Exploring opportunities for language immersion in the posthuman spectrum: lessons learned from digital agents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Technologically-enhanced language education has shifted from computer-assisted language learning (CALL) to mobile-assisted language learning (MALL), including the use of conversational digital agents, and more recently, towards the use of generative artificial intelligence (AI) large language model (LLM) programmes for language learning purposes. This paper aims to explore the interplay between such posthuman communication and posthumanist applied linguistics, and between digital agents and human agency in response to the increasing permeation of AI in life and learning. Design/methodology/approach A core team of four researchers investigated how digital agents could be leveraged to support immersive target language learning and practice, focusing specifically on the conversational AI that pervaded digitally-mediated communication prior to the release of generative AI. Each researcher engaged in a digital autoethnography using conversational agents found in the digital wilds to learn a target second language via digital immersion. Findings Through qualitative data analysis of autoethnographic narratives using NVIVO, four key thematic codes characterizing the learning journeys emerged: context, language learning, posthuman engagement and technological parameters. The posthuman learning experiences conflicted with the multisensory, embodied and embedded ethos of posthumanist applied linguistics, indicating that informed human pedagogical agency must crucially be exercised to benefit from the learning potential of posthuman agents. Interactions with conversational agents did provide small-scale, just-in-time learning opportunities, but these fell short of immersive learning. Originality/value The methodology and findings offer a unique and valuable lens on the language learning potential of emerging LLM-based generative agents that are rapidly infusing conversational practices.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it