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Teachers, Parents, and Students' perspectives on Integrating Generative AI into Elementary Literacy Education
Open accessAbstract
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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“Teachers and parents are likewise introducing conversational AI into classrooms and homes-to generate teaching materials, orchestrate chained questioning, co-author stories, and automate grading [8,9].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [9]
In the citing paper
“Furthermore, GenAI facilitates intercultural competence by incorporating diverse cultural contents into language learning.”
From Generative AI in Language Education: Bridging Divide and Fostering Inclusivity (2025) · cites this paper as Han et al., 2024
In the citing paper
“Cultural understanding and the reduction of biases in language learning, highlighted in studies by Han et al. (2024) and Lee et al. (2024), suggest that GenAI can serve as culturally intelligent tools.”
From Generative AI in Language Education: Bridging Divide and Fostering Inclusivity (2025) · cites this paper as Han et al., 2024
In the citing paper
“A parent mentioned using GenAI to generate content about traditional Asian dragons to teach her child about their cultural heritage.”
From Generative AI in Language Education: Bridging Divide and Fostering Inclusivity (2025) · cites this paper as Han et al., 2024
In the citing paper
“The findings pinpoint that GenAI can provide culturally relevant examples and feedback.”
From Generative AI in Language Education: Bridging Divide and Fostering Inclusivity (2025) · cites this paper as Han et al., 2024
In the citing paper
“Also, GenAI can create culturally salient stories, like fables, which resonate with a learner's heritage.”
From Generative AI in Language Education: Bridging Divide and Fostering Inclusivity (2025) · cites this paper as Han et al., 2024
In the citing paper
“In the reviewed papers, two studies [10,24] explicitly suggest that when AI systems are allowed to freely generate content, students may rely on AI for the entire output, completing their work with minimal engagement in the creative process.”
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“Other papers [10,24,37,61] discussed how misinformation or biased content can emerge unpredictably, especially when children rely heavily on generative models without understanding their underlying data sources.”
From Child-AI Co-Creation: A Review of the Current Research Landscape and a Proposal for Six Design Considerations (2025) · cites this paper as [24]
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“%) and generative AI (GAI) systems (e.g., ChatGPT) can aid writing feedback [36], they face challenges regarding interactional rigidity, algorithmic bias, and authorship.”
CNReader: a reading practice tool designed for Chinese children with developmental dyslexia
Liu, L., Fang, T., Liu, E., Shi, S. M., Zhai, S., Chen, Y., Zhang, L., Shi, Y., & Yao, C. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-05079-1 · Cited by 2
“Existing research (Han et al., 2024) also points out that over-reliance on technology may have negative impacts on children ’ s independent learning abilities and social adaptability.”
ASL Educators' Perspectives on AI for Enhancing Student Learning in American Sign Language Education
Hassan, S., Nourian, L., de Lacerda Pataca, C., M Olson, M., D'aurio, T., Agarwal, K., Asad, S. M. N., Tigwell, G. W., & Huenerfauth, M. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791928 · Cited by 1
Reimann, D. (2025) · Springer series on cultural computing · doi:10.1007/978-3-031-89037-6_6 · Cited by 1
Demystifying the mist: Why do individuals hesitate to accept AI educational services?
Shao, A., Lü, Z., Liu, S. Q., Shi, Y., & Lu, W. (2025) · British Journal of Psychology · doi:10.1111/bjop.70040 · Cited by 1
“In practice, users are concerned about issues such as information sources, students' initiative in learning and the uncertainty of biases and misinformation (Han et al., 2024).”
Pade, C. S., & Monga, C. (2025) · International Conference of Human-Computer Interaction Design & Research · doi:10.1145/3768633.3770139 · Cited by 1
Andleeb, S., Kantorski, B., & Carver, J. C. (2025) · Conference on Information Technology Education · doi:10.1145/3769694.3771128 · Cited by 2
“This prior work motivates our third research question.”
