Chatting with Al Capone: Leveraging virtual conversational agents in emotional landscapes
Bibliographic record
Abstract
This article explores the extent to which virtual conversational agents generated by artificial intelligence (AI) are being leveraged by language learning apps to develop social, emotional and intercultural skills in language learners. While the affordances of natural language processing (NLP) technology are well-documented in the cognitive domains of language learning (e.g. vocabulary acquisition, grammar), significantly less research has explored how learners’ interactions with virtual conversational agents can build socio-emotional and intercultural competence. We engage in dialogues within three popular apps (Duolingo, ImmerseMe and MakesYouFluent) to determine how these apps are equipping language learners to navigate emotional landscapes. We conclude that app developers and instruction designers are under-valuing the role of social, emotional, and intercultural learning in second language acquisition and/or are underusing the potential of AI and NLP to develop competence in these areas
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".