AI Literacy Among Future Math Educators: The Mediating Role of Digital Literacy in Mathematics Teaching

Authors

  • Anjie Manulat Manto
  • Gelyn E. Señedo
  • Feachy Gay E. Jauculan
  • Mary Shein Q. Giangan
  • Gerly A. Alcantara

Keywords:

AI literacy; digital literacy; mathematics teaching; pre-service mathematics teachers

Abstract

The use of artificial intelligence (AI) in mathematics teaching emphasizes the necessity for future educators to develop digital literacy (DL) and AI literacy (AIL). Despite increasing attention being paid to these competencies, little is known about how DL influences AIL among pre-service mathematics teachers. A descriptive correlational research design was used in this study to assess the levels of DL and AIL, explore their relationship, and profile participants based on gender and year level. Teachers (AILST)instruments, including the Digital Literacy Scale and AI Literacy Scale for Teachers (AILST), measured four AIL dimensions: perception, knowledge and skills, application and innovation, and ethics. Digital literacy was evaluated concurrently. The data were analyzed using IBM SPSS Statistics (version 21). Descriptive statistics (means and standard deviations) summarized overall literacy levels. To determine relationships, Pearson’s r was used to assess the correlation between digital and AI literacy, Spearman’s rho examined associations with year level, and the chi-square test explored the relationship between gender and literacy variables. Results showed that pre-service teachers had above-average competence of DL and AIL, with a moderate positive correlation between the two, indicating that greater digital fluency supports stronger AI-related competencies. However, notable gaps persist in applying AI for innovation and in critically evaluating AI-generated content. These findings indicate the need to integrate DL and AIL in teacher education programs to prepare aspiring mathematics educators for ethical, effective, and innovative AI use in classrooms, thereby contributing to research-informed instructional practices and responsive curriculum development.

https://doi.org/10.26803/ijlter.24.7.37

References

Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in Human Behavior, 63, 75–90. https://doi.org/10.1016/j.chb.2016.05.014

Adams, C., Pente, P., Lemermeyer, G., & Rockwell, G. (2023). Ethical principles for artificial intelligence in K-12 education. Computers and Education: Artificial Intelligence, 4, 100131. https://doi.org/10.1016/j.caeai.2023.100131

Avinç, E., & Do?an, F. (2024). Digital literacy scale: Validity and reliability study with the Rasch model. Education and Information Technologies, 29, 22895–22941. https://doi.org/10.1007/s10639-024-12662-7

Ayanwale, M. A., Adelana, O. P., Molefi, R. R., Adeeko, O., & Ishola, A. M. (2024). Examining artificial intelligence literacy among pre-service teachers for future classrooms. Computers and Education Open, 6, 100179. https://doi.org/10.1016/j.caeo.2024.100179

Ayanwale, M. A., Frimpong, E. K., Opesemowo, O. A. G., & Sanusi, I. T. (2024). Exploring factors that support pre-service teachers’ engagement in learning artificial intelligence. Journal for STEM Education Research, 8, 199–229. https://doi.org/10.1007/s41979-024-00121-4

Baker, R. S., Martin, T., & Rossi, L. M. (2016). Educational data mining and learning analytics. In A. A. Rupp & J. P. Leighton (Eds.), The Wiley handbook of cognition and assessment: Frameworks, methodologies, and applications (pp. 379–396). Wiley. https://doi.org/10.1002/9781118956588.ch16

Benvenuti, M., Cangelosi, A., Weinberger, A., Mazzoni, E., Benassi, M., Barbaresi, M., & Orsoni, M. (2023). Artificial intelligence and human behavioral development: A perspective on new skills and competences acquisition for the educational context. Computers in Human Behavior, 148, 107903. https://doi.org/10.1016/j.chb.2023.107903

Bleher, H., & Braun, M. (2023). Reflections on putting AI ethics into practice: How three AI ethics approaches conceptualize theory and practice. Science and Engineering Ethics, 29(3), 21. https://doi.org/10.1007/s11948-023-00443-3

Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., ... & Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21(1), 4. https://doi.org/10.1186/s41239-023-00436-z

Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023). MAILS-Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change-and meta-competencies. Computers in Human Behavior: Artificial Humans, 1(2), 100014. https://doi.org/10.1016/j.chbah.2023.100014

Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/access.2020.2988510

Chiu, T. K. F., Moorhouse, B. L., Chai, C. S., & Ismailov, M. (2023). Teacher supports and student motivation to learn with artificial intelligence (AI) based chatbot. Interactive Learning Environments, 1–17. https://doi.org/10.1080/10494820.2023.2172044

