Journal Articles

Multimodal Learning Analytics

Ham, Y. H., Cho, Y. H., Lee, H., & Kim, H. (2021). Systematic Literature Review to Explore Research Trends and Future Directions of Multimodal Learning Analytics. The Journal of Educational Information and Media, 27(2), 501-529. http://dx.doi.org/10.15833/KAFEIAM.27.2.501
Ham, Y. H., Cho, Y. H., Kim, H. G., Lee, J., Kim, H., & Lee, H. (2021). Influence of metacognitive support on predicting learning outcomes in video lectures: Using multimodal learning analytics. The Journal of Educational Information and Media, 27(4), 1295-1323. http://dx.doi.org/10.15833/KAFEIAM.27.4.1295
Kim, H., Cho, Y. H., Ham, E. (Under Review). Developing and validating the Human-AI Collaboration Competency Scale. Educational Technology Research and Development
S. Kim, J. Y. Park, J. Lee, J. H. Chae, S. Lee, D. H. Ann, H. Kim, B. Shin, S. Park, Y. Jo, Y. H. Cho, H. Y. Moon. (Under review). Visuomotor Coordination Linking Dopaminergic Genetic Variation to Cognitive and Emotional Functioning in Early Adolescence within a Digital Context
Kim, H., Cho, Y. H., Kang, M., & Han, J. Y. (In preparation). When Metacognitive Supports Distract: Behavioral Dispersion in Digital Writing Processes Revealed by Epistemic Network Analysis
Kim, H., Cho, Y. H., Anna Kim, Somi Kang, Yeonwoo Jo, Gyumin Lee,..... (In preparation). Exploring the Digital Landscape: A Multivariate Analysis of Digital Backgrounds and Holistic Development in Early Adolescents.
Kim, H. & Choo, Y. (In preparation). Navigating a Data Science Convergence Major: Education Majors’ Learning Trajectories and Support Needs

Human-AI Interaction

Cho, S. K., Cho, Y. H., Kim, H., & Kim, H. (2022). The influence of elementary school students’ anthropomorphism of AI on the attitude and the career hope toward AI. Journal of Learner-Centered Curriculum and Instruction, 22(17), 165-181. https://doi.org/10.22251/jlcci.2022.22.17.165
Kim, H., Cho, Y. H., & Park, S. (2023). Exploring the Interaction Patterns between Learners and AI Translator in English Writing. The Journal of Educational Information and Media, 29(1), 201-228. http://dx.doi.org/10.15833/KAFEIAM.29.1.201
Cho, Y. H., Xie, Y., & Kim, H. (2024). Exploring factors influencing interaction between learners and AI. The Journal of Educational Information and Media, 30(6), 1819-1845. http://dx.doi.org/10.15833/KAFEIAM.30.6.1819
Kim, H., Ham, E. H., & Cho, Y. H. (Under review). Developing and validating the human-AI collaboration competency scale. Educational Technology Research and Development
Kim, H., Cho, Y. H., Kang, M., & Son, Y. (Under review). Design-Based Research to Enhance Collaboration between Learners and AI in a Middle School Writing Class. Learning: Research and Practice
Koh, E., Cho, Y. H., Lee, J. C., Kim, H. (Under review). Collaborative learning between humans and AI From Human-to-Human to Human-to-AI: Rethinking Collaborative Learning with AI through Distributed Cognition, Agency, Regulation, and Trust (DART). Learning: Research and Practice

Conference Presentations

Multimodal Learning Analytics

Metacognitive Strategy Analysis Using Log Data from Digital Activities

As digital education expands, there is a growing need to automatically analyze learners’ log data and use the results for personalized instruction. This study explored a systematic approach to analyzing learners’ metacognitive strategies based on log data collected during digital writing activities. Using log data gathered from 111 sixth-grade students as they completed a computer-based argumentative writing task, the study derived learning analytics indicators of metacognitive strategy and examined their validity by comparing them with survey results. It also tested whether these metacognitive indicators predicted writing task performance. The findings provide useful implications for developing AI-based educational systems that can automatically analyze and adaptively support learners’ metacognitive strategies.
Kim, H., Cho, Y. H., Han, J., Kang, M., Kim, A., & Lee, G. (2025, November). Metacognitive Strategy Analysis Using Log Data from Digital Activities. Joint Fall Conference of the Korea Association of Educational Information and Media and the Korean Society for Educational Technology, Seoul, South Korea.

