Threats and Opportunities of Students’ Use Of AI-Integrated Technology (ChatGPT) in Online Higher Education: Saudi Arabian Educational Technologists’ Perspectives
Bibliographic record
Abstract
This research study explored the perspectives of 20 educational technologists from four Saudi Arabian universities regarding the integration of AI-powered technology, particularly ChatGPT, into online higher education. The study used a qualitative research method that relied on the principles of theoretical sampling to select participants and conducted in-depth interviews to collect their insights. The approach taken for data analysis was thematic analysis, which uncovered a rich range of insights on both the challenges and opportunities associated with students’ use of AI-integrated technology in the context of online higher education. Ten significant challenges emerged that shed light on the complexities and intricacies of integrating AI-powered technology into educational environments. These challenges included issues related to technological infrastructure, pedagogical adaptation, and the need for comprehensive training programs to empower both teachers and learners. Additionally, eight threats were examined that highlighted concerns about data security, privacy, and potential risks associated with AI technology in educational institutions. This study not only provided a comprehensive overview of the current landscape of AI-integrated technology in Saudi Arabian higher education, but also provided valuable insights for education stakeholders, technologists, and policy makers. It underscored the necessity of proactive measures to mitigate challenges and threats while harnessing the opportunities presented by AI technology to enhance the quality and effectiveness of online higher education.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".