Teachers’ perceptions of generative AI in gender-inclusive STEM education: a grounded theory study
Tipple, C., Keane, T., Wang, T., & Cerovac, M. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-13908-2 · Cited by 2
“…STEM remains limited, broader literature supports these findings, highlighting the potential of GenAI use cases for brainstorming (Chen & Zhu, 2023; Han et al., 2024) and also for dynamically adapting instructional materials based on students’ knowledge levels, performance, and interests, thereby…”
Wang, Y., Lei, K., Chiu, S., Isbister, K., Lee, D. T., & Ringland, K. E. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791880 · Cited by 3
“Following established practice in HCI (e.g., [36, 44, 63, 70]), we adopted a top-down approach in which each RQ (information flows, relational roles, temporal dynamics) served as an analytic lens.”
Kleveland, J., Giannakos, M. N., & Lindvig, Y. (2026) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3785022.3785052 · Cited by 1
StoryLensEdu: Personalized Learning Report Generation through Narrative-Driven Multi-Agent Systems
Shen, L., Luo, Y., Sheng, R., He, Y., Li, H., Yang, L., & Qu, H. (2026) · IEEE Pacific Visualization Conference (PacificVis) · doi:10.1109/pacificvis68791.2026.00039 · Cited by 1
Human-AI Collaborative Intelligence and Pedagogies of Hybrid Cognition in Learning Ecosystems
Alam, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7036-1.ch010 · Cited by 1
Jayathilake, H. M., & Ma, R. (2026) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3773077.3806126 · Cited by 1
“Systematic versus creative distinction [10, 23] appears exclusively in the concrete operational stage, marking this as Emerges as children map familiar social scripts ontothechatbot,fulfillingneedsforattachment orfunctionalassistancethroughdistinctrelationalroles.”
InfluentialCzech, E., Tabbaa, L., Li, X., Ppali, S., Cullen, Y., Chee, S., Verma, H., & Covaci, A. (2026) · International Conference on Interaction Design and Children · doi:10.1145/3773077.3806129 · Cited by 1
“Human–Computer Interaction (HCI) research has examined a wide range of ways in which digital tools can support process-based learning [17, 22, 31, 33, 45].”
Agency in Child–AI Interaction: A Review of How It Is Conceptualised, Studied, and Supported in HCI
Voysey, I., Vizgirda, V., Turner, S., Duran, L. D. D., Pauzi, Z., Mavrikis, M., Prunkl, C., & Zhao, J. (2026) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3773077.3806105 · Cited by 3
Sakowicz, M., & Hamidi, F. (2026) · Universal Access in the Information Society · doi:10.1007/s10209-026-01370-3 · Cited by 2
“The research revealed that teachers recognize a need to learn and integrate AI-enabled technologies into their practice, acknowledging that these technologies are already embedded in their students’ daily lives [ 4].”
InfluentialOptimizing the use of ai in elementary school learning: For teachers in the digital era
Fadli, M. R., Santosa, I., Alfian, A., Syofyan, H., & Fatonah, K. (2025) · Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) · doi:10.33474/jp2m.v6i1.23358 · Cited by 4
Collaborative AI for Global Education Networks
Sukmawati, S., Limbong, S., Rinantanti, Y., Asdar, A., & Sujarwo, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5786-7.ch001
Dursun, F. (2025) · Anadolu Üniversitesi Sanat & Tasarım Dergisi · doi:10.20488/sanattasarim.1717602
Technology Literacy in Empowering University Students' Extrinsic Motivation in Language Learning
Asia, M., Rahim, A., & Hamsiah, A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5781-2.ch014
Integration of AI in Language Learning and Literary Education
Asdar, A., Asia, M., Asdam, M., Magfirah, S., & Sujarwo, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6330-1.ch004
Supporting Middle School English Teachers’ AI Literacy Goals Through a Generative AI Tutor
Ritchie, D., Tate, T. P., Zhang, Y., Werry, K., & Warschauer, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98465-5_36 · Cited by 1
Camelo, V. C. S., & Siebra, C. (2025) · Anais do Workshop sobre Educação em Computação (WEI) · doi:10.5753/wei.2025.7429
Implicit SDG Reasoning in LLMs: A Classroom-Oriented Benchmark
Hermann, J., Jansen, N., Doğangün, A., & van Ledden, S. (2025) · Australian Conference on Human-Computer Interaction (OzCHI) · doi:10.1145/3764687.3769916
An experimental evaluation of an AI-powered interactive learning platform
Heldreth, C. M., Akrong, D., Vardoulakis, L., Miller, N. E., Haramaty, Y., Belinsky, L., Hackmon, L., Tapia, A. O., Tootill, L., & Siebert, S. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1783117 · Cited by 1
“These transformations of chunking and scaffolding are not just “extra features,” but mechanisms by which AI generates “dynamic” content rather than “static”.”
