My teammate is AI: understanding students’ perceptions of student-AI collaboration in drawing tasks

Kim, Jinhee; Cho, Young Hoan

doi:10.1080/02188791.2023.2286206

Cited by 50Artificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive LearningInnovative Teaching and Learning Methods

My teammate is AI: understanding students’ perceptions of student-AI collaboration in drawing tasks

Kim, Jinhee, Cho, Young Hoan·2025·Asia Pacific Journal of Education

DOI 10.1080/02188791.2023.2286206

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Abstract

Educators expect artificial intelligence (AI) to augment student capabilities and rapidly transform teaching and learning practices. However, little is known about students’ perceptions of AI and what they expect from AI in student-AI teaming. There is a lack of holistic understanding of the nature, effects, and areas of improvement in teamwork by students and AI on learning. This study aims to explore students’ perceptions and experiences about student-AI collaboration (SAC) to provide effective measures for the successful integration and implementation of SAC. In-depth interviews were carried out with 20 undergraduate students in South Korea after a drawing task with AutoDraw. This study found that students expected AI to serve various roles, including a learning mate, a tutor, and an effective tool to complete the task during SAC. Students perceived that SAC could benefit both the students’ affective domain and task performance. In contrast, students addressed multiple barriers to SAC both from the AI-related and student-related aspects. These findings can provide insights into the dynamic interaction between students and AI for problem-solving performance and meaningful learning. This study also provides implications on AI literacy education and the design of educational AI that can be made pedagogically rich to enhance student-AI teams.

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“Generally, understanding complicated AI abstracts and concepts is often challenging, particularly for learners from non- technical backgrounds (Lacerda Queiroz etal.2021; Kim et al. 2023).”
From Game‐Based Learning for Asynchronous AI Literacy Course: Approach to Improve Students' Cognitive, Behavioural, Affective, and Ethical Learning of AI (2026) · cites this paper as Kim et al. 2023

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Authors: Kim, J., & Cho, Y. H.
Affiliations: Old Dominion University (US)
Seoul National University (KR)
Corresponding author: Cho, Young Hoan
ORCID: Kim, Jinhee
Cho, Young Hoan
Venue: Asia Pacific Journal of Education
Publisher: Informa UK Limited
Published: 2023-11-29
Volume: 45
Issue: 3
Pages: 1013-1027
ISSN: 0129-4776 (ISSN-L)
0218-8791
1742-6855
DOI: 10.1080/02188791.2023.2286206
OpenAlex: W4389144132
Cited by: 50
References: 51
Citations by year: 2026: 22 · 2025: 21 · 2024: 7
FWCI: 11.74
Type: journal article
Language: en
Open access: closed
Locations: Asia Pacific Journal of Education (publishedVersion)
Topics: Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
Keywords: student perceptions of AI, drawing tasks, affective domain, task performance, AI adoption barriers, AI literacy, meaningful learning, problem-solving performance, undergraduate students

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