Human-Generative AI Collaborative Problem Solving Who Leads and How Students Perceive the Interactions
- URL: http://arxiv.org/abs/2405.13048v1
- Date: Sun, 19 May 2024 03:29:16 GMT
- Title: Human-Generative AI Collaborative Problem Solving Who Leads and How Students Perceive the Interactions
- Authors: Gaoxia Zhu, Vidya Sudarshan, Jason Fok Kow, Yew Soon Ong,
- Abstract summary: This research investigates human-generative AI collaboration types and students' interaction experiences when collaborating with generative AI (i.e., ChatGPT)
By analyzing the surveys and reflections of 79 undergraduate students, we identified three human-generative AI collaboration types: even contribution, human leads, and AI leads.
- Score: 22.467560842561852
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This research investigates distinct human-generative AI collaboration types and students' interaction experiences when collaborating with generative AI (i.e., ChatGPT) for problem-solving tasks and how these factors relate to students' sense of agency and perceived collaborative problem solving. By analyzing the surveys and reflections of 79 undergraduate students, we identified three human-generative AI collaboration types: even contribution, human leads, and AI leads. Notably, our study shows that 77.21% of students perceived they led or had even contributed to collaborative problem-solving when collaborating with ChatGPT. On the other hand, 15.19% of the human participants indicated that the collaborations were led by ChatGPT, indicating a potential tendency for students to rely on ChatGPT. Furthermore, 67.09% of students perceived their interaction experiences with ChatGPT to be positive or mixed. We also found a positive correlation between positive interaction experience and a sense of positive agency. The results of this study contribute to our understanding of the collaboration between students and generative AI and highlight the need to study further why some students let ChatGPT lead collaborative problem-solving and how to enhance their interaction experience through curriculum and technology design.
Related papers
- Visual-Geometric Collaborative Guidance for Affordance Learning [63.038406948791454]
We propose a visual-geometric collaborative guided affordance learning network that incorporates visual and geometric cues.
Our method outperforms the representative models regarding objective metrics and visual quality.
arXiv Detail & Related papers (2024-10-15T07:35:51Z) - Mutual Theory of Mind in Human-AI Collaboration: An Empirical Study with LLM-driven AI Agents in a Real-time Shared Workspace Task [56.92961847155029]
Theory of Mind (ToM) significantly impacts human collaboration and communication as a crucial capability to understand others.
Mutual Theory of Mind (MToM) arises when AI agents with ToM capability collaborate with humans.
We find that the agent's ToM capability does not significantly impact team performance but enhances human understanding of the agent.
arXiv Detail & Related papers (2024-09-13T13:19:48Z) - The Future of Learning: Large Language Models through the Lens of Students [20.64319102112755]
Students grapple with the dilemma of utilizing ChatGPT's efficiency for learning and information seeking.
Students perceive ChatGPT as being more "human-like" compared to traditional AI.
arXiv Detail & Related papers (2024-07-17T16:40:37Z) - Exploring the Impact of ChatGPT on Student Interactions in
Computer-Supported Collaborative Learning [1.5961625979922607]
This paper takes an initial step in exploring the applicability of ChatGPT in a computer-supported collaborative learning environment.
Using statistical analysis, we validate the shifts in student interactions during an asynchronous group brainstorming session by introducing ChatGPT as an instantaneous question-answering agent.
arXiv Detail & Related papers (2024-03-11T18:18:18Z) - Understanding Entrainment in Human Groups: Optimising Human-Robot
Collaboration from Lessons Learned during Human-Human Collaboration [7.670608800568494]
Successful entrainment during collaboration positively affects trust, willingness to collaborate, and likeability towards collaborators.
This paper contributes to the Human-Computer/Robot Interaction (HCI/HRI) using a human-centred approach to identify characteristics of entrainment during pair- and group-based collaboration.
arXiv Detail & Related papers (2024-02-23T16:42:17Z) - Covering Uncommon Ground: Gap-Focused Question Generation for Answer
Assessment [75.59538732476346]
We focus on the problem of generating such gap-focused questions (GFQs) automatically.
We define the task, highlight key desired aspects of a good GFQ, and propose a model that satisfies these.
arXiv Detail & Related papers (2023-07-06T22:21:42Z) - Embrace Opportunities and Face Challenges: Using ChatGPT in
Undergraduate Students' Collaborative Interdisciplinary Learning [0.6534705345202518]
ChatGPT has gained widespread attention from students and educators globally, with an online report by Hu (2023) stating it as the fastest-growing consumer application in history.
While discussions on the use of ChatGPT in higher education are abundant, empirical studies on its impact on collaborative interdisciplinary learning are rare.
We conducted a quasi-experimental study with 130 undergraduate students (STEM and non-STEM) learning digital literacy with or without ChatGPT over two weeks.
arXiv Detail & Related papers (2023-05-23T13:14:49Z) - IGLU 2022: Interactive Grounded Language Understanding in a
Collaborative Environment at NeurIPS 2022 [63.07251290802841]
We propose IGLU: Interactive Grounded Language Understanding in a Collaborative Environment.
The primary goal of the competition is to approach the problem of how to develop interactive embodied agents.
This research challenge is naturally related, but not limited, to two fields of study that are highly relevant to the NeurIPS community.
arXiv Detail & Related papers (2022-05-27T06:12:48Z) - NeurIPS 2021 Competition IGLU: Interactive Grounded Language
Understanding in a Collaborative Environment [71.11505407453072]
We propose IGLU: Interactive Grounded Language Understanding in a Collaborative Environment.
The primary goal of the competition is to approach the problem of how to build interactive agents that learn to solve a task while provided with grounded natural language instructions in a collaborative environment.
This research challenge is naturally related, but not limited, to two fields of study that are highly relevant to the NeurIPS community: Natural Language Understanding and Generation (NLU/G) and Reinforcement Learning (RL)
arXiv Detail & Related papers (2021-10-13T07:13:44Z) - AI-Driven Interface Design for Intelligent Tutoring System Improves
Student Engagement [2.083729551844793]
We explore AI-driven design for the interface of Intelligent Tutoring System (ITS) describing diagnostic feedback for students' problem-solving process.
We propose several interface designs powered by different AI components and empirically evaluate their impacts on student engagement through Santa.
Controlled A/B tests conducted on more than 20K students in the wild show that AI-driven interface design improves the factors of engagement by up to 25.13%.
arXiv Detail & Related papers (2020-09-18T10:32:01Z) - Joint Mind Modeling for Explanation Generation in Complex Human-Robot
Collaborative Tasks [83.37025218216888]
We propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations.
The robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications.
Results show that the generated explanations of our approach significantly improves the collaboration performance and user perception of the robot.
arXiv Detail & Related papers (2020-07-24T23:35:03Z)
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.