Description

Boris Vázquez-Calvo is an assistant professor of language education (English and Spanish) and a recognized expert in digital literacies, informal language learning, and technology-enhanced language education. His research explores how artificial intelligence, online communities, and digital platforms shape language learning, pedagogical practices, and the evolving identities of language learners and educators in multilingual and multicultural contexts. With more than 40 publications and over 60 conference presentations, including invited keynotes, his work has significantly contributed to understanding the intersections of digital technologies, language learning, and communication.

As Principal Investigator, Boris leads multiple publicly funded research projects that examine the intersections of digital technologies, literacies, and language education. His most recent project, DEFINERS: Digital Language Learning of Junior and Preservice Language Teachers (72,500€, TED2021-129984A-I00, Spain’s National Research Plan), investigates how preservice language educators engage with AI-powered tools, digital platforms, and online social networks to develop their professional skills and digital autonomy. The project provides critical insights into digital technologies and AI’s role in self-directed learning, digital autonomy, and identity, from the perspective of preservice teachers.

Beyond DEFINERS, Boris has spearheaded SEGUE: Social Media and Video Games for Language Learning and Teaching (4,200€, Research Plan UMA, 2022-2024), which examined how social media and gaming environments foster language development and literacy practices. Additionally, his past postdoctoral project, Gaming as Literacy (110,000€, Xunta de Galicia), explored how video games function as academic and vernacular literacy practices, emphasizing informal learning in online gaming communities fueled by gaming as a fan practice.

His research also extends to memes and digital humor in language teacher education, examining how humor and multimodal communication shape identity construction and pedagogical reflection. His work on AI-driven speech technologies in pronunciation training has contributed to discussions on self-regulated learning and AI-assisted feedback in teacher training programs. He is now researching AI-driven chatbots to determine the extent to which language teacher students co-construct specialized knowledge through human-AI interaction.

Currently, Boris is transitioning from the University of Málaga to the University of Seville, where, in July 2025, he will assume a prestigious, research-focused, tenure-track postdoctoral position in language education, funded by Spain’s and the EU’s research agencies under the Ramón y Cajal grant (RYC2023-043502-I).

AI Through a Social-Science Lens: Rethinking Learning, Teaching, and Research

Artificial Intelligence (AI) tools have slipped quietly from research labs into everyday classrooms: they now rewrite the texts our students read, grade essays in seconds, and forecast when attention will drift. Faced with systems this powerful—and this fast-moving—social-science educators, learners, and researchers need more than curiosity; they need a clear interpretive map. This keynote provides that map by looking at AI through a three-part lens—learning, teaching, and research—and by turning headline statistics and empirical studies into plain-language insights that matter on the ground. For example, in a survey of 1,611 Japanese university students of Spanish, 67 % had used generative-AI tools in the past month and 60 % used them weekly or daily for comprehension, translation, and writing support (Aroz et al., 2025)—evidence that AI has become routine, not experimental, indicating paths for growth and teaching intervention. Additionally, a meta-analysis of 50 intelligent-tutoring-system trials showed an average gain of 0.66 SD (Kulik & Fletcher, 2016)—roughly the jump from the 50th to the 75th percentile. However, the same review shows much smaller gains (≈ 0.13 SD) on independent, standardized tests; the big effects (≈ 0.73 SD) appear when the test is written by the designers themselves, meaning that AI can be powerful, yet results shrink when you step outside the tutor’s homework sheet. The COST (Content–Others–Self–Tasks) (Zhu et al., 2023) framework and a socio-cognitive approach to attitude (Hallajow, 2018) help pinpoint where and what is really improving learning-wise. 

