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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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In the citing paper
“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.”
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]
In the citing paper
“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]
Citations
Cites
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
Digital Natives, Digital Immigrants Part 1
Prensky, M. (2001) · On the Horizon · doi:10.1108/10748120110424816 · Cited by 11,104
The Coding Manual for Qualitative Researchers
Lungu, M. (2022) · American Journal of Qualitative Research · doi:10.29333/ajqr/12085 · Cited by 10,936 · cited 1×
“Following this, we utilized qualitative data analysis software for an initial round of open coding, adhering to established qualitative research methodologies [93, 103].”
Artificial Intelligence in Education: A Review
Chen, L., Chen, P., & Lin, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2988510 · Cited by 4,133 · cited 1×
“These technologies have enhanced teaching and learning [11, 21, 55, 76, 84, 101, 117, 121], yet little of this prior work addresses directly how AIED integrates holistically into educational settings [21, 54].”
BAİDOO-ANU, D., & OWUSU ANSAH, L. (2023) · Journal of AI · doi:10.61969/jai.1337500 · Cited by 3,939
Flow: The Psychology of Optimal Experience
Mirvis, P. H., & Csikszentmihalyi, M. (1991) · Academy of Management Review · doi:10.2307/258925 · Cited by 3,758
Baidoo-Anu, D., & Owusu Ansah, L. (2023) · doi:10.2139/ssrn.4337484 · Cited by 3,004
On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676
Vision, challenges, roles and research issues of Artificial Intelligence in Education
Hwang, G.-J., Xie, H., Wah, B. W., & Gašević, D. (2020) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2020.100001 · Cited by 1,435
Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023) · The International Journal of Management Education · doi:10.1016/j.ijme.2023.100790 · Cited by 1,261
Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100118 · Cited by 1,181
Self-citationAronson, B., & Laughter, J. (2016) · Review of Educational Research · doi:10.3102/0034654315582066 · Cited by 928
A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT
White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., & Schmidt, D. C. (2023) · arXiv · doi:10.48550/arxiv.2302.11382 · Cited by 838 · cited 1×
“Unlike traditional MOOC platforms, which rely on human-guided instructions, educators can now train the LLM with specialized datasets and employ prompt engineering techniques [114] to enable AI to construct instructional content autonomously.”
Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory
Zhou, H. M., Huang, M., Zhang, T., Zhu, X., & Liu, B. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11325 · Cited by 809
Education 4.0 Made Simple: Ideas For Teaching
Hussin, A. A. (2018) · International Journal of Education and Literacy Studies · doi:10.7575/aiac.ijels.v.6n.3p.92 · Cited by 741
Extracting Training Data from Large Language Models
Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07805 · Cited by 727 · cited 1×
“AI hallucination occurs when a machine learning model generates content that doesn’t align with the intended output [13, 18].”
Design Guidelines for Prompt Engineering Text-to-Image Generative Models
Liu, V., & Chilton, L. B. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501825 · Cited by 639
Graesser, A. C., McNamara, D. S., & Kulikowich, J. M. (2011) · Educational Researcher · doi:10.3102/0013189x11413260 · Cited by 635
Design Guidelines for Prompt Engineering Text-to-Image Generative Models
Liu, V., & Chilton, L. B. (2021) · arXiv · doi:10.48550/arxiv.2109.06977 · Cited by 635
Re-examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to Design
Yang, Q., Steinfeld, A., Rosé, C., & Zimmerman, J. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376301 · Cited by 622
Chounta, I., Bardone, E., Raudsep, A., & Pedaste, M. (2021) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00243-5 · Cited by 622
A Conversation on Artificial Intelligence, Chatbots, and Plagiarism in Higher Education
King, M. R., & chatGPT (2023) · Cellular and Molecular Bioengineering · doi:10.1007/s12195-022-00754-8 · Cited by 602
Linguistic Features of Writing Quality
McNamara, D. S., Crossley, S. A., & McCarthy, P. M. (2010) · Written Communication · doi:10.1177/0741088309351547 · Cited by 570 · cited 1×
“…a story with characters, events, places, and things, Syntactic Complexity : the complexity of the text’s syntactic structure, Vocabulary : sophistication and concreteness of students’ word choice, Grammatical Correctness : the extent to which students’ texts adhere to grammar norms [44, 45, 73].”
