Impact of Sustainability Awareness on AI Perception, Engagement, and Motivation among University-Level Literature Students
Bibliographic record
Abstract
AI is gaining recognition as a strategic tool in education, helping to reduce student workload and enhance learning. As technology rapidly evolves, institutions in the United Arab Emirates and the Kingdom of Saudi Arabia are actively integrating it into academic environments to better support students. As a result, it is crucial to examine its current impacts on students’ academic journey. This research aimed to examine the effect of sustainability awareness on academic aspects of higher education students in the GCC region. In other words, the focus remained on analyzing the effects of sustainability awareness on improving learning engagement, Perceptions of AI, and Educational Motivation among English language learners in the UAE and Kingdom of Saudi Arabia. Theoretically supported by Constructivist Learning Theory, data were gathered from 171 students using a random sampling technique from the selected institutions. Results show that sustainability awareness positively affects perceptions of AI in education, implying that students perceive AI as a useful tool for improving their understanding of sustainability. The effect of sustainability awareness on learning engagement remained significant, indicating that integrating sustainability topics into coursework improves students' interest and enhances their overall engagement in learning. Finally, the effect of sustainability awareness on educational motivation was also significant. This shows that sustainability-related learning promotes academic motivation, encouraging students to aim higher academically and giving them a stronger sense of purpose in education. Thus, it is concluded that institutions can improve this awareness by embedding sustainability-focused projects, reflective activities, and interactive tools within the curriculum. This approach not only reinforces the positive impact of AI in achieving sustainable development goals but also aligns with constructivist principles, where students actively connect new knowledge to real world issues, leading to the most constructive educational outcomes.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".