The field of Language Teaching and Learning has long been grounded in the concept of authenticity (Gilmore, 2007). Authentic materials are typically defined as resources created within genuine communicative contexts (Mishan, 2005; O’Keefe & McCarthy, 2022). Authenticity can also go beyond the learning materials and extends to learner engagement and interaction with teaching materials (van Lier, 1996). Under this complementary view, authenticity is designed to impart realness or genuineness to language use and is centered on real-life scenarios and communicative exchanges (Cook, 1999; Ellis, 2003; Widdowson, 1998).

However, in contemporary language teaching and learning, the conventional notion of authenticity has undergone a significant transformation, becoming a dynamic concept that extends to a wide array of digital tools. This evolution is particularly evident in the incorporation of interactive platforms and collaborative behaviours, marked by ongoing interaction and constant response (Gilmore, 2019). This paradigm shift, which aligns seamlessly with authentic digital exchanges, has become a key factor in redefining authenticity (Buendgens-Kosten, 2013; Kukulska-Hulme, 2009, 2017; Ifenthaler, 2016; Mishan, 2017).

The integration of Artificial Intelligence introduces a further dimension to authenticity: AI-powered tools generate and personalize content, tailoring language learning experiences to individual interests and proficiency levels (Pack & Maloney, 2023; Simonsen, 2019). While this opens up possibilities for tailored and engaging learning experiences, it also introduces questions about the authenticity of AI-generated materials compared to human-created content (Godwin-Jones, 2022; Hockly, 2023). These developments not only transform the learning landscape but also demands a thoughtful examination of teacher development and citizenship in the digital era (Akgün & Greenhow, 2021).

In summary, the concept of authenticity now embraces a diverse and dynamic landscape. It encompasses a spectrum of materials and tasks that reflect the intricacies of both traditional and digital communication, acknowledging the need to discuss the complexities of teaching and learning in today's interconnected and technologically driven world.

With this evolving perspective on authenticity in mind, this Special Issue welcomes original research, critical, empirical or theoretical research papers, case studies, or literature reviews, theoretical papers, and practical contributions addressing, but not limited to, the following main topics:

1 The re-conceptualization of authenticity

- Critically assessing the concept of authenticity in language education and exploring its evolving nature.

- Using authentic materials in traditional settings vs. those in digital environments.

- Comparing the authenticity of language materials generated by AI to human-created content.

2 The integration of authentic digital experiences into language teaching

- Incorporating digital materials and experiences into language curriculum design to cater to diverse learners, including those with special needs or disabilities.

- Exploring the use of interactive online platforms and tools to enhance authentic language experiences but also to ensure accessibility and inclusivity for all learners.

- Investigating innovative assessment methods that align with authentic digital language learning.

3 AI and authenticity in language education

- Exploring the impact of generative AI on digital learning environments, on the authenticity of both tasks and materials.

- Evaluating the pedagogical effectiveness of AI-generated materials and tasks in achieving language learning objectives.

- Investigating how AI can personalize language teaching materials to cater to individual learner needs.

4 The intersection of AI, ethics and digital citizenship 

- Addressing the role of teachers in creating authentic and ethically sound digital language learning experiences.

- Discussing the role of digital citizenship education within digital language learning contexts.

- Promoting responsible AI use and preparing teachers for the ethical dimension of a technologically advanced linguistic landscape.

Guest-Editors:

Ana R. Luís, U. Coimbra (Executive Guest Editor)

Mónica Lourenço, U. Coimbra

Tanara Zingano Kuhn, U. Coimbra

Article Processing Charges (APC):

Authors whose special issue papers have been accepted will not be asked to pay APCs.

Submission Information:

Those interested in potentially contributing to the special issue should send the following information to both guest editors by July 1, 2024: a) a 2-page abstract (excluding references) and b) author bios (150 words max. each). Papers that pass pre-check are double-blind peer-reviewed and must adhere to the journal’s submission guidelines. Accepted papers will be published continuously in the journal, as soon as accepted.

Timeline:

• Deadline for abstract submission: September 1, 2024

• Invitation for full paper: September 15, 2024

• Full papers submission due: December 31, 2024

• Early submissions: the reviewing process begins as soon as the abstracts and manuscripts are received.

References

Akgun, S., & Greenhow, C. (2021). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI Ethics, 2, 431–440.

Buendgens-Kosten, J. (2013). Authenticity in CALL: Three domains of ‘realness’. ReCALL, 25(2), 272-285.

Cook, V. (1999) Going beyond the native speaker in language teaching.  TESOL Quarterly, 33(2), 185-209.

Gilmore, A. (2007). Authentic materials and authenticity in foreign language learning. Language Teaching, 40(2), 97-118.

Gilmore, A. (2019). Materials and authenticity in language teaching. In S. Walsh & S. Mann (Eds.), Routledge Handbook of English Language Teacher Education (pp. 299-318). Routledge.

Godwin-Jones, R. (2022). Partnering with AI: Intelligent writing assistance and instructed language learning. Language Learning & Technology, 26(2), 5–24.

Ellis, R. (2003). Task-based language learning and teaching. Oxford University Press.

Hockly, N. (2023). Artificial Intelligence in English Language Teaching: the good, the bad and the ugly. RELC Journal, 54(2), 445-451.

Ifenthaler, D. (2016). Challenging authentic digital scenarios. Tech Know Learn 21, 151–153.

Kukulska-Hulme, A. (2009). Will mobile learning change language learning? ReCALL, 21, 157-165.

Kukulska-Hulme, A., Lee, H., & Norris, L. (2017). Mobile learning revolution: implications for language pedagogy. In C. A. Chapelle & S. Sauro (Eds.), The Handbook of Technology and Second Language Teaching and Learning (pp. 217-233). Wiley & Sons.

Mishan, F. (2005). Designing authenticity into language teaching materials. Intellect.

Mishan, F. (2017). Authenticity 2.0: Reconceptualising authenticity in the digital era. In A. Maley & B. Tomlinson (Eds.), Authenticity in materials development for language learning (pp. 10-24). Cambridge Scholars Publishing.

O’Keeffe, A., & McCarthy, M. (2022). Of what is past, or passing, or to come: Corpus linguistics, changes and challenges. In A. O'Keeffe & M. McCarthy (Eds.), The Routledge Handbook of Corpus Linguistics, 2nd Edn. (pp. 1-9). Routledge.

Pack, A., & Maloney, J. (2023). Potential affordances of Generative AI in language education. Teaching English with Technology, 23(2), 4–24.

Simonsen, J. (2019). An analysis of the problematic discourse surrounding "authentic texts". Hispania, 102(2), 245-258.

Tomlinson, B., & Masuhara, H. (2017). The complete guide to the theory and practice of materials development for language learning. Wiley-Blackwell.

Van Lier, L. (1996). Interaction in the language curriculum: Awareness, autonomy and authenticity. Longman.

Widdowson, H. (1998). The theory and practice of critical discourse analysis. Applied Linguistics, 19, 136-151.