Emotional AI in education and toys: Investigating moral risk awareness in the acceptance of AI technologies from a cross-sectional survey of the Japanese population
Open accessAbstract
Emotional artificial intelligence (AI), i.e., affective computing technologies, is rapidly reshaping the education of young minds worldwide. In Japan, government and commercial stakeholders are promulgating emotional AI not only as a neoliberal, cost-saving benefit but also as a heuristic that can improve the learning experience at home and in the classroom. Nevertheless, critics warn of a myriad of risks and harms posed by the technology such as privacy violation, unresolved deeper cultural and systemic issues, machinic parentalism as well as the danger of imposing attitudinal conformity. This study brings together the Technological Acceptance Model and Moral Foundation Theory to examine the cultural construal of risks and rewards regarding the application of emotional AI technologies. It explores Japanese citizens' perceptions of emotional AI in education and children's toys via analysis of a final sample of 2000 Japanese respondents with five age groups (20s–60s) and two sexes equally represented. The linear regression models for determinants of attitude toward emotional AI in education and in toys account for 44 % and 38 % variation in the data, respectively. The analyses reveal a significant negative correlation between attitudes toward emotional AI in both schools and toys and concerns about privacy violations or the dystopian nature of constantly monitoring of children and students' emotions with AI (Education: β DystopianConcern = − .094***; Toys: β PrivacyConcern = − .199***). However, worries about autonomy and bias show mixed results, which hints at certain cultural nuances of values in a Japanese context and how new the technologies are. Concurring with the empirical literature on the Moral Foundation Theory, the chi-square (Χ 2 ) test shows Japanese female respondents express more fear regarding the potential harms of emotional AI technologies for the youth's privacy, autonomy, data misuse, and fairness (p < 0.001). The policy implications of these results and insights on the impacts of emotional AI for the future of human-machine interaction are also provided.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
This lack of transparency inhibits informed consent and amplifies the potential for unintended relational harm, particularly among vulnerable populations.
“This lack of transparency inhibits informed consent and amplifies the potential for unintended relational harm, particularly among vulnerable populations”
From Toward an Ethic of Synthetic Relationality: Identity, Intimacy, and Risk in AI-Mediated Roleplay Environments (2025) · cites this paper as Ho, Mantello, and Vuong 2024
The Results (Emotional AI in children's toys; page unavailable) say parents may lack the literacy to assess opaque data practices and call for protecting vulnerable children from possible harms. The Discussion (Policy implications; page unavailable) warns that smart toys may worsen psychological tendencies in at-risk children. These passages support concerns about transparency and harm, but do not establish that a lack of transparency inhibits informed consent or specifically amplifies relational harm. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483 · see citing works
Graham, J., Nosek, B. A., Haidt, J., Iyer, R., Koleva, S., & Ditto, P. H. (2011) · Journal of Personality and Social Psychology · doi:10.1037/a0021847 · Cited by 2,666 · see citing works
The New Synthesis in Moral Psychology
Haidt, J. (2007) · Science · doi:10.1126/science.1137651 · Cited by 2,396 · see citing works
Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems
Mark, G., Fussell, S. R., Lampe, C., Schraefel, M., Hourcade, J., Appert, C., & Wigdor, D. J. (2017) · International Conference on Human Factors in Computing Systems · doi:10.1145/3025453 · Cited by 2,222 · see citing works
Scherer, R., Siddiq, F., & Tondeur, J. (2019) · Computers & Education · doi:10.1016/j.compedu.2018.09.009 · Cited by 1,923 · see citing works