An, Z., & Wang, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3638665
“Especially when faced with innovative technologies like AIGC, parents’ judgment mechanisms regarding its potential educational value, ethical risks, and adaptability show consistency and stability across educational stages [24].”
From Oral Traditions to AI-Generated Histories: The Role of Technology in Narrating the Past
Alomoush, A. N. (2026) · Studies in systems, decision and control · doi:10.1007/978-3-031-95310-1_124
Gözüm, A. İ. C., & OZBEY, N. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9074-1.ch001
TASARIMDA YAPAY ZEKANIN ROLÜ: PRESTİJLİ ÜNİVERSİTE LOGOLARININ YENİDEN ÜRETİMİ ÜZERİNE BİR ARAŞTIRMA
Dursun, F. (2024) · DergiPark (Istanbul University)
Yan, Y., Song, L., & liu, T. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6297166
Enhancing Early Childhood Learning Through AI-Generated Personalized Stories with Image Integration
Hattab, S., Salem, H., Pezer, M., & Mazzara, M. (2026) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-95-4423-3_57
CAT-AI: Supporting Teacher Workflows with AI-Assisted Exercise Creation
Calò, T., Cuccu, L., & Russis, L. D. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3799367
Developmentally Safe Generative AI Environment for Youth
Chanenson, J., Yu, Y.-M., Vitak, J., Erete, S., Clegg, T. L., Freed, D., & Chetty, M. (2026) · CHI Extended Abstracts · doi:10.1145/3772363.3778682
The Embodiment Trap: When AI Has a Body in Education
Constantinides, M., Ppali, S., Charalambidou, C., & Liarokapis, F. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3798693
“Large class sizes, limited resources, and increasing demands for individualized support create pressures that make AI-based assistance appear attractive [13, 16].”
Exploring GenAI as an Alternative to Traditional Language Learning
Eroglu, U., Oran, Ç., Severdim, K., Çelik, K. K., & Hancı-Azizoğlu, E. B. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/407558
ELLA: Generative AI-Powered Social Robots for Early Language Development at Home
Antony, V. N., Cao, S., Wang, S., & Huang, C. (2026) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3773077.3806113
Integrating Educator-Designed AI Tools in Classrooms: Teachers’ Practices, Perceptions, and Concerns
Ch, N. A. N., Wong, A. Y., Corcoran, B., Barbot, H., Ahmad, Y., & Mills, C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29763-1_27
Tzagkaraki, E., Papadakis, S., & Kalogiannakis, M. (2026) · Discover Education · doi:10.1007/s44217-026-01558-y · Cited by 1
“They are considered models of technological applications, which, when properly utilized, effectively promote interpersonal communication, interactivity, and access to information, regardless of location and time [20].”
Younes, M. T., & Ali, M. H. (2026) · International Symposium on AI-Driven Engineering Systems (ISADES) · doi:10.1109/isades69945.2026.11608174
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
- Semantic Scholar
- 58c21b7488ec9d5cdf51507c740277acacf10f3d
- Cited by
- 114
- References
- 80
- Citations by year
- 2026: 50 · 2025: 54 · 2024: 10
- FWCI
- 43.31
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- 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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