Tag: Artificial Intelligence

Technostress and English language teaching in the age of generative AI

Kohnke, L., Zou, D., & Moorhouse, B. L. (2024). Technostress and English language teaching in the age of generative AI.
Educational Technology & Society, 27(2), 306-320.

This article offers a description, examination and discussion of “technostress” in the field of language instruction.  The authors describe technostress as “…a current-day affliction among  teachers that impacts their ability to adjust and respond healthily to the increased use of innovative technologies” (p.307).

The context of the study is EFL instruction in Hong Kong, in relation to the integration of Artificial Intelligence (AI) in the language learning field. However, the interview responses of the participant instructors, and the discussion of the factors that influence technostress and strategies to alleviate its impact have significant relevance for the ESL community, including instructors, programs and curriculum designers, who are all likely to encounter similar challenges in relation to the integration of a range of digital technologies, including AI tools such as ChatGPT in language instruction.

The authors outline five factors that influence technostress as described by Tarafdar (2019). Briefly, these are: Techno-complexity (the need to constantly learn how to use new technologies), Techno-overload (the proliferation of technology causes us to modify our work habits etc.), Techno-invasion (the need to use personal time to learn about new technologies etc.), Techno-insecurity (anxiety that new technologies will lead to job loss), Techno-uncertainty (uneasiness due to the frequent changes and updates to technology tools, etc.).

Based on the analysis of the qualitative date in this study and a review of the literature, the authors discuss strategies to alleviate technostress. These include the following:

Enabling instructors to develop or enhance Technological Pedagogical Content Knowledge (TPACK) to support effective integration of technology. 

Online Engagement, whereby instructors have opportunities to share knowledge about the integration and use of technology with their peers and colleagues in collaborative communities of practice.

Gradual and realistic expectations, whereby the integration of new technologies is incremental.  

Institutional support and clear policies and incentives so that instructors have a clearer understanding of expectations and access to professional learning opportunities.

Retrievable from:

https://www.researchgate.net/publication/378774496_Technostress_and_English_language_teaching_in_the_age_of_generative_AI

AI and English language teaching: Affordances and challenges

Crompton, H., Edmett, A., Ichaporia, N., & Burke, D. (2024). AI and English language teaching: Affordances and challenges. British Journal of Educational Technology, 00, 1–27. https://doi.org/10.1111/bjet.13460

This article provides a systematic literature review focused on how Artificial Intelligence (AI) is currently being used in English Language teaching and learning. Based on this review the article also provides an analysis and discussion of affordances of AI, and identifies potential  limitations and challenges presented by AI in English language teaching and learning. The authors outline themes and issues requiring future research to support  the effective use of AI for English language teaching and learning at all learning levels.

The literature review demonstrated that AI is currently more likely to be used to support the learning in relation to speaking (primarily pronunciation) and writing  (primarily vocabulary and grammar) skills. The literature review indicated that AI is used less  in relation to reading,  and the authors posit that this may be due to the considerable  affordances of natural language processing which are  more pertinent in the areas of speaking and writing. The review also uncovered the potential of AI- powered tools including chatbots, on platforms such as Duolingo, Memrise and Mondly, to support learners in self-regulation, to become more autonomous learners, and to reduce anxiety, and increase confidence  in speaking English.

Based on their analysis of the literature the authors conclude that although there is solid evidence of promising affordances of AI, further research is required to better understand the implications of the use of AI in English language instruction. They contend that there is a  clear gap in the research in relation to challenges and limitations of AI, and identified the following areas for future research: technology breakdowns, concerns relating to personal cyber security, variable quality of AI-powered tools, and a potential over-standardisation of language.

Finally ,the authors point to the critical importance of enabling and supporting English language instructors to understand AI, how to evaluate AI-powered tools, and how to make effective use of these tools for language teaching and learning.

Retrievable from: https://bera-journals.onlinelibrary.wiley.com/doi/full/10.1111/bjet.13460

Conversational agents in language learning

Xiao, F.,, Zhao, P., Sha, H., Yang, D. and Warschauer, M. (2023) Conversational agents in language learning Journal of China Computer-Assisted Language Learning, 2023. 1-26.

This article provides a description and the findings of a scoping review of the use of conversational agents. The review includes some familiar commercial ones such as  Siri, Alexa and Google Assistant and others which are programmable such as Google’s Dialogflow and Amazon Lex. The authors define a scoping review as “a method of systematically identifying, mapping and summarising the available literature on a specific research topic” (p.3 ). It focuses on their use and potential use in language teaching and  learning, along with suggestions for future research to examine their potential to support language teaching and learning.

The scoping review indicated that such conversational agents are currently being used in language learning in three broad areas. The first is for general communication practice in which language learners interact directly with the conversational agent. The second is task-based language learning in which learners are required to interact with the conversational agent in the context of specific tasks. The third is structured pre-programmed dialogue whereby  conversational agents are designed and programmed by researchers and instructors to conduct dialogues with learners on specific topics.

Findings  from the literature identified in the scoping review show that conversational agents provide opportunities for learners to engage in conversation in the target language, thus providing useful learning opportunities both in and outside the traditional language learning classroom . The literature also indicates that generally language learners find this engagement useful in supporting and improving their language learning.  Some of the studies reviewed suggest that conversational agents can be useful as  diagnostic tools.  The review identified a significant gap in the research literature in relation to the educator perspective on the use of conversational agents and the authors suggest that  a focus on the educator perspective will be crucial in ongoing development and implementation of such agents.

Retrievable from: https://www.degruyter.com/document/doi/10.1515/jccall-2022-0032/html