The Impact of AI-Driven Assessment and Feedback on Language Learning Across Cultural Contexts

Dear Colleagues,

1. Focus, Scope, and Purpose This Special Issue focuses on how Artificial Intelligence (AI)—leveraging machine learning, natural language processing (NLP), and generative AI (GenAI)—is profoundly transforming language assessment and feedback ecosystems worldwide. The scope of this issue encompasses all language education sectors (including K–12, higher education, adult, vocational, and large-scale testing contexts) across second, foreign, heritage, and Indigenous language education. We invite theoretical, empirical, and design-oriented contributions addressing themes such as:

• Cultural values and pedagogical traditions shaping the reception of automated feedback
• Comparative practices and perceptions between different global regions (e.g., Asian vs. Western contexts)
• The impact of AI-generated feedback on learner engagement, agency, and achievement
• Language educators' perspectives, adaptations, and professional development
• Design principles for culturally responsive, equitable, and fair assessment systems
• Validity, fairness, transparency, algorithmic opacity, and bias in cross-context deployment
• AI-supported evaluation of writing, speaking, reading, listening, and interactional competence

The purpose of this issue is to critically examine the pedagogical, ethical, and measurement dimensions of automated systems in language classrooms. It seeks to illuminate how culturally embedded practices, language ideologies, and institutional structures dictate the actual impact of AI on teaching and learning.

2. Supplementing Existing Literature While recent literature highlights the massive technical capabilities of Large Language Models (LLMs) to simulate human dialogue and scale up real-time feedback (Godwin-Jones, 2024; Warschauer & Xu, 2024), current research often treats these tools as culturally neutral. However, feedback practices are deeply influenced by local pedagogical norms, power dynamics, and teacher authority (Carless, 2006; Ajjawi & Boud, 2017; Yu & Yang, 2021). What proves highly effective in Western higher education may be intrusive or counterproductive in hierarchical or exam-oriented systems (Gregersen & Mercer, 2022). Furthermore, systemic issues like algorithmic opacity and the underrepresentation of minoritized languages risk exacerbating educational inequalities (Baker & Hawn, 2022; Bender et al., 2021). This Special Issue usefully supplements the existing literature by shifting the narrative from purely technological affordances to context-sensitive, cross-cultural, and interdisciplinary evaluations. By grounding AI deployment in local sociocultural realities, this collection advances the field toward more equitable, responsive, and pedagogically sound language assessment systems.

3. Submission Guidelines We request that, prior to submitting a manuscript, interested authors initially submit a proposed title and an abstract of 400 words summarizing their intended contribution. Please send it to the guest editors (eun.jang@utoronto.ca) or to Languages editorial office (languages@mdpi.com). Abstracts will be reviewed by the guest editors for the purposes of ensuring proper fit within the scope of the special issue. Full manuscripts will undergo double-blind peer-review.

4. References

• Ajjawi, R., & Boud, D. (2017). Examining the nature and effects of feedback dialogue. Assessment & Evaluation in Higher Education, 42(2), 252–265.
• Baker, R. S., & Hawn, A. (2022). Algorithmic bias in education. International Journal of Artificial Intelligence in Education, 1–41.
• Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623.
• Carless, D. (2006). Differing perceptions in the feedback process. Studies in Higher Education, 31(2), 219–233.
• Godwin-Jones, R. (2024). Distributed agency in language learning and teaching through generative AI. Language Learning & Technology, 28(2), 5–30.
• Gregersen, T., & Mercer, S. (Eds.). (2022). The Routledge handbook of the psychology of language learning and teaching. Routledge.
• Warschauer, M., & Xu, Y. (2024). Generative AI for language learning: Entering a new era. Language Learning & Technology, 28(2), 1–4.
• Yu, R., & Yang, L. (2021). ESL/EFL learners' responses to teacher written feedback: Reviewing a recent decade of empirical studies. Frontiers in Psychology, 12, 735101.

Prof. Dr. Eunice Eunhee Jang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Languages is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• language assessment
• assessment feedback
• AI
• GenAI
• ML
• languages
• teaching and learning
• cultural contexts