The pervasive integration of Artificial Intelligence (AI) into the social and educational fabric of society has precipitated an urgent and non-negotiable need for AI literacy, a competency that must now be cultivated from the earliest years of schooling. While a burgeoning body of research has begun to demonstrate the feasibility, benefits, and methodologies for introducing AI concepts to young children, a profound and persistent barrier threatens to undermine this progress: a widespread lack of teacher readiness. This comprehensive conceptual paper addresses this critical impediment by proposing the AI-TPACK Model, a novel professional development framework that explicitly adapts and extends the Technological Pedagogical Content Knowledge (TPACK) model for the specific context of early childhood AI literacy. The AI-TPACK Model is then introduced as a multi-stage framework comprising four synergistic modules: (1) Foundational AI Concepts, (2) ECE-Appropriate AI Tools, (3) AI-Specific Pedagogy, and (4) Ethical Integration. The paper contributes to the field by providing a structured pathway for transforming ECE educators from apprehensive observers to confident facilitators of AI literacy, thereby ensuring that the integration of AI in early learning environments is both pedagogically sound and ethically grounded. This framework serves as a critical foundation for future research, policy development, and institutional implementation, positioning effective teacher preparation as the crucial linchpin for the responsible and equitable future of AI in early learning environments.
References
[1]
UNESCO (2019) Artificial Intelligence in Education: Challenges and Opportunities for Sustainable Development. UNESCO.
[2]
Zhang, K. and Aslan, A.B. (2021) AI Technologies for Education: Recent Research & Future Directions. ComputersandEducation: ArtificialIntelligence, 2, Article ID: 100025. https://doi.org/10.1016/j.caeai.2021.100025
[3]
Vuorikari, R., Punie, Y., Carretero Gomez, S. and Van den Brande, G. (2016) DigComp 2.0: The Digital Competence Framework for Citizens. Luxembourg: Publications Office of the European Union.
[4]
Ng, D.T.K., Leung, J.K.L., Chu, S.K.W. and Qiao, M.S. (2021) Conceptualizing AI Literacy: An Exploratory Review. ComputersandEducation: ArtificialIntelligence, 2, Article ID: 100041. https://doi.org/10.1016/j.caeai.2021.100041
[5]
Vartiainen, H., Tedre, M. and Valtonen, T. (2020) Learning Machine Learning with Very Young Children: Who Is Teaching Whom? International Journal of Child-ComputerInteraction, 25, Article ID: 100182. https://doi.org/10.1016/j.ijcci.2020.100182
[6]
Long, D. and Magerko, B. (2020) What Is AI Literacy? Competencies and Design Considerations. Proceedingsofthe 2020 CHIConferenceonHumanFactorsinComputingSystems, Honolulu, 25-30 April 2020, 1-16. https://doi.org/10.1145/3313831.3376727
[7]
Desimone, L.M. (2009) Improving Impact Studies of Teachers’ Professional Development: Toward Better Conceptualizations and Measures. EducationalResearcher, 38, 181-199. https://doi.org/10.3102/0013189x08331140
[8]
Plowman, L. and McPake, J. (2013) Seven Myths about Young Children and Technology. ChildhoodEducation, 89, 27-33. https://doi.org/10.1080/00094056.2013.757490
[9]
Yang, W. (2022) Artificial Intelligence Education for Young Children: Why, What, and How in Curriculum Design and Implementation. ComputersandEducation: ArtificialIntelligence, 3, Article ID: 100061. https://doi.org/10.1016/j.caeai.2022.100061
[10]
Resnick, M. (2017) Lifelong Kindergarten: Cultivating Creativity through Projects, Passion, Peers, and Play. MIT Press.
[11]
National Association for the Education of Young Children (2022) Developmentally Appropriate Practice in Early Childhood Programs Serving Children from Birth through Age 8. 4th Edition, NAEYC.
