Exploring the impact of Artificial Intelligence on students' skills for sustainable development in education
Bibliographic record
Abstract
Introduction In today's rapidly evolving technological landscape, the integration of artificial intelligence (AI) into education is increasingly inevitable. AI has the potential to improve educational quality and, in turn, contribute to sustainable development. This study investigates the effects of selected AI tools; Grammarly, ChatGPT, QuillBot, Textero AI, and ChatPDF, on the quality of education and examines students' perceptions of their influence on self-directed learning, problem-solving, critical thinking, and digital literacy skills. Methods A quantitative research design was adopted. Data were collected through a structured questionnaire and analyzed statistically using SPSS. Purposive sampling was employed, targeting 78 students enrolled in computer-aided translation courses over two semesters. Results The findings indicated that students perceived multiple AI tools as beneficial in enhancing their learning skills, although the degree of impact varied across tools. Overall, the selected AI tools positively influenced skills associated with quality education, including self-directed learning, problem-solving, critical thinking, and digital literacy. Discussion The study demonstrates that AI tools can significantly contribute to the enhancement of educational quality. These findings underscore the importance of integrating AI technologies into teaching and learning processes to foster sustainable education. Future research should explore strategies for optimizing the use of AI tools in various educational contexts to maximize their potential benefits.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".