Exploring the Role of GenAI Tools on Student Motivation and Communicative Competence in the Spanish Classroom
Bibliographic record
Abstract
This study explores the integration of Generative Artificial Intelligence (GenAI) in an advanced Spanish language course, with a focus on learner motivation and communicative competence. Although GenAI tools, such as chatbots and adaptive feedback systems, have been increasingly implemented in higher education, their application in Spanish as a Foreign Language (SFL) classroom remains under-researched. The research involved 41 students from two sections of an advanced Spanish course at a Canadian university, one for foreign language learners and one for heritage speakers. Participants completed a pre-task questionnaire, engaged in a GenAI supported collaborative task, and reflected on their experiences through a post-task survey. The primary task involved using ChatGPT to gather cultural information about a Spanish-speaking country and create a promotional campaign. Data were collected using both quantitative (Likert-scale surveys) and qualitative (open-ended reflections) methods. Findings indicate that most students felt comfortable and motivated when using GenAI tools and perceived them as useful for accessing real-time feedback and supporting personalized learning. The task was described as engaging and well-organized, although some students found it too simple to fully challenge their language abilities. Participants also identified limitations, including concerns about content accuracy, overreliance, and the lack of cultural and emotional nuance in GenAI outputs. The study underscores the importance of task design, GenAI literacy, and the instructor’s role when implementing GenAI tools in language instruction. It advocates for a hybrid approach that combines human expertise with GenAI capabilities to foster motivation, learner autonomy, and communicative competence, addressing an underexplored gap in the literature.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".