[12]
Su, J., Ng, D.T.K. and Chu, S.K.W. (2023) Artificial Intelligence (AI) Literacy in Early Childhood Education: The Challenges and Opportunities. ComputersandEducation: ArtificialIntelligence, 4, Article ID: 100124. https://doi.org/10.1016/j.caeai.2023.100124
[13]
Zawacki-Richter, O., Marín, V.I., Bond, M. and Gouverneur, F. (2019) Systematic Review of Research on Artificial Intelligence Applications in Higher Education—Where Are the Educators? InternationalJournalofEducationalTechnologyinHigherEducation, 16, Article No. 39. https://doi.org/10.1186/s41239-019-0171-0
[14]
Mishra, P. and Koehler, M.J. (2006) Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge. TeachersCollegeRecord: TheVoiceofScholarshipinEducation, 108, 1017-1054. https://doi.org/10.1177/016146810610800610
[15]
Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S. and Huber, P. (2016). Artificial Intelligence and Computer Science in Education: From Kindergarten to University. 2016 IEEEFrontiersinEducationConference (FIE), Erie, 12-15 October 2016, 1-5. https://doi.org/10.1109/fie.2016.7757570
[16]
Ng, D.T.K., Leung, J.K.L., Su, J., Ng, R.C.W. and Chu, S.K.W. (2023) AI Literacy in K-16 Classrooms. Springer.
[17]
Kewalramani, S., Kidman, G. and Palaiologou, I. (2021) Using Artificial Intelligence (AI)-Interfaced Robotic Toys in Early Childhood Settings: A Case for Children’s Inquiry Literacy. EuropeanEarlyChildhoodEducationResearchJournal, 29, 652-668. https://doi.org/10.1080/1350293x.2021.1968458
[18]
Zhang, T., Liu, X., Guo, Z. and Tian, Y. (2024) Adaptive Music Recommendation: Applying Machine Learning Algorithms Using Low Computing Device. Journal of SoftwareEngineeringandApplications, 17, 817-831. https://doi.org/10.4236/jsea.2024.1711045
[19]
Wei, Y., Gao, M., Xiao, J., Huang, B., Zhang, Y. and Tian, Y. (2025) Real-Time Traffic Sign Recognition Based on Threshold Segmentation and Neural Networks. 2025 10thInternationalConferenceonInformationandNetworkTechnologies (ICINT), Melbourne, 12-14 March 2025, 80-87. https://doi.org/10.1109/icint65528.2025.11030892
[20]
Touretzky, D., Gardner-McCune, C., Martin, F. and Seehorn, D. (2019) Envisioning AI for K-12: What Should Every Child Know about AI? ProceedingsoftheAAAIConferenceonArtificialIntelligence, 33, 9795-9799. https://doi.org/10.1609/aaai.v33i01.33019795
[21]
Williams, R., Park, H.W. and Breazeal, C. (2019) A Is for Artificial Intelligence: The Impact of Artificial Intelligence Activities on Young Children’s Perceptions of Robots. Proceedingsofthe 2019 CHIConferenceonHumanFactorsinComputingSystems, Glasgow, 4-9 May 2019, 1-11. https://doi.org/10.1145/3290605.3300677
[22]
Lin, P., Van Brummelen, J. and Breazeal, C. (2020) Zhorai: Designing a Conversational Agent for Children to Explore Machine Learning Concepts. Proceedings of the 2020 ACM Interaction Design and Children Conference, London, 21-24 June 2020, 399-411.
[23]
Zhang, T., Zhang, X., Liu, X., Guo, Z. and Tian, Y. (2024) Using Decision Tree Classification and Principal Component Analysis to Predict Ethnicity Based on Individual Characteristics: A Case Study of Assam and Bhutan Ethnicities. JournalofSoftwareEngineeringandApplications, 17, 833-850. https://doi.org/10.4236/jsea.2024.1712046
[24]
Druga, S. (2018) Cognimates: A Framework for AI Education for Families and Young Children. M.S. Thesis, MIT Media Lab.
