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 introducing conversational AI into classrooms and homes to generate teaching materials, orchestrate chained questioning, co-author stories, and automate grading.
“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]
GenAI facilitates intercultural competence by incorporating diverse cultural contents into language learning.
“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
GenAI can serve as culturally intelligent tools.
“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
A parent mentioned using GenAI to generate content about traditional Asian dragons to teach her child about their cultural heritage.
“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
GenAI can provide culturally relevant examples and feedback.
“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
GenAI can create culturally salient stories, like fables, which resonate with a learner's heritage.
“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
Two reviewed studies [10,24] explicitly suggest that when AI systems freely generate content, students may rely on AI for the entire output, completing work with minimal engagement in the creative process.
“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]
Other reviewed papers discussed that misinformation or biased content can emerge unpredictably, especially when children rely heavily on generative models without understanding their underlying data sources.
“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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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 · see citing works
“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 · see citing works
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 · see citing works
Yalçın, Ö. N., Lallé, S., & Conati, C. (2022) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3490099.3511124 · Cited by 12 · see citing works
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 · see citing works
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Yim, I. H. Y. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100319 · Cited by 198 · see citing works
Yim, I. H. Y., & Wegerif, R. (2024) · Future in Educational Research · doi:10.1002/fer3.65 · Cited by 90 · see citing works
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 131 · see citing works
Hu, L., Wang, H., & Xin, Y. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13353-7 · Cited by 77 · see citing works
“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 · see citing works
“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).”
InfluentialCai, Z., Park, S., Nixon, N., & Doroudi, S. (2024) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3650868 · Cited by 32 · see citing works
Self-citationZhang, S., Wang, H., & Yi, X. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757594 · Cited by 36 · see citing works
“Berge et al. [18] emphasize semi-proactive workflow integration to balance AI decision-support with documentation demands.”
Harvey, E., Koenecke, A., & Kizilcec, R. F. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713210 · Cited by 37 · see citing works
Do, T. D., Shafqat, U. B., Ling, E., & Sarda, N. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713460 · Cited by 29 · see citing works
Alotaibi, S. M. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2492785 · Cited by 40 · see citing works
“Furthermore, AI-driven tutoring systems like Socratic by Google, ChatGPT, and Wolfram Alpha assist students in compre-hending complex academic concepts by offering thorough explanations rather than just answers (Han et al., 2024).”
Generative AI in Language Education: Bridging Divide and Fostering Inclusivity
Weng, Z., & Fu, Y. (2025) · International Journal of Technology in Education · doi:10.46328/ijte.1056 · Cited by 31 · see citing works
“Five studies demonstrate that the use of GenAI can enhance language learners’ understanding of diverse cultures (e.g., Han et al., 2024; Karataş et al., 2024; Mabuan, 2024; Liu, Davin, & Ma, 2024) and reduce their biases in language learning, such as native-speakerism (e.g., Lee et al., 2024).”
Critical AI literacy for applied linguistics and language education students
Pérez-Paredes, P., Curry, N., & Ordoñana-Guillamón, C. (2025) · Journal of China Computer-Assisted Language Learning · doi:10.1515/jccall-2025-0005 · Cited by 30 · see citing works
Lin, X., & Tan, H. (2025) · Systems · doi:10.3390/systems13100840 · Cited by 22 · see citing works
Co-creating stories with generative AI
Cheung, L. M. E., & Shi, H. (2024) · Australian Review of Applied Linguistics · doi:10.1075/aral.24101.che · Cited by 26 · see citing works
Generative artificial intelligence in fifth-generation education systems: A systematic review
Chakraborty, S. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114463 · Cited by 50 · see citing works
“…Grading Tools (e.g., OpenAI’s Codex) Automated grading of assignments, Instant feedback on student performance, Customized study plans [21], [35] Language Learning Transformer-based Language Models (e.g., BERT) Interactive language exercises, Personalized language learning paths [32], [63]…”
GenAI and misinformation in education: a systematic scoping review of opportunities and challenges
Fulsher, A., Pagkratidou, M., & Kendeou, P. (2025) · AI & Society · doi:10.1007/s00146-025-02536-y · Cited by 25 · see citing works
“ChatGPT version 3.5 was used across these studies, suggesting that more advanced models or additional fine-tuning may improve reliability—though these solutions can create new problems around equitable access to GenAI (Capraro et al. 2024; Han et al. 2024).”
InfluentialAgency in the age of generative AI: a critical review of educational implications
Roe, J., & Perkins, M. (2026) · Academia AI and Applications · doi:10.20935/acadai8422 · Cited by 62 · see citing works
Prompt Machine: A Tangible Generative AI Tool for Supporting Children's Learning and Literacy
Lindrup, M. V. A., Jacobsen, R. M., Wester, J., van Berkel, N., Raptis, D., & Nielsen, P. A. (2025) · Designing Interactive Systems Conference · doi:10.1145/3715336.3735673 · Cited by 17 · see citing works
“People’s perceptions of AI technologies within educational contexts are critical to consider when designing not only the technology itself but also when designing the use contexts for AI [34].”