Applications of structural equation modeling (SEM) in ecological studies: an updated review
Fan, Y., Chen, J., Shirkey, G., John, R., Wu, S. R., Park, H., & Shao, C. (2016) · Ecological Processes · doi:10.1186/s13717-016-0063-3 · Cited by 1,523 · see citing works
A review of technology acceptance and adoption models and theories
Taherdoost, H. (2018) · Procedia Manufacturing · doi:10.1016/j.promfg.2018.03.137 · Cited by 1,162 · see citing works
Kamal, S. A., Shafiq, M., & Kakria, P. (2019) · Technology in Society · doi:10.1016/j.techsoc.2019.101212 · Cited by 959 · see citing works
Proceedings of the 56th Hawaii International Conference on System Sciences
(2023) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2023.000 · Cited by 754 · see citing works
Acculturation and global mindsponge: An emerging market perspective
Vuong, Q., & Napier, N. K. (2015) · International Journal of Intercultural Relations · doi:10.1016/j.ijintrel.2015.06.003 · Cited by 607 · see citing works
Self-citationVuong, Q., Bui, Q.-K., La, V., Vuong, T., Nguyen, V.-H. T., Ho, M., Nguyen, H. T., & Ho, T. M. (2018) · Humanities and Social Sciences Communications · doi:10.1057/s41599-018-0189-2 · Cited by 469 · see citing works
Self-citationEmotional AI: The Rise of Empathic Media
McStay, A. (2018) · doi:10.4135/9781526451293 · Cited by 385 · see citing works
McClure, P. K. (2018) · Social Science Computer Review · doi:10.1177/0894439317698637 · Cited by 367 · see citing works
Connecting with Generation Z: Approaches in Nursing Education
Chicca, J., & Shellenbarger, T. (2018) · Teaching and learning in nursing · doi:10.1016/j.teln.2018.03.008 · Cited by 332 · see citing works
Lew, S., Tan, G. W., Loh, X.-M., Hew, J.-J., & Ooi, K. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101430 · Cited by 305 · see citing works
DITTO for Gen Z: A framework for leveraging the uniqueness of the new generation
Pichler, S. M., Kohli, C. S., & Granitz, N. A. (2021) · Business Horizons · doi:10.1016/j.bushor.2021.02.021 · Cited by 222 · see citing works
McStay, A. (2020) · Big Data & Society · doi:10.1177/2053951720904386 · Cited by 217 · see citing works
Technological innovation and building a ‘super smart’ society: Japan’s vision of society 5.0
Holroyd, C. (2020) · Journal of Asian Public Policy · doi:10.1080/17516234.2020.1749340 · Cited by 182 · see citing works
Decoding ClassDojo: psycho-policy, social-emotional learning and persuasive educational technologies
Williamson, B. (2017) · Learning Media and Technology · doi:10.1080/17439884.2017.1278020 · Cited by 169 · see citing works
Mantello, P., Ho, M.-T., Nguyen, M.-H., & Vuong, Q. (2021) · Ai & Society · doi:10.1007/s00146-021-01290-1 · Cited by 159 · see citing works
Self-citationAttitudes towards artificial intelligence at work: Scale development and validation
Park, J., Woo, S. E., & Kim, J. (2024) · Journal of Occupational and Organizational Psychology · doi:10.1111/joop.12502 · Cited by 150 · see citing works
Sex differences in moral judgements across 67 countries
Atari, M., Lai, M. H. C., & Dehghani, M. (2020) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2020.1201 · Cited by 145 · see citing works
Ho, T. M., Mantello, P. A., Ghotbi, N., Nguyen, M., Nguyen, H. T., & Vuong, Q. (2022) · Technology in Society · doi:10.1016/j.techsoc.2022.102011 · Cited by 136 · see citing works
Self-citationDesigning trust: The formation of employees’ trust in conversational AI in the digital workplace
Gkinko, L., & Elbanna, A. R. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2023.113707 · Cited by 136 · see citing works
Vu, H. T., & Lim, J. (2021) · Behaviour and Information Technology · doi:10.1080/0144929x.2021.1884288 · Cited by 135 · see citing works
A Monitoring System for Patients of Autism Spectrum Disorder Using Artificial Intelligence
Banna, M. H. A., Ghosh, T., Taher, K. A., Kaiser, M. S., & Mahmud, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59277-6_23 · Cited by 128 · see citing works
Emotional AI and EdTech: serving the public good?
