The Impact of AI-Based Learning Tools on Student Motivation and Academic Self-Concept
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
The present research examined the effects of AI-based learning tools on the academic self-concept and the motivation of students at the university. Quantitative research design was used involving the students or 250 in total who participated in the study using structured questionnaires. Pearson correlation was used in searching the relationship between AI tools usage and motivation, regression to calculate whether it is possible to predict academic self-concept using AI tools, and moderation analysis was used to find the influence of teacher support. Correlation of these results was strongly positive resulting in AI-based learning tools and student motivation with a high correlation (the more the students use AI-based learning tools the more likely they will be motivated). Regression analysis showed that the attainment of academic self-concept is strongly predicted by the use of AI tools such that, the more the AI tool was used by the student, the more his/her self-concept of academic ability is high. Furthermore, it was discovered that the AI tool impact on motivation is forcefully elevated by teacher support. These observations demonstrate that although AI tools are useful in enhancing learning attitudes and beliefs, it is not possible to realize them fully without the need of guidance and encouragement of teachers. The versatility of the study in the category of students of different ages, faculties, and years strengthens the overall generalizability of the results. On the whole, the study indicates that the use of AI in learning tools and the high level of support of teachers may help improve the learning process of students due to its personalization, interactivity, and motivating nature.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 it