Amelioration of Google Assistant – A Review of Artificial Intelligence Stimulated Second Language Learning and Teaching
Bibliographic record
Abstract
Artificial Intelligence (AI) has become an undeniable technological innovation in our world. The usability of AI-powered applications has gradually increased in all fields. In need of change and adaptability in life, many AI tools have been developed to accomplish certain tasks faster. AI-featured Intelligent Personal Assistant (IPA) applications like Google Assistant (GA), Alexa, Siri, Cortona, and Bixby are involved in the process of helping humankind to achieve certain actions in a faster mode. The evolvement of Industry 4.0 and Education 4.0 triggers, as well as, challenges the language curriculum to adapt AI-based applications to engage in second language learning. Among all the above-mentioned AI-featured applications, Google Assistant predominantly involves in language learning and teaching. The main objective of this paper is to review the Al-powered Google Assistant for teaching and learning languages. It specifically reviews and examines the study on the use of Google Assistant in terms of teaching and learning a language. The approach used to evaluate the articles pulled from pertinent databases is the qualitative research method, especially content analysis. The findings of the study show that there are four distinct patterns in which AI-powered Google Assistant is used to teach and learn languages. The endorsement of AI-powered Google Assistant and pedagogy based on it proves that it is very helpful for second language acquisition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".