McStay, A. (2020) · Journal of Educational Media · doi:10.1080/17439884.2020.1686016 · Cited by 113 · see citing works
Williamson, B. (2019) · Journal of Education Policy · doi:10.1080/02680939.2019.1672895 · Cited by 103 · see citing works
Ali, J. (2012) · Journal of Agricultural & Food Information · doi:10.1080/10496505.2012.636980 · Cited by 95 · see citing works
McStay, A., & Rosner, G. L. (2021) · Big Data & Society · doi:10.1177/2053951721994877 · Cited by 95 · see citing works
Ho, T. M., Le, N.-T. B., Mantello, P. A., Ho, M., & Ghotbi, N. (2022) · Technology in Society · doi:10.1016/j.techsoc.2022.102166 · Cited by 87 · see citing works
Self-citationImproving Bayesian statistics understanding in the age of Big Data with the bayesvl R package
Vuong, Q., La, V., Nguyen, M., Ho, M., Ho, T. M., & Mantello, P. A. (2020) · Software Impacts · doi:10.1016/j.simpa.2020.100016 · Cited by 86 · see citing works
Self-citationMantello, P. A., Ho, T. M., Nguyen, M., & Vuong, Q. (2023) · Humanities and Social Sciences Communications · doi:10.1057/s41599-023-01837-1 · Cited by 84 · see citing works
Self-citationEmotional AI and the future of wellbeing in the post-pandemic workplace
Mantello, P. A., & Ho, T. M. (2023) · AI & Society · doi:10.1007/s00146-023-01639-8 · Cited by 79 · see citing works
Self-citationFive premises to understand human–computer interactions as AI is changing the world
Ho, T. M., & Vuong, Q. (2024) · AI & Society · doi:10.1007/s00146-024-01913-3 · Cited by 76 · see citing works
Self-citationTime to regulate AI that interprets human emotions
Crawford, K. (2021) · Nature · doi:10.1038/d41586-021-00868-5 · Cited by 69 · see citing works
An analytical framework for studying attitude towards emotional AI: The three-pronged approach
Ho, T. M., Mantello, P. A., & Ho, M. (2023) · MethodsX · doi:10.1016/j.mex.2023.102149 · Cited by 64 · see citing works
Self-citationFosch-Villaronga, E., van der Hof, S., Lutz, C., & Tamò-Larrieux, A. (2023) · AI & SOCIETY · doi:10.1007/s00146-021-01295-w · Cited by 54 · see citing works
Matsushita, M., & Yamamura, S. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.775522 · Cited by 49 · see citing works
We have to talk about emotional AI and crime
Podoletz, L. (2022) · AI & Society · doi:10.1007/s00146-022-01435-w · Cited by 45 · see citing works
Combating Loneliness with Artificial Intelligence: An AI-Based Emotional Support Model
Sullivan, Y., Nyawa, S., & Fosso Wamba, S. (2023) · Proceedings of the Annual Hawaii International Conference on System Sciences · doi:10.24251/hicss.2023.541 · Cited by 44 · see citing works
Affective computing scholarship and the rise of China: a view from 25 years of bibliometric data
Ho, T. M., Mantello, P. A., Nguyen, H. T., & Vuong, Q. (2021) · Humanities and Social Sciences Communications · doi:10.1057/s41599-021-00959-8 · Cited by 44 · see citing works
Self-citationPolicing faces: the present and future of intelligent facial surveillance
Urquhart, L., & Miranda, D. (2021) · Information & Communications Technology Law · doi:10.1080/13600834.2021.1994220 · Cited by 42 · see citing works
In cars (are we really safest of all?): interior sensing and emotional opacity
McStay, A., & Urquhart, L. (2022) · International Review of Law Computers & Technology · doi:10.1080/13600869.2021.2009181 · Cited by 41 · see citing works
Human-centric data protection laws and policies: A lesson from Japan
Miyashita, H. (2020) · Computer law & security review · doi:10.1016/j.clsr.2020.105487 · Cited by 35 · see citing works
Beeler, L. L., Zablah, A. R., & Rapp, A. A. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.04.045 · Cited by 34 · see citing works