Exploring the Interaction between Learners and AI in Writing Tasks

This study investigates learner-AI interaction patterns during writing tasks among 28 university students using ChatGPT. Participants were categorized into four groups—Passive, Learner-directed, AI-dependent, and Collaborative—based on their behavioral engagement. The analysis revealed that writing self-efficacy was the key differentiator, with the Collaborative group demonstrating the highest self-efficacy and a sophisticated division of labor. These findings suggest that fostering student agency and self-efficacy is essential for learners to utilize AI effectively as a "critical collaborator" rather than a substitute.
Kim, H., Cho, Y. H., Shin, B., Han, Y., Son, Y., & Kim, A. (2025, August). Exploring the interaction between learners and AI in Writing tasks [Poster presentation]. 40th European Association for Research in Learning and Instruction, Graz, Austria.

Developing and validating a scale for measuring Learner-AI collaboration

This study develops and validates the Learner–AI Collaboration Competency Scale (LACCS) to assess students’ competence in allocating roles and managing tasks—sharing, coordinating, and integrating—with AI through ongoing interaction to achieve a common goal. The scale initially included five hypothesized subscales: Engagement (EG), Fluency (FL), Team bond (TB), Interdependence (INT), and Task regulation (TSK). Two independent samples of high school students were collected to examine the factor structures: the first sample (n=195) was used for exploratory factor analysis, and the second (n=356) for confirmatory factor analysis. The final version of the LACCS consists of a total of 25 items loading onto the five intended factors. Construct validity was supported through EFA and CFA, and evidence for concurrent validity was obtained by examining correlations with measures of digital literacy, AI literacy, positive AI attitude and collaborative learning skills. These findings provide empirical support for the internal structure and external associations of the LACCS. The instrument offers a basis for future research and instructional practices aiming to enhance students’ learner-AI collaboration.
Kim, H., Cho, Y. H., Ham, E., Kang, M., & Son, Y. (2025, July). Developing and validating a scale for measuring Learner-AI collaboration [Paper presentation]. 24th of International Conference on Education Research, Seooul, South Korea.

Learning analytics of Cognitive and Metacognitive Strategies in Video-based Learning

This study develops and validates the Learner–AI Collaboration Competency Scale (LACCS) to assess students’ competence in allocating roles and managing tasks—sharing, coordinating, and integrating—with AI through ongoing interaction to achieve a common goal. The scale initially included five hypothesized subscales: Engagement (EG), Fluency (FL), Team bond (TB), Interdependence (INT), and Task regulation (TSK). Two independent samples of high school students were collected to examine the factor structures: the first sample (n=195) was used for exploratory factor analysis, and the second (n=356) for confirmatory factor analysis. The final version of the LACCS consists of a total of 25 items loading onto the five intended factors. Construct validity was supported through EFA and CFA, and evidence for concurrent validity was obtained by examining correlations with measures of digital literacy, AI literacy, positive AI attitude and collaborative learning skills. These findings provide empirical support for the internal structure and external associations of the LACCS. The instrument offers a basis for future research and instructional practices aiming to enhance students’ learner-AI collaboration.
Kim, H., Cho, Y. H., Han, J., Kang, M., & Kim, A. (2025, July). Learning Analytics of Cognitive and Metacognitive Strategies in Video-based Learning [Paper presentation]. 24th International Conference on Education Research, Seoul, South Korea.

Human-AI Interaction

Exploring the Interaction between L2 Learners and AI Translator in English Writing

This study investigated how L2 learners interact with an AI translator for English writing and explored the effects of the English writing skills on interaction patterns. L2 learners (n=21) wrote an essay in English, using AI translator. As a result, three clusters were found: English writing, Korean writing, Interactive writing. And the interaction pattern was different according to the expression scores. The result of this study provides implications in designing interactions between learners and AI.
Kim, H., Park, S., & Cho, Y. H. (2022, June). Exploring the Interaction between L2 Learners and AI Translator in English Writing [Poster presentation]. 16th International Conference of the Learning Sciences (ICLS 2022), Hiroshima, Japan.

• Kim, H., Cho, Y. H., Kang, M., & Son, Y. (2026, April). Design-Based Research to Enhance Collaboration between Learners and AI in a Middle School Writing Class [Roundtable presentation]. Annual Meeting of the American Educational Research Association (AERA), Los Angeles, CA.

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