BackgroundComparing the quality of human and ChatGPT feedback of students’ writing
Steiss, J., Tate, T., Graham, S., Cruz, J., Hebert, M., Wang, J., Moon, Y., Tseng, W., Warschauer, M., & Olson, C. B. (2024) · Learning and Instruction · doi:10.1016/j.learninstruc.2024.101894 · Cited by 509 · cited 2×
“The ongoing discourse in education around generative AI (GAI) emphasizes the need for comprehensive research into its integration within educational contexts [98].”
Semantic text similarity using corpus-based word similarity and string similarity
Islam, A., & Inkpen, D. Z. (2008) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1376815.1376819 · Cited by 492 · cited 1×
“To diferentiate between student-generated content and machine-generated text, the platform will employ text-similarity analysis [57].”
MethodArtificial Intelligence (AI) Literacy in Early Childhood Education: The Challenges and Opportunities
Su, J., Ng, D. T. K., & Chu, S. K. W. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100124 · Cited by 489
A Pragmatic Definition of the Concept of Theoretical Saturation
Low, J. (2019) · Sociological Focus · doi:10.1080/00380237.2018.1544514 · Cited by 445
Wordcraft: Story Writing With Large Language Models
Yuan, A., Coenen, A., Reif, E., & Ippolito, D. (2022) · 27th International Conference on Intelligent User Interfaces · doi:10.1145/3490099.3511105 · Cited by 374
Tahiru, F. (2020) · Journal of Cases on Information Technology · doi:10.4018/jcit.2021010101 · Cited by 369 · cited 1×
“These technologies have enhanced teaching and learning [11, 21, 55, 76, 84, 101, 117, 121], yet little of this prior work addresses directly how AIED integrates holistically into educational settings [21, 54].”
Lee, M., Liang, P., & Yang, Q. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502030 · Cited by 345
Communication Breakdowns Between Families and Alexa
Beneteau, E., Richards, O. K., Zhang, M., Kientz, J. A., Yip, J., & Hiniker, A. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300473 · Cited by 313 · cited 2×
“These technologies have enhanced teaching and learning [11, 21, 55, 76, 84, 101, 117, 121], yet little of this prior work addresses directly how AIED integrates holistically into educational settings [21, 54].”
BackgroundEngaging Teachers to Co-Design Integrated AI Curriculum for K-12 Classrooms
Lin, P., & Brummelen, J. V. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445377 · Cited by 304
A is for Artificial Intelligence
Williams, R., Park, H. W., & Breazeal, C. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300677 · Cited by 297
Plagiarism in the age of massive generative pre-trained transformers (GPT-3)
Dehouche, N. (2021) · Ethics in Science and Environmental Politics · doi:10.3354/esep00195 · Cited by 296 · cited 1×
“Consequently, there is a need for further research into writers’ ownership, authorship, and plagiarism, in addition to developing new methods for assessing and measuring writers’ progress [29, 58, 59].”
Challenges in building intelligent open-domain dialog systems
Huang, M., Zhu, X., & Gao, J. (2020) · ACM Transactions on Information Systems · doi:10.1145/3383123 · Cited by 288 · cited 1×
“The recent LLMs have brought breakthroughs of open-ended conversational systems, which perform open-domain dialog with any topics [51] and it ofers the capability to be fne-tuned [82], enhancing its performance to align with specifc domains and instructional objectives [112, 129].”
BackgroundAcademic integrity and artificial intelligence: is ChatGPT hype, hero or heresy?
Currie, G. M. (2023) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2023.04.008 · Cited by 283 · cited 1×
“Our fndings highlighted that teachers are concerned about their students’ authorship and integrity of their writing output, particularly when GAI generates the majority of the content for students [25, 28, 64].”
Creative Writing with a Machine in the Loop: Case Studies on Slogans and Stories
Clark, E., Ross, A. S., Tan, C., Ji, Y., & Smith, N. A. (2018) · 23rd International Conference on Intelligent User Interfaces · doi:10.1145/3172944.3172983 · Cited by 257
Freeman, L. M., & Blaufox, M. D. (1972) · Seminars in Nuclear Medicine · doi:10.1016/s0001-2998(72)80062-x · Cited by 241
A systematic review of teaching and learning machine learning in K-12 education
Sanusi, I. T., Oyelere, S. S., Vartiainen, H., Suhonen, J., & Tukiainen, M. (2023) · Education and Information Technologies · doi:10.1007/s10639-022-11416-7 · Cited by 212
Coh-Metrix Measures Text Characteristics at Multiple Levels of Language and Discourse
Graesser, A. C., McNamara, D. S., Cai, Z., Conley, M. W., Li, H., & Pennebaker, J. W. (2014) · The Elementary School Journal · doi:10.1086/678293 · Cited by 199 · cited 1×
“…a story with characters, events, places, and things, Syntactic Complexity : the complexity of the text’s syntactic structure, Vocabulary : sophistication and concreteness of students’ word choice, Grammatical Correctness : the extent to which students’ texts adhere to grammar norms [44, 45, 73].”
