Learner Experiences of Mobile Apps and Artificial Intelligence to Support Additional Language Learning in Education
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
This study examines learners’ experiences and the use of language learning applications (“apps”) as a primary source of second or additional language learning (“L2”) instruction and assessment in higher education. It purviews the integration of artificial intelligence (AI)-powered features that support technology-enhanced language learning experiences. Principles of pedagogy, heutagogy, and self-determination theory are used to inform the appropriate design and application of AI to support language learning. We examine the congruence between learner's goals with perceived outcomes following a 4-week language learning intervention using an app. A survey of n = 151 adult learners across two Canadian universities revealed: (a) apps are perceived as an engaging, convenient, and structured approach to early stages of L2 learning and (b) the integration of AI for conversation-based simulations or speech recognition would enable more adaptive, personalized L2 learning experiences. The authors discuss implications for future developments and AI uptake for language learning apps.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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