Within the evolving context of educational informatization , the synergistic mechanism between Teachers’ Technological Literacy (TTL) and Task-Based Language Teaching (TBLT) has become a critical issue for enhancing language teaching efficacy. Grounded in situated cognition theory, this study constructs the theoretical framework of "Technology-Mediated Task Situations (TMTS)", revealing three empowerment pathways through which teachers’ technological literacy enhances TBLT via technological tools (e.g., VR/AR, Intelligent Tutoring Systems): task authenticity enhancement, cognitive load optimization, and learning evidence visualization. Integrating the TPACK framework, cognitive load theory, and distributed cognition theory, the study proposes the TMT-Synergy dynamic collaboration model with four core dimensions: technological compatibility, task complexity, situational authenticity, and data-driven orientation. A dual-loop feedback mechanism is established to clarify the dynamic interaction between teachers’ technological literacy and task design. Theoretical validation demonstrates that technological intervention significantly improves the language learning efficacy of TBLT while promoting the evolution of teachers' technological literacy from tool mastery to paradigm innovation. 

Yanhua Li. Synergistic mechanism Between Teachers' Technological Literacy and TBLT Strategies: A Situated Cognition-Based Framework Reconstruction. Frontiers in Educational Research(2025), Vol. 8, Issue 4: 81-87. https://doi.org/10.25236/FER.2025.080412.

1] UNESCO. (2021). AI and education: Guidance for policy-makers. https://unesdoc.unesco.org/ark:/48223/pf0000376091

[2] Long, M. H. (2015). Second language acquisition and task-based language teaching (2nd ed.). John Benjamins: Amsterdam.

[3] Koehler, M. J., & Mishra, P. (2009). What is technological pedagogical content knowledge? Contemporary Issues in Technology and Teacher Education, 9(1), 60–70.

[4] OECD. (2024). Generative AI in education: A policy guide. OECD Publishing: Paris.

[5] Zhou et al.(2021). Intelligent tutoring systems for task-based language learning: A meta-analysis. Computer Assisted Language Learning, 34(5–6), 876–898. 

[6] Sweller, J. (2011). Cognitive load theory. Psychology of Learning and Motivation, 55, 37–76. 

[7] Ellis, R. (2018). Task-based language teaching: A reader. Routledge.

[8] Godwin-Jones, R. (2024). Generative AI in task-based language teaching: Promise and perils. Language Learning & Technology, 28(1), 1–18.

[9] Blake, R. J. (2013). Brave new digital classroom: Technology and foreign language learning. Georgetown University Press.

[10] Dörnyei, Z. (2001). Motivation in second language learning. Cambridge University Press.

[11] Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–40.

[12] Zhang, Y., Li, X., & Wang, J. (2022). A meta-analysis of technology-mediated task-based language teaching: Effects and implications. Journal of Educational Technology & Society, 25(3), 45–60. 

[13] Chapelle, C. A. (2003). English language learning and technology: Lectures on applied linguistics in the digital age. John Benjamins.

[14] Kukulska-Hulme, A., Traxler, J., & Kearney, M. (2015). Mobile learning in resource-constrained settings: A review of the literature. Journal of Computer Assisted Learning, 31(1), 58–71. 

[15] Ministry of Education. (2023). China education informatization development report (2022–2023). Educational Science Press: Beijing.

[16] Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255–284.

[17] Chen, Y. (2020). Virtual reality in task-based language teaching: A case study of cross-cultural communication. ReCALL, 32(3), 331–350.

[18] Grgurović, M., Chapelle, C. A., & Shelley, M. C. (2013). A meta-analysis of effectiveness studies on computer technology-supported language learning. ReCALL, 25(2), 165–198. 

[19] Jones, S., et al. (2024). Cultural bias in AI-generated TBLT scenarios: A multilingual analysis. TESOL Quarterly, 58(2), 321–345.

[20] Tondeur, J., van Braak, J., & Valcke, M. (2017). Teacher technological literacy: A review of the literature. Journal of Computer Assisted Learning, 33(2), 109–121.

[21] Scollon, R., & Scollon, S. W. (2001). Intercultural communication: A discourse approach (2nd ed.). Blackwell.

[22] Hutchins, E. (1995). Cognition in the wild. MIT Press.

[23] Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.

[24] Paivio, A. (1986). Mental representations: A dual coding approach. Oxford University Press.

[25] Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. 

[26] Pulvermüller, F. (2018). The neuroscience of language: Brain networks for words and sequences (2nd ed.). Cambridge University Press.

[27] Means et al. (2014). Evaluation of evidence-based practices in online learning: A meta-analysis and review of online learning studies. U.S. Department of Education, Office of Planning, Evaluation and Policy Development.

[28] Cao, Y., et al. (2022). Technology integration in rural Chinese schools: A mixed-methods study. Computers & Education, 190, 104602. 

[29] Holland, J. H. (2012). Signals and boundaries: Building blocks for complex adaptive systems. MIT Press.

[30] Teece, D. J. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.

[31] Bender, E. M., et al. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the ACM on Human-Computer Interaction, 5(CSCW), 1–33. 

[32] W3C. (2023). Web content accessibility guidelines (WCAG) 2.2.https://www.w3.org/TR/WCAG22/