BackgroundSelf-citationGraham, S., Kiuhara, S. A., Harris, K. R., & Fishman, E. J. (2017) · The Elementary School Journal · doi:10.1086/693009 · Cited by 176 · cited 1×
“Early interventions during this stage can greatly infuence a child’s educational path and future opportunities[ 47].”
BackgroundZhang, Z., Xu, Y., Wang, Y., Yao, B., Ritchie, D., Wu, T., Yu, M., Wang, D., & Li, T. J.-J. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517479 · Cited by 174
Failure to Disrupt: Why Technology Alone Can’t Transform Education
Johnson, L. (2022) · Journal of Teaching and Learning · doi:10.22329/jtl.v16i1.7033 · Cited by 166 · cited 7×
“Personalized learning platforms claim to tailor education to individual student needs, but they often fall short in practice due to the complexities of learning processes, efective pedagogies, and the constraints of algorithmic customization [87].”
InfluentialSparks: Inspiration for Science Writing using Language Models
Gero, K. I., Liu, V., & Chilton, L. (2022) · Designing Interactive Systems Conference · doi:10.1145/3532106.3533533 · Cited by 161
Emerging Technologies in K–12 Education: A Future HCI Research Agenda
Mechelen, M. V., Smith, R. C., Schaper, M.-M., Tamashiro, M. A., Bilstrup, K.-E. K., Lunding, M. S., Petersen, M. G., & Iversen, O. S. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3569897 · Cited by 158
Information We Collect: Surveillance and Privacy in the Implementation of Google Apps for Education
Lindh, M., & Nolin, J. M. (2016) · European Educational Research Journal · doi:10.1177/1474904116654917 · Cited by 136 · cited 1×
“These dilemmas include the preference for familiar tools, the unequal benefts of new technologies, the challenge of nuanced assessment beyond binary right or wrong answers, and the issues of data privacy and equity [66].”
BackgroundTeachers’ perspective on artificial intelligence education: An initial investigation
Polak, S., Schiavo, G., & Zancanaro, M. (2022) · CHI Conference on Human Factors in Computing Systems Extended Abstracts · doi:10.1145/3491101.3519866 · Cited by 132
Family as a Third Space for AI Literacies: How do children and parents learn about AI together?
Druga, S., Christoph, F. L., & Ko, A. J. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502031 · Cited by 130
Social Dynamics of AI Support in Creative Writing
Gero, K. I., Long, T., & Chilton, L. B. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580782 · Cited by 129
Design implications of generative AI systems for visual storytelling for young learners
Han, A., & Cai, Z. (2023) · Proceedings of the 22nd Annual ACM Interaction Design and Children Conference · doi:10.1145/3585088.3593867 · Cited by 127
Self-citationWhere to Hide a Stolen Elephant: Leaps in Creative Writing with Multimodal Machine Intelligence
Singh, N., Bernal, G., Savchenko, D., & Glassman, E. L. (2023) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3511599 · Cited by 119
Opal: Multimodal Image Generation for News Illustration
Liu, V., Qiao, H., & Chilton, L. (2022) · Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology · doi:10.1145/3526113.3545621 · Cited by 104
GenAICHI: Generative AI and HCI
Muller, M., Chilton, L. B., Kantosalo, A., Martin, C. P., & Walsh, G. (2022) · CHI Conference on Human Factors in Computing Systems Extended Abstracts · doi:10.1145/3491101.3503719 · Cited by 101
Generative artificial intelligence and the education sector
Ahmad, N., Murugesan, S., & Kshetri, N. (2023) · Computer · doi:10.1109/mc.2023.3263576 · Cited by 95
What Are You Talking To?: Understanding Children's Perceptions of Conversational Agents
Xu, Y., & Warschauer, M. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376416 · Cited by 82
The Last Decade of HCI Research on Children and Voice-based Conversational Agents
Garg, R., Cui, H., Seligson, S., Zhang, B., Porcheron, M., Clark, L., Cowan, B. R., & Beneteau, E. (2022) · International Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502016 · Cited by 82
Emotional Dialogue Generation using Image-Grounded Language Models
Huber, B., McDuff, D., Brockett, C., Galley, M., & Dolan, B. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173851 · Cited by 74 · cited 1×
“The HCI research community has started to examine utilities and interaction techniques with these systems [63, 116], focusing on new interaction styles [52, 125], Large Language Models’ (LLM) capacities [63], and how to adapt the systems to creative activities for adults [43, 68].”