Emotional AI, Ethics, and Japanese Spice: Contributing Community, Wholeness, Sincerity, and Heart
McStay, A. (2021) · Philosophy & Technology · doi:10.1007/s13347-021-00487-y · Cited by 32 · see citing works
Urueña, A., Arenas, A. E., & Nuchera, A. H. (2018) · Economic Research-Ekonomska Istraživanja · doi:10.1080/1331677x.2018.1436451 · Cited by 31 · see citing works
Perception of New Technologies: Constructing an Innovation Openness Index
Pishnyak, A. I., & Вячеславовна, Х. Н. (2021) · Foresight-Russia · doi:10.17323/2500-2597.2021.1.39.54 · Cited by 27 · see citing works
1,2,3,4 tell me how to grow more: A position paper on children, design ethics and biowearables
Antle, A. N., & Kitson, A. (2021) · International Journal of Child-Computer Interaction · doi:10.1016/j.ijcci.2021.100328 · Cited by 26 · see citing works
How Humans Judge MachinesPreprint
Hidalgo, C. A., Orghian, D., Albó-Canals, J., de Almeida, F., & Martin, N. (2021) · CrimRxiv · doi:10.21428/cb6ab371.a6bc9cb4 · Cited by 24 · see citing works
Identification of Video Game Addiction Using Heart-Rate Variability Parameters
Kim, J.-Y., Kim, H.-S., Kim, D. J., Im, S., & Kim, M. S. (2021) · Sensors · doi:10.3390/s21144683 · Cited by 23 · see citing works
Changes in Computer-Analyzed Facial Expressions with Age
Ko, H., Kim, K.-S., Bae, M., Seo, M.-G., Nam, G., Park, S., Park, S., Ihm, J., & Lee, J. (2021) · Sensors · doi:10.3390/s21144858 · Cited by 22 · see citing works
Privacy Preservation Framework for Smart Connected Toys
Yankson, B., Iqbal, F., & Hung, P. C. K. (2017) · International series on computer entertainment and media technology · doi:10.1007/978-3-319-62072-5_9 · Cited by 20 · see citing works
Martín-Ruiz, M.-L., Fernández-Aller, C., Aldana, E. P., Hernández, J. M., & del Barrio, C. (2017) · Science and Engineering Ethics · doi:10.1007/s11948-017-9951-x · Cited by 18 · see citing works
Vyatkin, A. V., Fomina, L. V., & Шмелева, Ж. Н. (2020) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/421/3/032037 · Cited by 16 · see citing works
Ethical Risk Assessment for Social Robots: Case Studies in Smart Robot Toys
Winfield, A. F. T., van Maris, A., Winkle, K., Jirotka, M. D., Salvini, P., Webb, H., Scott, A. S., Freeman, J. L., Kunze, L., Slovák, P., et al. (2022) · International series on intelligent systems, control and automation: science and engineering · doi:10.1007/978-3-031-09823-9_4 · Cited by 15 · see citing works
Towards Trustworthy Artificial Intelligent Systems
(2022) · International series on intelligent systems, control and automation: science and engineering · doi:10.1007/978-3-031-09823-9 · Cited by 10 · see citing works
Ho, T. M., & Nguyen, H. T. (2023) · AI & Society · doi:10.1007/s00146-023-01737-7 · Cited by 10 · see citing works
Homogenous Japan? An Empirical Examination on Public Perceptions of Citizenship
Woo, Y. (2022) · Social Science Japan Journal · doi:10.1093/ssjj/jyac001 · Cited by 9 · see citing works
Losing the information war to adversarial AI
Mantello, P. A., & Ho, T. M. (2023) · AI & Society · doi:10.1007/s00146-023-01674-5 · Cited by 9 · see citing works
Self-citationKucirkova, N., Toda, Y., & Flewitt, R. (2020) · Technology, Knowledge and Learning · doi:10.1007/s10758-020-09465-3 · Cited by 7 · see citing works
Urquhart, L., Laffer, A., & Miranda, D. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2205.08264 · Cited by 3 · see citing works
Disengage to survive the AI-powered sensory overload world
Ho, T. M., & Vuong, Q. (2023) · AI & Society · doi:10.1007/s00146-023-01714-0 · Cited by 3 · see citing works
Self-citation
Flagged first, then by citations