Chiu, T. K., Ahmad, Z., Ismailov, M., & Sanusi, I. T. (2024). What is artificial intelligence, literacy and competency? A comprehensive framework to support them. Computers and Education Open, 6, 100171. https://doi.org/10.1016/j.caeo.2024.100171

Cirneanu, A. L., & Moldoveanu, C. E. (2024). Use of digital technology in integrated mathematics education. Applied System Innovation, 7(4), 66. https://doi.org/10.3390/asi7040066

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982

Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology changes: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255–284. https://doi.org/10.1080/15391523.2010.10782551

Falloon, G. (2020). From digital literacy to digital competence: The teacher digital competency (TDC) framework. Educational Technology Research and Development, 68(5), 2449–2472. https://doi.org/10.1007/s11423-020-09767-4

Garlinska, M., Osial, M., Proniewska, K., & Pregowska, A. (2023). The influence of emerging technologies on distance education. Electronics, 12(7), 1550. https://doi.org/10.3390/electronics12071550

Geraniou, E., & Jankvist, U. T. (2019). Towards a definition of “mathematical digital competency”. Educational Studies in Mathematics, 102(1), 29–45. https://doi.org/10.1007/s10649-019-09893-8

Guan, C., Mou, J., & Jiang, Z. (2020). Artificial intelligence innovation in education: A twenty-year data-driven historical analysis. International Journal of Innovation Studies, 4(4), 134–147. https://doi.org/10.1016/j.ijis.2020.09.001

Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of digital technologies in education: A review. Sustainable Operations and Computers, 3, 275–285. https://doi.org/10.1016/j.susoc.2022.05.004

Hasibuan, N. S., & Lubis, M. S. (2024). The influence of digital literacy and learning styles on students' mathematics learning outcomes. Unnes Journal of Mathematics Education, 13(1), 36–45.

Hatlevik, O. E., Guðmundsdóttir, G. B., & Loi, M. (2015). Digital diversity among upper secondary students: A multilevel analysis of the relationship between cultural capital, self-efficacy, strategic use of information and digital competence. Computers & Education, 81, 345–353. https://doi.org/10.1016/j.compedu.2014.10.019

Huang, X. (2021). Aims for cultivating students’ key competencies based on artificial intelligence education in China. Education and Information Technologies, 26(5), 5127–5147. https://doi.org/10.1007/s10639-021-10530-2

Huang, X., Craig, S. D., Xie, J., Graesser, A., & Hu, X. (2016). Intelligent tutoring systems work as a math gap reducer in 6th grade after-school program. Learning and Individual Differences, 47, 258–265. https://doi.org/10.1016/j.lindif.2016.01.012

Hur, J. W. (2025). Fostering AI literacy: Overcoming concerns and nurturing confidence among preservice teachers. Information and Learning Sciences, 126(1/2), 56–74. https://doi.org/10.1108/ILS-11-2023-0170

Ilomäki, L., Paavola, S., Lakkala, M., & Kantosalo, A. (2016). Digital competence–An emergent boundary concept for policy and educational research. Education and Information Technologies, 21, 655–679. https://doi.org/10.1007/s10639-014-9346-4

Jarke, J., & Breiter, A. (2019). The datafication of education. Learning, Media and Technology, 44(1), 1–6. https://doi.org/10.1080/17439884.2019.1573833

Kaswan, K. S., Dhatterwal, J. S., & Ojha, R. P. (2024). AI in personalized learning. In Advances in technological innovations in higher education (pp. 103–117). CRC Press. https://doi.org/10.1201/9781003376699-9

Kim, K., & Kwon, K. (2023). Exploring the AI competencies of elementary school teachers in South Korea. Computers and Education: Artificial Intelligence, 4, 100137. https://doi.org/10.1016/j.caeai.2023.100137

Kim, S., Jang, Y., Kim, W., Choi, S., Jung, H., Kim, S., & Kim, H. (2021, May). Why and what to teach: AI curriculum for elementary school. In Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15569–15576. https://doi.org/10.1609/aaai.v35i17.17833

Kong, S. C., Cheung, W. M. Y., & Zhang, G. (2021). An evaluation of an artificial intelligence literacy course for university students with diverse study backgrounds. Computers and Education: Artificial Intelligence, 2, 100026. https://doi.org/10.1016/j.caeai.2021.100026

