploring the potential of Artificial Intelligence (AI) in education. This research study takes a unique approach by not only considering AI as a tool for learning but also as an intelligent agent that can actively engage in collaborative learning with humans. This multi-disciplinary concept combines elements of computer science, education, and cognitive psychology to revolutionize traditional classroom practices.
The researchers adopt a design and development research approach, following the Analysis, Design, Development, Implementation and Evaluation (ADDIE) model. This structured framework allows them to prototype a tangible instructional system called Collaborative Learning with AI Speakers (CLAIS), which aims to create small groups consisting of 3-4 human learners and an AI speaker as peers participating in the Jigsaw learning process.
Utilizing the NUGU AI speaker platform, the CLAIS system was successfully implemented in a Science Education course session with pre-service elementary science teachers. The participants’ evaluation of the CLAIS system was conducted through mixed method surveys, exploring their experiences as teachers, learners, peers, and users of this innovative collaborative learning environment.
The quantitative data obtained from the surveys revealed that the participants’ Intelligent-Technological, Pedagogical, And Content Knowledge (TPACK) significantly increased after the CLAIS session. This demonstrates the effectiveness of AI in enhancing participants’ understanding and mastery of both subject matter and technological skills. Furthermore, the perception of the CLAIS learning experience was largely positive, indicating that human learners were able to successfully collaborate with AI peers during the learning process.
However, it is important to note that the peer assessment on AI speakers and human peers differed. This raises intriguing questions about the dynamics of collaboration between humans and AI. As AI continues to evolve and become more integrated into our daily lives, it will be crucial to investigate the implications of AI-assisted collaboration on traditional assessment methods and social dynamics within learning environments.
The qualitative data gathered from the participants also shed light on their anticipation of future changes in the epistemic process in science classrooms. This suggests that the use of AI as a collaborative learning agent has the potential to transform how knowledge is co-constructed in authentic classroom settings. Nonetheless, technical issues such as speech recognition performance and response latency were acknowledged, underscoring the need for ongoing technological advancements to optimize the user experience.
This study serves as a strong example of the multi-disciplinary nature of AI in education. By combining elements of cognitive psychology, pedagogy, and technology, it demonstrates how AI can shape the future landscape of epistemic practices in the classroom. As the field of AI-assisted education continues to evolve, it is essential to consider the ethical implications, address technical challenges, and promote interdisciplinary collaboration to ensure the responsible integration of AI into educational contexts.
In conclusion, this research not only highlights the potential of Human-AI Collaboration for knowledge co-construction in authentic classroom settings but also exemplifies how AI could shape the future of collaborative learning. The findings of this study contribute to our understanding of the benefits and challenges associated with incorporating AI into educational practices, paving the way for further exploration and innovation in this exciting field.