Cai, Z., Han, A., Zhou, X., Gazulla, E. D., & Peppler, K. (2025) · Proceedings of the 24th Interaction Design and Children · doi:10.1145/3713043.3731506 · Cited by 17 · see citing works
Self-citationExamining Student and Teacher Perspectives on Undisclosed Use of Generative AI in Academic Work
Adnin, R., Pandkar, A., Yao, B., Wang, D., & Das, M. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713393 · Cited by 16 · see citing works
“Additionally, researchers and practitioners have focused on building GenAI-powered applications to assist teachers in preparing lesson plans and course materials [32, 40, 58, 81], such as Khanmigo [66] and Magicschool.ai [84].”
InfluentialParents’ Perceptions About the Use of Generative AI Systems by Adolescents
Eira, M., Rasouli, A., & Charisi, V. (2025) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3713043.3731508 · Cited by 16 · see citing works
“GenAI tools can provide timely and contextually relevant feedback, which broadens ideation for tasks such as writing [11]; however, there are challenges regarding authenticity [12], cultivation of collective novelty [28] and the generation of ideas beyond what is generated by GenAI [11].”
Sakowicz, M., & Hamidi, F. (2025) · American Journal of STEM Education · doi:10.32674/yeyaaq64 · Cited by 29 · see citing works
Yadav, A. K., Lachney, M., Hu, A., & Tavernier, L. (2025) · Journal of Research in Childhood Education · doi:10.1080/02568543.2025.2581712 · Cited by 13 · see citing works
“In their interviews with teachers about GenAI and literacy education, Han et al. (2024) found that some teachers saw the integration of GenAI into their pedagogy as part of preparing students for a world saturated with technology and they all thought it could be used as part of adapting and…”
Middle school students’ perspectives on adopting generative AI in K-12 education
Adisa, I. O., & Adefisayo, A. O. (2025) · The Journal of Educational Research · doi:10.1080/00220671.2025.2510393 · Cited by 13 · see citing works
“While many students valued GenAI for its personalized support, efficiency, and accessibility, they also recognized that it lacked the relational, dynamic, and interactive qualities that make human collaboration meaningful (Han et al., 2024).”
Blundell, C. N., Mukherjee, M. M., & Nykvist, S. S. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13699-y · Cited by 19 · see citing works
“…2023) and when planning lessons and developing resources, including differentiating learning experiences and adjusting resources to match students’ needs (Bower et al., 2024; Galindo-Domín-guez et al., 2024; Han et al., 2024; Jauhiainen & Guerra, 2023; Kong & Yang, 2024 ; K.-S. Tang et al., 2024).”
Desafíos de la Educación para la Implementación de la Inteligencia Artificial
Inca, U. R. G., Ruano, A. C. B., Lozada, R. F. L., Llantui, M. D. C. R., & Bravo, R. B. P. (2024) · Ciencia Latina Revista Científica Multidisciplinar · doi:10.37811/cl_rcm.v8i3.11576 · Cited by 10 · see citing works
Text Simplification for Children: Evaluating LLMs vis-à-vis Human Experts
Smirnova, A., Chun, K. B., Rothman, W. L., & Sarma, S. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719889 · Cited by 10 · see citing works
Cao, H. J., Choi, K., Park, C., & Lee, H. R. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713173 · Cited by 9 · see citing works
“Due to this reason, AI literacy education research for K-12 has emerged, where AI literacy refers to the cognitive competencies students need to understand AI concepts [50, 58, 74, 80, 108], use AI systems in daily settings [74, 81], and critically evaluate these systems [74, 81].”
The Future of Learning: Large Language Models through the Lens of Students
Zhang, H., Xie, J., Wu, C., Cai, J., Kim, C., & Carroll, J. M. (2024) · Conference on Information Technology Education · doi:10.1145/3686852.3687069 · Cited by 9 · see citing works
Morales‐Navarro, L., Noh, D. J., & Kafai, Y. B. (2025) · International Journal of Child-Computer Interaction · doi:10.1016/j.ijcci.2025.100769 · Cited by 9 · see citing works
Exploring students’ perceptions of generative AI-generated feedback in learning programming
Khor, E. T., Chan, L., Koh, E., & SEOW, P. S. K. (2025) · Educational Psychology · doi:10.1080/01443410.2025.2564894 · Cited by 9 · see citing works
“…studying students’ perceptions of adopting GenAI in education employs an undergraduate population (Ahmed et al., 2024; Almassaad et al., 2024; dube et al., 2024), while most research on this topic set in a K-12 context investigates educators’ perspectives (Cheah & Kim, 2025; Han et al., 2024).”