Kousa, P., & Niemi, H. (2023). AI ethics and learning: EdTech companies’ challenges and solutions. Interactive Learning Environments, 31(10), 6735–6746. https://doi.org/10.1080/10494820.2022.2043908

Lim, E. M. (2023). The effects of pre-service early childhood teachers’ digital literacy and self-efficacy on their perception of AI education for young children. Education and Information Technologies, 28(10), 12969–12995. https://doi.org/10.1007/s10639-023-11724-6

Long, D., & Magerko, B. (2020, April). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). https://doi.org/10.1145/3313831.3376727

Lorenz, U., & Romeike, R. (2023, October). What is AI-PACK? – Outline of AI competencies for teaching with DPACK. In International Conference on Informatics in Schools: Situation, Evolution, and Perspectives (pp. 13–25). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-44900-0_2

Lucas, M., Bem-haja, P., Zhang, Y., Llorente-Cejudo, C., & Palacios-Rodríguez, A. (2025). A comparative analysis of pre-service teachers’ readiness for AI integration. Computers and Education: Artificial Intelligence, 8, 100396. https://doi.org/10.1016/j.caeai.2025.100396

Mandal, S., & Naskar, S. K. (2021). Classifying and solving arithmetic math word problems—An intelligent math solver. IEEE Transactions on Learning Technologies, 14(1), 28–41. https://doi.org/10.1109/TLT.2021.3057805

Markauskaite, L., Marrone, R., Poquet, O., Knight, S., Martinez-Maldonado, R., Howard, S., ... & Siemens, G. (2022). Rethinking the entwining between artificial intelligence and human learning: What capabilities do learners need for a world with AI? Computers and Education: Artificial Intelligence, 3, 100056. https://doi.org/10.1016/j.caeai.2022.100056

Monteiro, A., & Leite, C. (2021). Digital literacies in higher education: Skills, uses, opportunities and obstacles to digital transformation. Revista de Educación a Distancia (RED), 21(65). https://doi.org/10.6018/red.438721

Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041

Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71(1), 137–161. https://doi.org/10.1007/s11423-023-10203-6

Ning, Y., Zhang, C., Xu, B., Zhou, Y., & Wijaya, T. T. (2024). Teachers’ AI-TPACK: Exploring the relationship between knowledge elements. Sustainability, 16(3), 978. https://doi.org/10.3390/su16030978

Ning, Y., Zhang, W., Yao, D., Fang, B., Xu, B., & Wijaya, T. T. (2025). Development and validation of the Artificial Intelligence Literacy Scale for Teachers (AILST). Education and Information Technologies, 1–35. https://doi.org/10.1007/s10639-025-13347-5

Ning, Y., Zhou, Y., Wijaya, T. T., & Chen, J. (2022). Teacher education interventions on teacher TPACK: A meta-analysis study. Sustainability, 14(18), 11791. https://doi.org/10.3390/su141811791

OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https:// chat.openai.com/chat

Pane, J. F., Steiner, E. D., Baird, M. D., Hamilton, L. S., & Pane, J. D. (2017). Informing progress: Insights on personalized learning implementation and effects. Research Report RR-2042-BMGF. RAND Corporation. https://doi.org/10.7249/RR2042

Pinski, M., & Benlian, A. (2023). AI literacy—Towards measuring human competency in artificial intelligence. AIS Transactions on Human-Computer Interaction, 15(3), 1–25. Retrieved from https://aisel.aisnet.org/thci/vol15/iss3/1/

Poquet, O., & De Laat, M. (2021). Developing capabilities: Lifelong learning in the age of AI. British Journal of Educational Technology, 52(4), 1695–1708. https://doi.org/10.1111/bjet.13123

Rafi, M., JianMing, Z., & Ahmad, K. (2019). Technology integration for students’ information and digital literacy education in academic libraries. Information Discovery and Delivery, 47(4), 203–217. https://doi.org/10.1108/IDD-07-2019-0049

Sattelmaier, L., & Pawlowski, J. M. (2023, October). Towards a generative artificial intelligence competence framework for schools. In A. Gunawan, R. Andriani, & D. Ardiansyah (Eds.), Proceedings of the International Conference on Enterprise and Industrial Systems (ICOEINS 2023). 270, pp. 291–298. Springer Nature. https://doi.org/10.2991/978-94-6463-340-5_26

