Reclaiming Pedagogy in CALL: Foregrounding Pedagogical Design in Technology-Enhanced Language Teaching

Reclaiming Pedagogy in CALL: Foregrounding Pedagogical Design in Technology-Enhanced Language Teaching

Over the past few decades, computer-assisted language learning (CALL) has played a pivotal role in transforming second language (L2) education through the integration of digital tools, online platforms, mobile applications, data-driven learning, automated feedback, intelligent tutoring systems, and, more recently, generative artificial intelligence (Lim & Aryadoust, 2022; Son et al., 2025). While these technologies have generated much enthusiasm and have sparked meaningful innovations in SLA, much of the discussion in the field has focused on technological affordances, user perceptions, learner experiences, skill development, language outcomes, or implementation challenges (Law, 2024; Lee et al., 2025; Lyu et al., 2025; Seyyedrezaei et al., 2024). Yet, these lines of inquiry have collectively highlighted a critical gap: the pedagogical dimensions of technology-enhanced L2 instruction remain under-explored (Canals & Mor, 2023; Liu et al., 2025). As recent new technologies, especially generative AI tools, become increasingly integrated into instructional routines, assessment systems, and communicative practices, there is an urgent need to shift the discourse from “technology for learning” to “technology for pedagogy”—that is, from evaluating what technologies can do to understanding how pedagogical design principles guide their effective integration into language teaching. The promise of advanced technologies cannot be fully realised without a deeper engagement with the pedagogical principles that guide their adoption.

The proliferation of technological tools in L2 education calls for a renewed focus on the teacher’s role, pedagogical reasoning, instructional design, and classroom orchestration in CALL contexts (Chapelle, 2025; Wang et al., 2024). Teachers remain central actors in determining how technology is used to support learning, how it aligns with curricula, and how it interacts with broader objectives in instructed SLA. Without explicit attention to pedagogy, technology risks being treated as an add-on or an automated solution rather than an element that can be purposefully integrated into coherent language teaching designs (Pack & Maloney, 2023). As technology continues to profoundly influence all essential aspects of language education, this has intensified the need to account for the ways teachers conceptualise, design, implement, negotiate, and assess pedagogically meaningful uses of technological resources. This emphasis on pedagogy is crucial not only for understanding how technology works in L2 classrooms, but also for ensuring that its use contributes to informative, rigorous, and theoretically-grounded forms of language instruction.

This special issue aims to reclaim pedagogy as a central focus in CALL research by examining how technology-mediated teaching approaches are designed, enacted, and evaluated in diverse L2 instructional settings. It encourages research that foregrounds teaching practices and pedagogical design principles rather than technological capabilities, emphasising the dynamic interplay between pedagogical decisions, learner engagement, and language development. Such an orientation invites contributions that consider how teachers adapt technological applications to language curricula, scaffold interactions around technology-generated tasks, improve learners’ experiences with technology-supported learning, balance human judgement with automated support, and assess the effectiveness of technology-integrated instructional designs. By drawing together empirical, conceptual, and design-based studies, this issue seeks to move beyond demonstrations of technological capability toward a richer understanding of the pedagogical frameworks, rationale, planning, practice, and evaluation that underlie successful integration of technology into classroom language teaching, thereby advancing a more balanced, principled, and sustainable vision of technology-facilitated education in CALL.

Topics of interests for the special issue include but are not limited to the following:

•         Instructional frameworks and design principles for integrating technology into L2 pedagogy

•         Classroom orchestration strategies for managing technology-supported tasks, student-technology interactions, and teacher instruction

•         Methodological innovations, mixed-methods approaches, quasi-experimental designs, classroom-based intervention studies, and design-based research for investigating the dynamic relationships between pedagogical decisions, technology-mediated interactions, and language learning outcomes

•         Ethical, equitable, and responsible pedagogical strategies for technology-facilitated language teaching

•         Technology-assisted feedback and assessment as pedagogical practice to promote L2 reading, speaking, listening, and writing skills

•         Intervention studies examining how specific pedagogical approaches in technology-enhanced environments influence learner emotions, motivation, engagement, and their relationships with language learning outcomes

•         Empirical investigations of teachers’ pedagogical reasoning, roles, and agency in designing, adapting, and enacting technology-enhanced instruction, interpreting and filtering AI-generated content, and mediating technology use for instructional purposes