Empowering Children’s AI Literacy Through Co-Creating Stories with LLM
Han, A., & Han, S. (2025) · Proceedings of the 24th Interaction Design and Children · doi:10.1145/3713043.3731520 · Cited by 8 · see citing works
Self-citationA Systematic Review of Gen-AI Applications in Education: Rewards, Challenges and Future Prospects
Wanyonyi, E. N., & Murithi, M. (2025) · Pan-African journal of education and social sciences · doi:10.56893/pajes2025v06i01.01 · Cited by 7 · see citing works
Morales‐Navarro, L., Noh, D. J., & Kafai, Y. B. (2025) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3713043.3731525 · Cited by 7 · see citing works
Hohendanner, M., Ullstein, C., Onyekwelu, B. A., Katirai, A., Kuribayashi, J., Babalola, O., Ema, A., & Großklags, J. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714322 · Cited by 5 · see citing works
“…study [76, 169]; thematic analysis of on-line threats [125] perspectives of teachers, parents, and students on genAI for literacy education [75, 76]; knowledge workers on the impact of genAI in their respective context (advertising, business communications, education, journalism, law, mental…”
InfluentialTechnological Competence, Training and Support, Attitude Towards AI, and Teachers’ Acceptance
Silagan, B. L., & Tumapon, T. T. (2025) · Psychology and Education A Multidisciplinary Journal · doi:10.70838/pemj.360809 · Cited by 5 · see citing works
Güngör, İ. C., & Öztürk, T. (2025) · Recep Tayyip Erdoğan Üniversitesi Sosyal Bilimler Dergisi · doi:10.34086/rteusbe.1660052 · Cited by 5 · see citing works
Tang, Z. R., Kim, S., Wang, H., Xue, B., & Zhou, Y. (2026) · Computers & Education · doi:10.1016/j.compedu.2026.105592 · Cited by 5 · see citing works
A Systematic Literature Review of Generative AI Risks and Harms for Youth
Chang, T., & Razi, A. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3798673 · Cited by 5 · see citing works
Zhang, X., & Oubibi, M. (2025) · TechTrends · doi:10.1007/s11528-025-01104-x · Cited by 5 · see citing works
“Experts predict that GAI will gradually phase out the traditional classroom teaching system (Han et al., 2024).”
Jin, Q., & Yuan, Y. (2025) · International Journal of Child-Computer Interaction · doi:10.1016/j.ijcci.2025.100787 · Cited by 4 · see citing works
Hugerth, M. W., & Hugerth, L. W. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100531 · Cited by 6 · see citing works
Benner, D., Rauch, J., Janson, A., & Leimeister, J. (2025) · CHI Extended Abstracts · doi:10.1145/3706599.3719957 · Cited by 4 · see citing works
“Simultaneously, the advent of generative artificial intelligence (AI) has revolutionized the creation and distribution of educational content [13, 19].”
Aly, H., Volonte, M., Byrne, K. A., & Knijnenburg, B. P. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713570 · Cited by 7 · see citing works
Khan, N., Daud, H. E., & Shahid, S. (2024) · International Conference on Information and Communication Technologies and Development (ICTD) · doi:10.1145/3700794.3700813 · Cited by 3 · see citing works
“The design of educational interventions should promote student agency, while balancing the roles of AI and educators (including both teachers and parents) to foster deeper engagement in learning tasks [29].”
From ChatGPT to Custom GPTs: Scaffolding Conceptual Learning in Adult Education
Masalaci, Z., Shalawadi, S., & Papachristos, E. (2025) · International Conference on Mobile and Ubiquitous Multimedia (MUM) · doi:10.1145/3771882.3771907 · Cited by 3 · see citing works
Oguine, O. C., Garcia, A. A., Muller, M. J., & Badillo-Urquiola, K. A. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791346 · Cited by 4 · see citing works
“For example, integrating youth-led audits, reviews, and critical reflection exercises into school curricula could shift youth participation from peripheral, volunteer-like roles to structured, student-like contributions [46].”
"Did you sleep well?": A Multimodal Sleep Diary for Sustained Self-Reporting by Children
Chen, S., Hu, J., van Iterson, H. C., Fang, N., & Markopoulos, P. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713425 · Cited by 3 · see citing works
“Adopting this balanced approach can help mitigate risks associated with attachment and dependency [37].”
AIstory-bot: An AI-Based Digital Story Writing Platform for Children
Han, A., & Han, S. (2024) · XRDS Crossroads The ACM Magazine for Students · doi:10.1145/3688084 · Cited by 3 · see citing works
“However, a significant gap remains in understanding AI, specifically AI competencies among young learners [2].”