BackgroundLearning Transferable Human-Object Interaction Detector with Natural Language Supervision
Wang, S., Duan, Y., Ding, H., Tan, Y., Yap, K., & Yuan, J. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00101 · Cited by 72
Garg, R., & Sengupta, S. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376631 · Cited by 68 · cited 1×
“Findings reveal that parents desire CA to foster children’s social engagement and involve parents in in-home learning [41].”
BackgroundHunter, S., Kalanithi, J., & Merrill, D. J. (2010) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/1810543.1810572 · Cited by 63
Role design considerations of conversational agents to facilitate discussion and systems thinking
Nguyen, H. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104661 · Cited by 62
Young Children's Reading and Learning with Conversational Agents
Xu, Y., & Warschauer, M. (2019) · Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290607.3299035 · Cited by 60
Story Centaur: Large Language Model Few Shot Learning as a Creative Writing Tool
Swanson, B., Mathewson, K. W., Pietrzak, B., Chen, S., & Dinalescu, M. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-demos.29 · Cited by 59
New Opportunities for Interest-Driven Arts Learning in a Digital Age
Peppler, K. (2013) · doi:10.59656/a-ya8704.001 · Cited by 55
Self-citationCollaborative Storytelling with Large-scale Neural Language Models
Nichols, E., Gao, L., & Gomez, R. (2020) · Motion, Interaction and Games · doi:10.1145/3424636.3426903 · Cited by 50 · cited 1×
“…such conversational, drawing, or robotic interactions, researchers have developed diferent LLMs as supports for collaborative creative writing [77, 100], where creativity requires writing with a relevant purpose, understanding, judgment, and evaluative abilities in ways that are deemed…”
BackgroundThree approaches to measuring children's perceptions about technology
Rennie, L. J., & Jarvis, T. (1995) · International Journal of Science Education · doi:10.1080/0950069950170607 · Cited by 48 · cited 2×
“We sought to understand students’ opinions and their perceptions of AI by posing the following questions [88]: What do you like or dislike about using ChatGPT and Stable Difusion for your creative writing and visuals?, Have you found AI useful?, How can artifcial intelligence help you?”
BackgroundLiving Memories: AI-Generated Characters as Digital Mementos
Pataranutaporn, P., Danry, V., Blanchard, L., Thakral, L., Ohsugi, N., Maes, P., & Sra, M. (2023) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3581641.3584065 · Cited by 48
Xu, Y., He, K., Vigil, V., Ojeda-Ramírez, S., Liu, X., Levine, J., Cervera, K., & Warschauer, M. (2023) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3585088.3589354 · Cited by 47
Generative AI Considered Harmful
Fischer, J. E. (2023) · Proceedings of the 5th International Conference on Conversational User Interfaces · doi:10.1145/3571884.3603756 · Cited by 47 · cited 1×
“These safeguards may include content fltering, moderation of user inputs, and continuous improvement of the chatbot’s response mechanisms to detect and handle inappropriate or harmful content [36, 81, 92].”
BackgroundWang, G., Zhao, J., Kleek, M. G. V., & Shadbolt, N. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580933 · Cited by 46
Pedagogical Agents for Fostering Question-Asking Skills in Children
Alaimi, M., Law, E., Pantasdo, K. D., Oudeyer, P.-Y., & Sauzeon, H. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376776 · Cited by 44
Children’s agency by design: Design parameters for personalization in story-making apps
Kucirkova, N. (2019) · International Journal of Child-Computer Interaction · doi:10.1016/j.ijcci.2019.06.003 · Cited by 36 · cited 1×
“According to the workshop with students and teacher interviews, promoting students’ agency as writers is essential [62], especially for enhancing learning experiences.”
