The Future of Education
Empowering EFL Teachers through Collaborative Evaluation of AI Tools for Linguistic Skills Enhancement
Maria Perifanou, Aristotle University of Thessaloniki (Greece)
Abstract
As Artificial Intelligence (AI) becomes increasingly integrated into education, English as a Foreign Language (EFL) educators are faced with the dual imperative of technological literacy and pedagogical discernment. This activity, conducted with pre-service (bachelor) and in-service (master) EFL teachers at the Aristotle University of Thessaloniki, aimed to foster critical engagement with AI-powered tools that support specific linguistic skills: reading, writing, listening, speaking, grammar, and vocabulary.
Participants worked in skill-based groups to explore a curated repository of English learning AI tools (https://topai.tools/s/learn-english). Emphasis was placed on critically examining not just the tools themselves, but also the opportunities and limitations inherent in such aggregated AI repositories. Using a structured evaluation grid informed by previous research (e.g., Reinders & White, 2016), students assessed 2–3 tools and selected one for in-depth analysis. Evaluation criteria included functionality, pedagogical alignment, usability, accessibility, customization, engagement, and data ethics. Results were presented and visually curated on the Miro collaborative platform, supporting peer-based reflection and digital co-construction of knowledge.
The activity culminated in individual reflective essays, prompting teachers to consider tool integration in their own classrooms and the broader implications for EFL practice. Discussion themes included the responsible use of AI (e.g., Holmes et al., 2022; Williamson & Eynon, 2020), digital equity (Zawacki-Richter et al., 2019), and the alignment of technological tools with communicative language teaching principles (Golonka et al., 2019).
Students reported increased confidence in navigating educational AI and demonstrated awareness of both its pedagogical potential and ethical complexities. The task highlights the importance of critical digital pedagogy in teacher education and the need for continual upskilling in an evolving technological landscape. As AI tools proliferate, teacher education must prioritize not only practical skills but also reflective practice and principled technology adoption.
REFERENCES
- Golonka, E.M., Bowles, A.R., Frank, V., et al. (2014) Technologies for Foreign Language Learning: A Review of Technology Types and Their Effectiveness. Computer Assisted Language Learning, 27, 70-105. https://doi.org/10.1080/09588221.2012.700315
- Holmes, W., & Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education, 57, 542–570. https://doi.org/10.1111/ejed.12533
- Reinders, H., & White, C. (2016). 20 years of autonomy and technology: How far have we come and where to next? Language Learning & Technology, 20(2), 143-154.Retrieved from http://llt.msu.edu/issues/june2016/reinderswhite.pdf
- Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223-235
- 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, 39. https://doi.org/10.1186/s41239-019-0171-0