2 of the 66 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- [35]Abe K. Iwata S. NEC's Emotion Analysis Solution Supports Work Style Reform and Health Management 2022 NEC Technical Journal 2019 190109
Cited by
Sorted by citations
Innovating responsibly: ethical considerations for AI in early childhood education
Berson, I. R., Berson, M. J., & Luo, W. (2025) · AI Brain and Child · doi:10.1007/s44436-025-00003-5 · Cited by 122 · see citing works
Shahzad, M. F., Xu, S., Liu, H., & Zahid, H. (2024) · European Journal of Education · doi:10.1111/ejed.12835 · Cited by 64 · see citing works
Cognitive bias in generative AI influences religious education
Zhang, J., Song, W., & Liu, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-99121-6 · Cited by 63 · see citing works
Emotional Artificial Intelligence in Education: A Systematic Review and Meta-Analysis
Zhang, H., Liu, Y., Jiang, M., Chen, J., Wang, M., & PAAS, F. G. W. C. (2025) · Educational Psychology Review · doi:10.1007/s10648-025-10086-4 · Cited by 53 · see citing works
Hui-li, Y. (2025) · Learning and Motivation · doi:10.1016/j.lmot.2025.102136 · Cited by 49 · see citing works
Hui-li, Y., & Yan-ping, R. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.105104 · Cited by 44 · see citing works
The perceived impact of artificial intelligence on academic learning
Dogaru, M., Pisică, O., Popa, C.-Ș., Răgman, A.-A., & Tololoi, I.-R. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1611183 · Cited by 34 · see citing works
Nurdi, M. S., & Ahmad, A. B. (2025) · Attadrib Jurnal Pendidikan Guru Madrasah Ibtidaiyah · doi:10.54069/attadrib.v8i1.896 · Cited by 31 · see citing works
Smoła, P., Młoźniak, I., Wojcieszko, M., Zwierczyk, U., Kobryn, M., Rzepecka, E., & Duplaga, M. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1458685 · Cited by 28 · see citing works
AI teachers (AI-based robots as teachers): history, potential, concerns and recommendations
Malik, M. A., & Shah, R. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1541543 · Cited by 19 · see citing works
Cheng, S., Cao, R., & Rashid, S. M. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-05872-y · Cited by 18 · see citing works
Fangni, L. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2025.1565869 · Cited by 17 · see citing works
Sacco, F., Manzi, F., Dio, C. D., Schepman, A., Rodway, P., Massaro, D., & Marchetti, A. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100751 · Cited by 13 · see citing works
Samadhiya, A., Kumar, A., V, Mulat-Weldemeskel, E., Luthra, S., Garza‐Reyes, J. A., & Yadav, S. (2025) · The TQM Journal · doi:10.1108/tqm-11-2024-0441 · Cited by 11 · see citing works
Hou, Z., Chen, J., & Guo, H. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2025.1754139 · Cited by 8 · see citing works
Hadi, A. F. M. Q. A., Jinan, M., Maksum, M. N. R., & Ibrahim, S. A. S. I. S. A. S. (2025) · ICEETE Conference Series · doi:10.36728/iceete.v3i1.244 · Cited by 6 · see citing works
State-of-the-arts methods for studying factors driving the utilization of open science resources
Le, T., Thu, H. N. T., Minh, C. L., Dinh, N. V., Linh, H. K., Trinh, D. T., Tram, N. T. P., Nguyen, H., & Ho, T. M. (2024) · MethodsX · doi:10.1016/j.mex.2024.103125 · Cited by 6 · see citing works
Bhat, M. (2025) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1609/aies.v8i1.36560 · 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
Regulating Emotion AI in the United States: Insights from Empirical Inquiry
Ingber, A. S., & Andalibi, N. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732014 · Cited by 5 · see citing works
Will the Application of AI Technology in Higher Education Exacerbate Teacher Burnout?
