Perceptions and Challenges of Using Artificial Intelligence (AI) Among Primary School Leaders and Teachers: A Case Study at Kinabatangan Sabah
Bibliographic record
Abstract
The Fourth Industrial Revolution (IR 4.0) has introduced significant transformations in the education sector through the integration of artificial intelligence (AI) technologies, which have the potential to enhance the effectiveness of teaching and learning processes, streamline school administration, and accelerate student assessment procedures. However, the level of AI adoption in Malaysian education remains limited, particularly in rural areas such as Kinabatangan, Sabah. This situation is influenced by various challenges, including inadequate infrastructure, and insufficient institutional support. Therefore, this study aims to explore the perceptions of primary school teachers and school administrators in Kinabatangan regarding the use of AI, identify the key challenges encountered, and analyse the factors influencing the acceptance and utilisation of AI within a rural education context. This study adopts a qualitative research approach using semi-structured interviews involving ten teachers and school administrators from several primary schools in Kinabatangan. The data collected are analysed using thematic analysis to identify recurring patterns and key themes related to the acceptance of AI and the challenges associated with its implementation in rural educational settings. The findings indicate varying levels of AI awareness among teachers and school administrators. While some participants perceive AI as a beneficial innovation, others remain hesitant or insufficiently prepared to adopt it in their professional practices. Key challenges identified include a lack of professional training related to AI, limitations in digital infrastructure such as poor internet connectivity, and difficulties in integrating AI technologies with traditional teaching methods. - The findings also suggest that administrative support plays a crucial role in determining the effectiveness of AI implementation, either facilitating or hindering its adoption. This study contributes to policymakers, school administrators, teachers, as well as parents and students by providing empirical insights into the challenges and potential of AI integration in rural education. The findings may also serve as a foundation for future strategies and initiatives aimed at strengthening the use of technology within the Malaysian education system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".