BackgroundStoryDrawer: A Co-Creative Agent Supporting Children's Storytelling through Collaborative Drawing
Zhang, C., Yao, C., Liu, J., Zhou, Z., Zhang, W., Liu, L., Ying, F., Zhao, Y., & Wang, G. (2021) · Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411763.3451785 · Cited by 35
Oversold and Underused: Computers in the Classroom
(2004) · International Journal of Educational Management · doi:10.1108/09513540410527202 · Cited by 34
Comparing the Quality of Human and ChatGPT Feedback on Students’ WritingPreprint
Steiss, J., Tate, T. P., Graham, S., et al. (2023) · doi:10.35542/osf.io/ty3em · Cited by 34
Are Current Voice Interfaces Designed to Support Children’s Language Development?
Xu, Y., Branham, S. M., Deng, X., Collins, P., & Warschauer, M. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445271 · Cited by 28
CreativeBot: a Creative Storyteller robot to stimulate creativity in children
Elgarf, M., Zojaji, S., Skantze, G., & Peters, C. (2022) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3536221.3556578 · Cited by 24
Brummelen, J. V., Kelleher, M., Tian, M. C., & Nguyen, N. H. (2023) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3585088.3589353 · Cited by 24
Culturally sustaining pedagogies: Teaching and learning for justice in a changing world
Gaynor, C., & Akay, M. (2019) · Diaspora Indigenous and Minority Education · doi:10.1080/15595692.2019.1669016 · Cited by 22
Agostinelli, F., Mavalankar, M., Khandelwal, V., Tang, H., Wu, D., Berry, B., Srivastava, B., Sheth, A., & Irvin, M. J. (2021) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3459990.3465175 · Cited by 19
Child-Centred AI Design: Definition, Operation, and Considerations
Wang, G., Sun, K., Atabey, A., Pothong, K., Lin, G. C., Zhao, J., & Yip, J. (2023) · Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544549.3573821 · Cited by 16
“My Unconditional Homework Buddy:” Exploring Children’s Preferences for a Homework Companion Robot
Çağıltay, B., Mutlu, B., & Michaelis, J. E. (2023) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3585088.3589388 · Cited by 13
Embracing Diversity in the Learning Sciences
Kafai, Y. B., Sandoval, W. A., Enyedy, N., Nixon, A. S., & Herrera, F. (2012) · Routledge eBooks · doi:10.4324/9781410611017 · Cited by 12
Yalçın, Ö. N., Lallé, S., & Conati, C. (2022) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3490099.3511124 · Cited by 12
Ji, Y., & Gao, S. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.giscience.2023.43 · Cited by 10
Human-Computer Interaction and AI
Russell, D. M., Liao, Q. V., Kulkarni, C. E., Glassman, E. L., & Martelaro, N. (2023) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3544549.3574170 · Cited by 4
Research works printed but not linked: 17
- 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.
- Giorgio Franceschelli and Mirco Musolesi. 2023. On the creativity of large language models. arXiv preprint arXiv:2304.00008 (2023). · arXiv
- 10.1145/3313831.3376631 · DOI · Paper page
- 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).
- Mohammad Khalil and Erkan Er. 2023. Will ChatGPT get you caught? Rethinking of plagiarism detection. arXiv preprint arXiv:2302.04335 (2023). · arXiv
- 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
- Wander ly. 2023. Wanderly: Magical adventures for curious children. https://app.wander.ly/
- 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.
- 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).
- David R Thomas. 2006. A general inductive approach for analyzing qualitative evaluation data. American journal of evaluation 27, 2 (2006), 237–246.
- Levent Uzun. 2023. ChatGPT and academic integrity concerns: Detecting artificial intelligence generated content. Language Education and Technology 3, 1 (2023).
- 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.
- 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
- 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
- 10.1145/3411764.3445271 · DOI
- 10.1145/3411764.3445271 · DOI
- 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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Yim, I. H. Y. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100319 · Cited by 240
Yim, I. H. Y., & Wegerif, R. (2024) · Future in Educational Research · doi:10.1002/fer3.65 · Cited by 130
Generative AI use in K-12 education: a systematic review
Alfarwan, A. M. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1647573 · Cited by 162
Hu, L., Wang, H., & Xin, Y. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13353-7 · Cited by 77
“Several studies have explored teachers’ intention to use GAI-assisted instruction and the factors that influence it (Han et al., 2024; Lu et al., 2024; Moorhouse, 2024).”
Generative AI and the future of connectivist learning in higher education
Liang, E. S., & Bai, S. (2025) · Journal of Asian Public Policy · doi:10.1080/17516234.2024.2386085 · Cited by 34
“These capabilities allow GenAI to create new social interactions and provide learners with new ways to access and interact with diverse sources of information (Han et al., 2024).”
Influential
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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