[25]
Zhang, X., Liu, Y., Zhang, T., Hou, L., Liu, X., Guo, Z., et al. (2025) A BERT-LSTM-Attention Framework for Robust Multi-Class Sentiment Analysis on Twitter Data. Systems, 13, Article No. 964. https://doi.org/10.3390/systems13110964
[26]
Darling-Hammond, L., Hyler, M.E. and Gardner, M. (2017) Effective Teacher Professional Development. Learning Policy Institute.
[27]
Kanayo, N.A. and Tian, Y. (2025) Chinese Private Enterprises and Human Capital Development in Nigeria: Assessing the Educational and Skill Transfer Dimensions. ScientificAfrican, 28, e02699. https://doi.org/10.1016/j.sciaf.2025.e02699
[28]
Huang, B., Wei, Y., Xiao, J., Tian, Y., Zhang, Y. and Zheng, C. (2025) Proactive Reliability Governance in Complex Systems: Leveraging Pattern Mining for Scalable Solutions. 2025 10thInternationalConferenceonInformationandNetworkTechnologies (ICINT), Melbourne, 12-14 March 2025, 180-185. https://doi.org/10.1109/icint65528.2025.11030904
[29]
Ali, S., Payne, B.H., Williams, R., Park, H.W. and Breazeal, C. (2019) Constructionism, Ethics, and Creativity: Developing Primary and Middle School Artificial Intelligence Education. International Journal of Child-Computer Interaction, 22, 100-109.
[30]
Dong, B., Guo, Z., Mulat, A., Tian, Y., Lu, M., Yuan, Y., et al. (2024) Leveraging Heterogeneous Networks to Analyze Energy Storage Systems in Power Systems and Renewable Energy Research: A Scientometric Study. FrontiersinEnergyResearch, 12, Article ID: 1424928. https://doi.org/10.3389/fenrg.2024.1424928
[31]
Google Creative Lab (2023) Teachable Machine. https://teachablemachine.withgoogle.com/
[32]
Noble, S.U. (2018) Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.
[33]
Ribble, M. (2015) Digital Citizenship in Schools: Nine Elements All Students Should Know. International Society for Technology in Education.
[34]
Tschannen-Moran, M. and Hoy, A.W. (2001) Teacher Efficacy: Capturing an Elusive Construct. TeachingandTeacherEducation, 17, 783-805. https://doi.org/10.1016/s0742-051x(01)00036-1
[35]
Liu, X., Zhang, T., Zhang, X., Hou, L., Guo, Z., Tian, Y. and Liu, Y. (2025) Integrating Attention-Enhanced LSTM and Particle Swarm Optimization for Dynamic Pricing and Replenishment Strategies in Fresh Food Supermarkets.
[36]
Kumar, A., O’Shea, C.R., Yadav, V.K., Kandasamy, G., Moorthy, B.T., Ambrose, E.A., et al. (2025) Arginyltransferase1 Drives a Mitochondria-Dependent Program to Induce Cell Death. CellDeath&Disease, 16, Article No. 622. https://doi.org/10.1038/s41419-025-07917-1
[37]
Zhang, X., Zhang, T., Hou, L., Liu, X., Guo, Z., Tian, Y., et al. (2025) Data-Driven Loan Default Prediction: A Machine Learning Approach for Enhancing Business Process Management. Systems, 13, Article No. 581. https://doi.org/10.3390/systems13070581
[38]
Joo, Y.J., Park, S. and Lim, E. (2018) Factors Influencing Preservice Teachers’ Intention to Use Technology: TPACK, Teacher Self-Efficacy, and Technology Acceptance Model. Journal of Educational Technology & Society, 21, 48-59.
[39]
Chaudron, S., Di Gioia, R. and Gemo, M. (2018) Young Children (0-8) and Digital Technology: A Qualitative Study across Europe. Publications Office of the European Union.