Cited by 116Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationEducation and Technology Integration

The DOI matches its Crossref record; 86 of the 86 references it cites were found in a registry or an index.

Teachers, Parents, and Students' perspectives on Integrating Generative AI into Elementary Literacy Education

Open access
Han, Ariel, Zhou, Xiaofei, Cai, Zhenyao, Han, Shenshen, Ko, Richard, Corrigan, Seth, Peppler, Kylie A·2024·Proceedings of the CHI Conference on Human Factors in Computing Systems

Abstract

The viral launch of new generative AI (GAI) systems, such as ChatGPT and Text-to-Image (TTL) generators, sparked questions about how they can be effectively incorporated into writing education. However, it is still unclear how teachers, parents, and students perceive and suspect GAI systems in elementary school settings. We conducted a workshop with twelve families (parent-child dyads) with children ages 8-12 and interviewed sixteen teachers in order to understand each stakeholder’s perspectives and opinions on GAI systems for learning and teaching writing. We found that the GAI systems could be beneficial in generating adaptable teaching materials for teachers, enhancing ideation, and providing students with personalized, timely feedback. However, there are concerns over authorship, students’ agency in learning, and uncertainty concerning bias and misinformation. In this article, we discuss design strategies to mitigate these constraints by implementing an adults-oversight system, balancing AI-role allocation, and facilitating customization to enhance students’ agency over writing projects.

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Crossref · 10.1145/3613904.3642438

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Research works printed but not linked: 17

  1. Debby RE Cotton, Peter A Cotton, and J Reuben Shipway. 2023. Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International (2023), 1–12.
  2. Giorgio Franceschelli and Mirco Musolesi. 2023. On the creativity of large language models. arXiv preprint arXiv:2304.00008 (2023). · arXiv
  3. 10.1145/3313831.3376631 · DOI · Paper page
  4. Steve Graham, Alisha Bollinger, Carol Booth Olson, Catherine D’Aoust, Charles MacArthur, Deborah McCutchen, and Natalie Olinghouse. 2012. Teaching Elementary School Students to Be Effective Writers: A Practice Guide. NCEE 2012-4058.What Works Clearinghouse (2012).
  5. Mohammad Khalil and Erkan Er. 2023. Will ChatGPT get you caught? Rethinking of plagiarism detection. arXiv preprint arXiv:2302.04335 (2023). · arXiv
  6. Alyson Klein. 2023. 180 Degree Turn: NYC District Goes From Banning ChatGPT to Exploring AI’s Potential. Education Week (Oct. 2023). https://www.edweek.org/technology/180-degree-turn-nyc-schools-goes-from-banning-chatgpt-to-exploring-ais-potential/2023/10
  7. Wander ly. 2023. Wanderly: Magical adventures for curious children. https://app.wander.ly/
  8. Igor Pesek, Novica Nosović, and Marjan Krašna. 2022. The Role of AI in the Education and for the Education. In 2022 11th Mediterranean Conference on Embedded Computing (MECO). IEEE, 1–4.
  9. Mitchel Resnick, Brad Myers, Kumiyo Nakakoji, Ben Shneiderman, Randy Pausch, and Mike Eisenberg. 2005. Design Principles for Tools to Support Creative Thinking. Report of Workshop on Creativity Support Tools 20 (Jan. 2005).
  10. David R Thomas. 2006. A general inductive approach for analyzing qualitative evaluation data. American journal of evaluation 27, 2 (2006), 237–246.
  11. Levent Uzun. 2023. ChatGPT and academic integrity concerns: Detecting artificial intelligence generated content. Language Education and Technology 3, 1 (2023).
  12. Ge Wang, Jun Zhao, Max Van Kleek, and Nigel Shadbolt. 2022. ’Don’t make assumptions about me!’: Understanding Children’s Perception of Datafication Online. Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (2022), 1–24.
  13. Lu Wang, Munif Ishad Mujib, Jake Williams, George Demiris, and Jina Huh-Yoo. 2021. An evaluation of generative pre-training model-based therapy chatbot for caregivers. arXiv preprint arXiv:2107.13115 (2021). · arXiv
  14. Tongshuang Wu Michael Terry and Carrie J. Cai. 2022. AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts | Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. https://dl.acm.org/doi/abs/10.1145/3491102.3517582 · DOI
  15. 10.1145/3411764.3445271 · DOI
  16. 10.1145/3411764.3445271 · DOI
  17. Daijin Yang, Yanpeng Zhou, Zhiyuan Zhang, and Toby Jia-Jun Li. 2022. AI as an Active Writer: Interaction strategies with generated text in human-AI collaborative fiction writing. ACM IUI Workshop (2022).

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Metadata

Authors
Han, A., Zhou, X., Cai, Z., Han, S., Ko, R., Corrigan, S., & Peppler, K. A.
Affiliations
  • University of California, Irvine (US)
  • University of Rochester (US)
  • UC Irvine, United States
Venue
Proceedings of the CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2024-05-11
Pages
1-17
OpenAlex
W4396832735
Cited by
114
References
80
Citations by year
2026: 50 · 2025: 54 · 2024: 10
FWCI
43.31
Type
conference paper
Language
en
Open access
Locations
CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by)
Topics
  • Social Sciences › Education › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Education › Education and Technology Integration
Keywords
generative AI, writing instruction, teacher perceptions, parental attitudes, student perceptions, ChatGPT, authorship, student agency, personalized feedback, idea generation

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