Self-citation"Ask Sir Oliver Ingham": LLM-based Social Simulations for History Education
Park, K., Song, H., Seo, S., Kim, J., & Suh, B. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719728 · Cited by 2 · see citing works
Shin, M., Choi, J., Song, H., Kim, J., & Suh, B. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719797 · Cited by 3 · see citing works
“VCA applications have expanded into specific domains including education [9] and healthcare [44].”
Design Heuristics for GenAI Educational Platforms
Owen, V., Parkman, S., Lindley, J., & Rubegni, E. (2025) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3713043.3731499 · Cited by 2 · see citing works
Automatic Modeling and Analysis of Students’ Problem-Solving Handwriting Trajectories
Sheng, Z., Shen, S., Shen, L., Duan, Q., Tang, N., Hui, P., Qu, H., & Luo, Y. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98414-3_16 · Cited by 2 · see citing works
Zhu, Y., & Li, Y. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2025.103654 · Cited by 5 · see citing works
Obstacles or Opportunities: Teachers' Concerns About Adopting Generative AI in Learning and Teaching
Yue, M., Jong, M. S., Chen, Y., & Jiang, M. Y. (2025) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt64023.2025.00084 · Cited by 2 · see citing works
“While GAI offers opportunities to foster a student-centered learning environment by addressing individual needs, it also risks undermining students’ agency and ownership over their learning processes [9].”
“Just Be Careful”: Exploring Middle School Students’ Views About Generative AI
Anderson, S. E., & Bauml, M. (2025) · RMLE Online · doi:10.1080/19404476.2025.2596551 · Cited by 2 · see citing works
“Consequently, the adoption of GenAI in education raises concerns about issues such as misinformation, bias, and learner agency (Han et al., 2024).”
Zhao, G. L., Yang, L., Wang, J., & Li, J. (2026) · Innovation in Language Learning and Teaching · doi:10.1080/17501229.2026.2615797 · Cited by 2 · see citing works
Pal, O., Duddu, V., Goyal, A., Goel, D., & Saha, K. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3799386 · Cited by 2 · see citing works
Si, J. J., Li, D., Wang, Q., Liu, A. W., Lin, X., Zhu, Y., Zhou, X., Wang, A. Y., & Wang, K. Y. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790366 · Cited by 3 · see citing works
Self-citationFrom Crafting Text to Crafting Thought: Grounding AI Writing Support to Writing Center Pedagogy
Liu, Y., Gallagher, J., Sterman, S., & August, T. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790292 · Cited by 2 · see citing works
“Building on this pedagogical approach, we propose design insights to a form of intelligent writing support that focuses on non-directive feedback buttressed by writing center strategies.”
Generative AI in Education: A Sentiment Analysis on Reddit Discourse
Kumarage, P. M., Gunasekara, S., & Saarela, M. (2026) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa67686.2026.11467743 · Cited by 2 · see citing works
“Limited evidence suggests many parents are unaware of their children’s GenAI use yet acknowledge both benefits and risks, with attitudes shaped by personal familiarity [16], [17].”
Co-designing Digital Humans for Online Learning: A Framework for Human-AI Pedagogical Integration
Lei, X., Pang, C. C., Jiang, Y., Tong, X., & Hui, P. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-30784-2_34 · Cited by 5 · see citing works
“%) 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 · see citing works
“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 · see citing works
Reimann, D. (2025) · Springer series on cultural computing · doi:10.1007/978-3-031-89037-6_6 · Cited by 1 · see citing works
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 · see citing works
“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 · see citing works
Andleeb, S., Kantorski, B., & Carver, J. C. (2025) · Conference on Information Technology Education · doi:10.1145/3769694.3771128 · Cited by 2 · see citing works
“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 · see citing works
“…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 1 · see citing works
“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 · see citing works
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 · see citing works
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 · see citing works
Jayathilake, H. M., & Ma, R. (2026) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3773077.3806126 · Cited by 1 · see citing works
“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 · see citing works
“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 1 · see citing works
Sakowicz, M., & Hamidi, F. (2026) · Universal Access in the Information Society · doi:10.1007/s10209-026-01370-3 · Cited by 2 · see citing works
“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
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
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., Akrong, D., Vardoulakis, L. M., Miller, N., Haramaty, Y., Belinsky, L., Hackmon, L., Tapia, A. O., Tootill, L., & Siebert, S. (2025) · Frontiers Artif. Intell. · doi:10.3389/frai.2026.1783117
“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., Jessica Vitak, B., Erete, S. L., Clegg, T., Freed, D., & Chetty, M. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · 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
“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
- 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
- Physical Sciences › Computer Science › Information Systems › 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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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.
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