Enhancing Data Literacy Competencies through the Application of Artificial Intelligence for Primary School Students

Authors

  • Sittichai Pomtong Bachelor of Science, Faculty of Business Administration and Information Technology, Nakhonratchasima College
  • Onnitcha Thossata Master of Education, Faculty of Education, Nakhonratchasima College
  • Sita Tubmongkhon Bachelor of Science, Faculty of Business Administration and Information Technology, Nakhonratchasima College

Keywords:

Competency Enhancement, Data Literacy, Application of Artificial Intelligence

Abstract

This study adopted a one-group pretest–posttest quasi-experimental design             (no control group) to develop and evaluate artificial-intelligence (AI)–integrated learning activities that enhance data literacy competencies among lower-secondary students. The objectives were to: (1) design AI-enabled learning activities that foster data-driven thinking; (2) compare students’ data-literacy performance before and after the intervention; and (3) examine learner satisfaction with the AI-based activities. The participants were 30 students from Ban Nong Ta Khong School, under Nakhon Ratchasima Primary Educational Service Area Office 1. A multi-stage sampling procedure was employed: the school was first selected by purposive sampling based on ICT infrastructure readiness; a classroom was then chosen via cluster random sampling; and all 30 students in that class were included. Research instruments were: (1) a 20-item data-literacy competency test scored on a five-point scale (max = 5) with item difficulty 0.25–0.78, discrimination 0.33–0.68, and KR-20 = .81; (2) a 20-item learner-satisfaction questionnaire with Cronbach’s  = .79; and (3) a semi-structured interview protocol with IOC = 0.67–1.00. Data were analyzed using descriptive statistics (mean, standard deviation) and inferential statistics (dependent/paired-samples t-test and Cohen’s d for effect size). Findings showed that: (1) three AI-enhanced learning activities were developed “Future Data Detectives,” “AI Fake-News Detector,” and “Headline-Only News Analyst” all aligned with a five-stage data-thinking process; expert review rated their appropriateness at a high level (equation= 3.90, S.D. = 0.57). (2) Posttest scores (equation= 3.88, S.D. = 0.31) were significantly higher than pretest scores (equation= 2.15, S.D. = 0.35) at the .01 level (t = 16.905, p < .01), indicating a very large effect (reported Cohen’s d = 5.33), evidencing strong practical significance of the intervention. (3) Learners’ satisfaction with the AI-enhanced learning activities was at a high level (equation= 4.29, S.D. = 0.60).

References

กัญชลารักษ์ ทีปกากร. (2568). การเรียนรู้กับ AI ในปี 2568: ครูไทยปรับตัวอย่างไรในยุค Generative AI [Thai teachers’ adaptation in the era of generative AI: Educational opportunities and challenges in 2025]. วารสารราชสีมาปริทัศน์, 1(1), 1–10.

https://so09.tci-thaijo.org/index.php/RSMP/issue/view/430/329

สถาบันส่งเสริมการสอนวิทยาศาสตร์และเทคโนโลยี. (2567). รายงานผลการประเมินสมรรถนะของนักเรียนไทยตามโครงการ PISA 2025 (ฉบับเบื้องต้น). สถาบันส่งเสริมการสอนวิทยาศาสตร์และเทคโนโลยี

Bandura, A. (1977). Social Learning Theory. Prentice Hall.

Chen, X., et al. (2022). Developing Data Thinking Skills Through AI-Based Learning Activities: A Mixed-Methods Study. Computers & Education, 176, 104357. https://doi.org/10.1016/j.compedu.2021.104357

Chiu, T. K. F., et al. (2023). AI-Based Dialog Systems and their Effect on Students’ Critical Thinking: A Systematic Review. Computers & Education, 192, 104642. https://doi.org/10.1016/j.compedu.2022.104642

Chiu, T. K., et al. (2023). Teacher Support and Student Motivation to Learn with Artificial Intelligence (AI) based chatbot. Interactive Learning Environments, 1–17. https://doi.org/10.1080/10494820.2023.2177983

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Deng, L. and Yu, Y. (2023). A Meta-Analysis and Systematic Review of the Effect of Chatbot Technology Use in Sustainable Education. Sustainability, 15(4), 2940. https://doi.org/10.3390/su15042940

Head, A. J., et al. (2020). Information Literacy in the age of Algorithms. Project Information Literacy Research Report.

Holmes, W., et al. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.

Kellner, A. and Otrel-Cass, K. (2020). Data Literacy and Epistemic Cognition in Secondary Schools. Educational Studies, 56(2), 135–153. https://doi.org/10.1080/00131946.2019.1704021

Li, H. and Crossley, S. (2021). Exploring the Impact of Artificial Intelligence on Student Satisfaction and Learning Achievement in Digital Learning Environments. Internet and Higher Education, 49, 100793. https://doi.org/10.1016/j.iheduc.2020.100793

Liu, M., et al. (2024). Self-Directed Learning with Artificial Intelligence: Implications for Student Motivation and Learning Outcomes. Journal of Computer Assisted Learning, 40(1), 45–62. https://doi.org/10.1111/jcal.12784

Luckin, R. (2018). Machine Learning and Human Intelligence: The Future of Education for the 21st Century. UCL IOE Press.

Mandinach, E. B. and Gummer, E. S. (2016). What does it mean for Teachers to be Data Literate? Laying out the skills and knowledge base. Teaching and Teacher Education, 60, 366–376. https://doi.org/10.1016/j.tate.2016.07.011

Partnership for 21st Century Learning. (2019). Framework for 21st Century Learning. Battelle for Kids. https://www.battelleforkids.org/networks/p21/frameworks-resources

Piaget, J. (1972). The Psychology of the Child. Basic Books.

Rahim, N. A., et al. (2023). AI-Enhanced Data Literacy: A Malaysian Perspective on Integrating Artificial Intelligence in STEM Curriculum. Asia-Pacific Journal of Educational Technology, 9(2), 45–60. https://doi.org/10.22158/apjet.v9n2p45

Ridsdale, C., et al. (2015). Strategies and Best Practices for Data Literacy Education: Knowledge Synthesis Report. Dalhousie University.https://doi.org/10.13140/RG.2.1.1922.5044

Tan, M., et al. (2022). Integrating Data Literacy in STEM Education: A Framework for Secondary Schools in Singapore. Journal of Science Education and Technology, 31(4), 587–603. https://doi.org/10.1007/s10956-021-09967-8

Vygotsky, L. S. (1978). Mind in Society: The Development of Higher Psychological Processes. Harvard University Press.

Wineburg, S., et al. (2016). Evaluating Information: The Cornerstone of Civic Online Reasoning. Stanford History Education Group. https://cor.stanford.edu

Wolff, A., et al. (2016). Creating an Understanding of Data Literacy for a Data-Driven Society. Journal of Community Informatics, 12(3), 9–26. https://doi.org/10.15353/joci.v12i3.3275

Zhang, H., et al. (2023). Integrating Ethics and Career Futures with Technical Learning to Promote AI Literacy for Middle School Students: An Exploratory Study. International Journal of Artificial Intelligence in Education, 33(2), 290–324. https://doi.org/10.1007/s40593-022-00298-0

Downloads

Published

2025-12-22

How to Cite

Pomtong, S. ., Thossata, O. ., & Tubmongkhon, S. . (2025). Enhancing Data Literacy Competencies through the Application of Artificial Intelligence for Primary School Students. NEU ACADEMIC AND RESEARCH JOURNAL, 15(4), 138–152. retrieved from https://so04.tci-thaijo.org/index.php/neuarj/article/view/281165