Zhang, Y., & Xiong, P. (2025) · International Journal of High Speed Electronics and Systems · doi:10.1142/s0129156425408381 · Cited by 4 · see citing works
Artificial Intelligence in Education Challenges and Solutions Through the Eyes of Students
Esfijani, A., Nili, M., & Kazemi, M. S. (2025) · International and National Conference on E-Learning and E-Teaching (ICeLeT) · doi:10.1109/icelet66022.2025.11025304 · Cited by 3 · see citing works
Li, Z., Lyu, M., Li, L., & Huang, J. (2025) · Information Communication & Society · doi:10.1080/1369118x.2025.2565359 · Cited by 3 · see citing works
Incorporation of Artificial Intelligence: Exploring Readiness Factors at School Level
Angadi, G. R., & Karan, B. (2026) · Bulletin of Science Technology & Society · doi:10.1177/02704676251407967 · Cited by 3 · see citing works
Ma, X., Xu, Q. H., & Li, L. (2025) · Algorithms · doi:10.3390/a18080506 · Cited by 2 · see citing works
Yakin, A. M. A., Muthmainnah, M., Asad, M. M., & Almusharraf, N. (2026) · International Journal of Sociology and Social Policy · doi:10.1108/ijssp-08-2025-0566 · Cited by 2 · see citing works
Luu, P. T., Ho, M.-T., Nguyen, H. T., Nguyen, D., Le, N.-T. B., & Pham, H. (2026) · European Journal of Education · doi:10.1111/ejed.70487 · Cited by 2 · see citing works
Self-citationGarcia, M. B. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2658204 · Cited by 1 · see citing works
AHLUSSUNNAH WAL JAMA'AH LEADERSHIP IN ISLAMIC EDUCATION DEVELOPMENT
Subaidi, S., Wahab, A., Sukarman, S., Isrohmawati, I., & Haq, M. D. (2025) · PEDAGOGIK Jurnal Pendidikan · doi:10.33650/pjp.v12i1.10487 · Cited by 1 · see citing works
Digital Innovations in the Educational Process at the Present Stage
Chernousova, K. S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-99598-9_28
AI-driven nudges: unpacking and challenging the issue of political opacity
Calboli, S. (2025) · AI & Society · doi:10.1007/s00146-025-02657-4
Use of Artificial Intelligence in Early Childhood Education
Aydos, E. H., & Güder, S. Y. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7574-8.ch005
Nurtanto, M., Hamid, M. A., Wahab, A. A., Kholifah, N., Gustiawan, W., & Subakti, H. (2025) · International Conference on Emerging Technologies · doi:10.1109/icet67257.2025.11291059
Ham, S., & Maeng, K. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2594144
Hoang, V. N., & Tsholofelo, E. (2026) · Journal of Health Innovation and Environmental Education. · doi:10.37251/jhiee.v3i1.2875
Bulusan, F. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01124-3
Muhammad, S., Al-Amin, M., Nahid, M. A. R., Rahman, M., Talapatra, S., Kamal, M. A. S., Tahiduzzaman, M., & Yamada, K. (2026) · Benchmarking An International Journal · doi:10.1108/bij-02-2026-0138
Strengthening Digital Literacy in Elementary Schools to Prepare Students for the Society 5.0 Era
Liafitri, S., Maharani, N. A. K., Handayani, T. N., & Purwanti, K. Y. (2026) · Proceeding International Collaborative Conference on Multidisciplinary Science · doi:10.70062/iccms.v3i1.249
Ali, Y., Gajewski, M., Nowakowski, P. T., Colman, R. D., Poudel, D. B., Aledeh, M., & Kotera, Y. (2026) · Education Innovations Systems and Future Learning · doi:10.1108/eisfl-04-2026-0065
Nguyen, D., Ho, M.-T., Pham, T. A., Ngo, D.-T., & Nguyen, H. T. (2026) · AI & Society · doi:10.1007/s00146-026-03311-3
Self-citationHo, T. M., Luong, M.-V., Tran, T.-K., Phan, T.-H., Nguyen, H. T., Ghotbi, N., Pham, H., & Ho, M. (2026) · Societal Impacts · doi:10.1016/j.socimp.2026.100209
Self-citationJoseph, F. V., Bernardshaw, J. G., & Palanisamy, C. (2026) · RESEARCH REVIEW International Journal of Multidisciplinary · doi:10.31305/rrijm.2026.v11.n09.005
Fuentes-Suarez, T. M., & Palacio, L. F. C. (2026) · Portal de la Ciencia · doi:10.51247/pdlc.v7i4.1031
- Authors
- Ho, M.-T., Mantello, P., & Vuong, Q.-H.
- Affiliations
- Ritsumeikan Asia Pacific University (JP)
- Vietnam Academy of Social Sciences (VN)
- Venue
- Heliyon
- Publisher
- Elsevier BV
- Published
- 2024-08
- Volume
- 10
- Issue
- 16
- Pages
- e36251
- ISSN
- 2405-8440 (ISSN-L)
- PMC
- PMC11382058
- OpenAlex
- W4401537241
- Cited by
- 37
- References
- 66
- Citations by year
- 2026: 13 · 2025: 22 · 2024: 2
- FWCI
- 15.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Heliyon (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Japan Science and Technology Agency
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- emotional artificial intelligence, affective computing, technology acceptance, technology acceptance model, toys, education, privacy concerns, autonomy, algorithmic bias, gender differences, human-machine interaction, parentalism
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 ›
Are you an author, or do you see a problem with this page? Request a review