A 2024 survey-model of 393 Malaysian university lecturers revealed that the more anxious teachers felt about AI, the less willing they were to experiment with it—each one-point rise on the anxiety scale cut “adoption readiness” by almost half a point. Crucially, readiness itself proved the major swing factor: it accounted for 67 % of the overall change in teachers’ positive attitude toward AI-based assessment (Shahid et al., 2024). Put simply, boosting teachers’ practical confidence does far more to win acceptance than any top-down policy circular or shiny new gadget, whereas unchecked anxiety stalls innovation before it starts. A qualitative discourse analysis with 17 teachers worldwide revealed common worries about plagiarism yet a cautious willingness to experiment (Paz-López et al., under review). Together these findings quantify—and humanize—the pedagogical lag between fast-moving student adoption and hesitant teacher uptake. A review of 146 AI-in-higher-education papers applauded the field’s technical ingenuity—better prediction models, smarter chatbots—but also warned that most studies stop at the algorithm and rarely ask “so what for teaching?” or “who benefits?” (Zawacki-Richter et al., 2019). In other words, research output is booming, yet its links to real pedagogy and social justice remain thin.

The equity gap widens when we look at language coverage: fewer than 2 % of the planet’s 7,000-plus languages appear in Natural Language Processing (NLP) corpora (Joshi et al., 2020), so today’s AI “polyglot” still ignores 98 % of the world’s linguistic diversity. UNESCO’s Recommendation on the Ethics of Artificial Intelligence (2021, p. 10-13) responds by setting minimum guardrails— transparency, human oversight, bias and stereotyping checks, environmental duty of care— while recent AI-literacy frameworks distil four actionable competencies: know what AI is, use it wisely, evaluate its outputs, and act ethically (Ng et al., 2021). Taken together, the message is clear: impressive code is not enough; without pedagogy and inclusion, AI risks reinforcing the very inequities it promises to solve. The evidence sends a clear but urgent signal: AI capability is sprinting; AI literacy is jogging. Unless educators and researchers learn to explain these systems, critique their blind spots, and co-design them for equity, the technology’s promise will harden into new barriers. The keynote closes with three immediately actionable steps—teacher “sandbox” workshops to melt anxiety, multilingual data drives to shrink the 2 % language gap, and ethics-by-design checklists for every pilot research or classroom project. Together, these steps sketch an initial roadmap for helping minds and machines thrive—today, in tomorrow’s classrooms, and throughout an equitable future of learning, teaching, and research.

References

Aroz, A., Hirose, H., Nishimura, K., & Cassany, D. (2025). Inteligencia artificial generativa para aprender español: Prácticas y valoraciones. Cuadernos CANELA, 36, 1–22.

Hallajow, N. (2018). Identity and Attitude: Eternal Conflict or Harmonious Coexistence. Journal of Social Sciences, 14(1), 43–54. https://doi.org/10.3844/jssp.2018.43.54

Joshi, P., Santy, S., Budhiraja, A., Bali, K., & Choudhury, M. (2020). The state and fate of linguistic diversity and inclusion in the NLP world. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 6282–6293). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.560

Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of intelligent tutoring systems: A meta-analytic review. Review of Educational Research, 86(1), 42–78. https://doi.org/10.3102/0034654315581420

Ng, D. T. K., Chai, C. S., & Low, E. L. (2021). Conceptualising AI literacy: An exploratory review. Computers & Education: Artificial Intelligence, 2, Article 100041. https://doi.org/10.1016/j.caeai.2021.100041

Paz-López, A., Shafirova, L., & Vazquez-Calvo, B. (under review). A discourse analysis of language teachers’ attitudes toward Artificial Intelligence.

Shahid, M. K., Zia, T., Bangfan, L., Iqbal, Z., & Ahmad, F. (2024). Exploring the relationship of psychological factors and adoption readiness in determining university teachers’ attitude on AI-based assessment systems. The International Journal of Management Education, 22(2), 100967. https://doi.org/10.1016/j.ijme.2024.100967 

UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence. https://unesdoc.unesco.org/ark:/48223/pf0000380455

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial-intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0

Zhu, C., Meng, S., Luo, J., Li, T., & Wang, M. (2023). How to harness the potential of ChatGPT in education? Knowledge Management & E-Learning: An International Journal, 15(2), 133–152. https://doi.org/10.34105/j.kmel.2023.15.008

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