•         Teacher education and professional development programmes for building pedagogical knowledge and competence in technology-integrated language teaching

•         Effects of theoretically-grounded and well-designed technology interventions on language learners’ experiences, agency, and performance

•         Assessment methods and instruments for evaluating learning gains, linguistic progress, or proficiency development in technology-enhanced L2 teaching environments

Methodological Expectations

This special issue welcomes empirical research employing diverse methodologies, including experimental and quasi-experimental designs, classroom-based intervention studies, design-based research, conversation analysis, corpus-based investigations, and rigorous qualitative case studies that examine pedagogical processes and their effects on language learning. Submissions must demonstrate clear theoretical framing, appropriate research designs for addressing the stated research questions, systematic data collection and analysis, and substantive contributions to understanding pedagogy in technology-mediated language learning.

The following types of submissions are not suitable for this special issue and are unlikely to be sent for review: purely descriptive accounts of technology implementation without examining pedagogical processes or learning outcomes; survey-only studies documenting perceptions, attitudes, or self-reported behaviours without investigating actual pedagogical practice or its effects; conceptual or position papers without empirical validation; studies that focus primarily on technology features or user experiences without foregrounding pedagogical dimensions; and studies that evaluate the effectiveness of a specific AI tool or platform where the primary contribution is about the technology’s capabilities rather than the pedagogical design principles guiding its use. Authors should ensure their submissions move beyond documentation toward investigation of the relationships between pedagogical decisions, technology-mediated instruction, and language development.

References

Canals, L., & Mor, Y. (2023). Towards a signature pedagogy for technology-enhanced task-based language teaching: Defining its design principles. ReCALL, 35, 4–18. doi:10.1017/S0958344022000118

Chapelle, C. A. (2025). Generative AI as game changer: Implications for language education. System, 132, 103672. https://doi.org/10.1016/j.system.2025.103672

Law, L. (2024). Application of generative artificial intelligence (GenAI) in language teaching and learning: A scoping literature review. Computers and Education Open, 6, 100174. https://doi.org/10.1016/j.caeo.2024.100174

Lee, S., Choe, H., Zou, D., & Jeon, J. (2025). Generative AI (GenAI) in the language classroom: A systematic review. Interactive Learning Environments, 1–25. https://doi.org/10.1080/10494820.2025.2498537

Lim, M. H., & Aryadoust, V. (2022). A scientometric review of research trends in computer-assisted language learning (1977–2020). Computer Assisted Language Learning, 35, 2675–2700. https://doi.org/10.1080/09588221.2021.1892768

Liu, J., Sihes, A. J. B., & Lu, Y. (2025). How do generative artificial intelligence (AI) tools and large language models (LLMs) influence language learners’ critical thinking in EFL education? A systematic review. Smart Learning Environments, 12, 48. https://doi.org/10.1186/s40561-025-00406-0

Lyu, B., Lai, C., & Guo, J. (2025). Effectiveness of chatbots in improving language learning: A meta-analysis of comparative studies. International Journal of Applied Linguistics, 35, 834–851. https://doi.org/10.1111/ijal.12668

Pack, A., & Maloney, J. (2023). Using generative artificial intelligence for language education research: Insights from using OpenAI’s ChatGPT. TESOL Quarterly, 57, 1571–1582. https://doi.org/10.1002/tesq.3253

Son, J. B., Ružić, N. K., & Philpott, A. (2025). Artificial intelligence technologies and applications for language learning and teaching. Journal of China Computer-Assisted Language Learning, 5, 94–112. https://doi.org/10.1515/jccall-2023-0015

Seyyedrezaei, M. S., Amiryousefi, M., Gimeno-Sanz, A., & Tavakoli, M. (2024). A meta-analysis of the relative effectiveness of technology-enhanced language learning on ESL/EFL writing performance: retrospect and prospect. Computer Assisted Language Learning, 37, 1771–1805. https://doi.org/10.1080/09588221.2022.2118782

Wang, J., Zhou, H., Chen, S., Tong, H., & Yang, Y. (2024). How teachers support secondary school students to become self-regulated learners in technology-enhanced language learning. System, 123, 103313. https://doi.org/10.1016/j.system.2024.103313