Sing, C. C., Teo, T., Huang, F., Chiu, T. K., & Wei, W. X. (2022). Secondary school students’ intentions to learn AI: Testing moderation effects of readiness, social good and optimism. Educational Technology Research and Development, 70(3), 765–782. https://doi.org/10.1007/s11423-022-10111-1

Spante, M., Hashemi, S. S., Lundin, M., & Algers, A. (2018). Digital competence and digital literacy in higher education research: Systematic review of concept use. Cogent Education, 5(1), 1519143. https://doi.org/10.1080/2331186X.2018.1519143

Teo, T., Zhou, M., Fan, A. C. W., & Huang, F. (2019). Factors that influence university students’ intention to use Moodle: A study in Macau. Educational Technology Research and Development, 67, 749–766. https://doi.org/10.1007/s11423-019-09650-x

Tondeur, J., Van Braak, J., Ertmer, P. A., & Ottenbreit-Leftwich, A. (2017). Understanding the relationship between teachers’ pedagogical beliefs and technology use in education: A systematic review of qualitative evidence. Educational Technology Research and Development, 65, 555–575. https://doi.org/10.1007/s11423-016-9481-2

Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019). Envisioning AI for K-12: What should every child know about AI? Proceedings of the AAAI Conference on Artificial Intelligence, 33(1), 9795–9799. https://doi.org/10.1609/aaai.v33i01.33019795

Viberg, O., Grönlund, Å., & Andersson, A. (2023). Integrating digital technology in mathematics education: A Swedish case study. Interactive Learning Environments, 31(1), 232–243. https://doi.org/10.1080/10494820.2020.1770801

Voogt, J., Fisser, P., Good, J., Mishra, P., & Yadav, A. (2015). Computational thinking in compulsory education: Towards an agenda for research and practice. Education and Information Technologies, 20, 715–728. https://doi.org/10.1007/s10639-015-9412-6

Wang, B., Rau, P. L. P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology, 42(9), 1324–1337. https://doi.org/10.1080/0144929X.2022.2072768

Wang, W. S., Lin, C. J., Lee, H. Y., Huang, Y. M., & Wu, T. T. (2025). Integrating feedback mechanisms and ChatGPT for VR-based experiential learning: Impacts on reflective thinking and AIoT physical hands-on tasks. Interactive Learning Environments, 33(2), 1770–1787. https://doi.org/10.1080/10494820.2024.2375644

Yang, J. (2025). Research on the correlation between college students' use of large language models and AI digital literacy. In C. Barstow & H. Briel (Eds.), Connecting Ideas, Cultures, and Communities (pp. 516–522). Routledge.

Yang, W. (2022). Artificial intelligence education for young children: Why, what, and how in curriculum design and implementation. Computers and Education: Artificial Intelligence, 3, 100061. https://doi.org/10.1016/j.caeai.2022.100061

Yuan, Z., Liu, J., Deng, X., Ding, T., & Wijaya, T. (2023). Facilitating conditions as the biggest factor influencing elementary school teachers’ usage behavior of dynamic mathematics software in China. Mathematics, 11(6), 1536. https://doi.org/10.3390/math11061536

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), Article 1. https://doi.org/10.1186/s41239-019-0171-0

Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M. (2023). Acceptance of artificial intelligence among pre-service teachers: A multigroup analysis. International Journal of Educational Technology in Higher Education, 20(1), 49. https://doi.org/10.1186/s41239-023-00420-7

Zhang, H., Lee, I., Ali, S., DiPaola, D., Cheng, Y., & Breazeal, C. (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/s40593-022-00293-3

Zhao, L., Wu, X., & Luo, H. (2022). Developing AI literacy for primary and middle school teachers in China: Based on a structural equation modeling analysis. Sustainability, 14(21), 14549. https://doi.org/10.3390/su142114549

Zhao, Y., Llorente, A. M. P., & Gómez, M. C. S. (2021). Digital competence in higher education research: A systematic literature review. Computers & Education, 168, 104212. https://doi.org/10.1016/j.compedu.2021.104212

Downloads

Published

2025-07-30

How to Cite

Manto, A. M. ., Señedo, G. E. ., Jauculan, F. G. E. ., Giangan, M. S. Q. ., & Alcantara, G. A. . (2025). AI Literacy Among Future Math Educators: The Mediating Role of Digital Literacy in Mathematics Teaching. International Journal of Learning, Teaching and Educational Research, 24(7), 753–775. Retrieved from http://ijlter.net/index.php/ijlter/article/view/2421

Issue

Section

Articles