Abstract
Several disciplines are being significantly impacted by different types of AI technologies. One of these disciplines is foreign language teaching and in particular teaching students L2 text production, Simonsen (2021).
This article discusses how teachers can use AI Text Generators (ATGs) in foreign language classes with a particular focus on L2 text production, Simonsen (2021). In this article, ATGs are seen as language technological resources.
The article is based on insights from an empirical study investigating how students and professionals work with a selected ATG and what potential they see ATGs might have in a pedagogical context. The seventy test subjects first worked with a specific ATG conducting three writing operations and afterwards they participated in an online questionnaire with questions about the potential use of ATGs in teaching and learning. Finally, the data were thematically analysed by means of the qualitative statistical analysis tool NVIVO.
The data seem to suggest that ATGs indeed can be used as a powerful resource in foreign language classes. Most of the test subjects in fact found that the ATG in question was easy to use when producing texts, but the data also suggest that the test subjects found the quality of the ATG-generated content to be below standard and that they had to perform several editing operations before, during and after the automatic text generation. The data also seem to indicate that ATGs can be used as powerful text production facilitators and that they may even help students overcome writer's block, help them structure, and develop written assignments.
Based on these insights, the article presents a lexicographical-pedagogical framework for using ATGs in L2 text production building on Leroyer & Simonsen (2018), Simonsen (2021), Simonsen (2022) and Simonsen & Viberg (2022). The framework uses a Backward-Design approach, Wiggins et al. (1998) and presents a lexicographical-pedagogical framework, which aligns specific learning outcomes with specific learning activities using specially selected lexicographical data generated by specific text production operations by the ATG.
All this of course raises several concerns and important ethical, pedagogical, and didactical questions, which will require educators to rethink how they teach, assess, and use technology, Sharples (2022).
Select Bibliography:
Leroyer, Patrick & Simonsen, Henrik Køhler (2018): When Learners Produce Specialized L2 Texts: Specialized Lexicography between Communication and Knowledge. In: Proceedings of XVIII EURALEX International Congress.
Sharples, Mike (2022): New AI tools that can write student essays require educators to rethink teaching and assessment. In: https://blogs.lse.ac.uk/impactofsocialsciences/2022/05/17/new-ai-tools-that-can-write-student-essays-require-educators-to-rethink-teaching-and-assessment/ [Accessed 18 June 2022].
Simonsen, Henrik Køhler (2021) : AI Writers in Language Learning. In: 2021 International Conference on Advanced Learning Technologies, 238-240. https://ieeexplore.ieee.org/document/9499836/ [Accessed 18 June 2022].
Simonsen, Henrik Køhler (2022): AI Text Generators and Text Producers. In: 2022 International Conference on Advanced Learning Technologies [Forthcoming].
Simonsen, Henrik Køhler & Olga Viberg (2022): Supporting Second Language Learners through SKANDIBOT: A Lexicographical Design Approach. In: 2022 International Conference on Advanced Learning Technologies [Forthcoming].
Wiggins, Grant, and McTighe, Jay. (1998). Backward Design. In Understanding by Design (pp. 13-34). ASCD.
