The Readiness Survey of Students in Using Artificial Intelligence for Distance Education in Higher Education
Bibliographic record
Abstract
With the rapid advancement of artificial intelligence, AI-powered learning platforms have become essential tools for college students to acquire knowledge and enhance their academic performance. However, students’ readiness to effectively integrate AI into distance learning remains critical in maximizing its benefits. This study aimed to (1) examine students’ readiness to apply artificial intelligence (AI) for distance learning in higher education and (2) compare the readiness levels between undergraduate and graduate students in distance education at Sukhothai Thammathirat Open University. The sample consisted of 445 students from 12 disciplines, including undergraduate and graduate students, selected through volunteer sampling. The research instrument was a survey assessing students’ readiness to apply AI in distance learning at the tertiary level. Data analysis included frequency, percentage, mean, standard deviation, analysis of variance (ANOVA), and content analysis. The results revealed that (1) students demonstrated high readiness to use artificial intelligence for distance learning. The findings were as follows: 1.1) Of the respondents, 61.1% were female, and 38.9% were male. Undergraduates accounted for 59.8%, while 40.2% were graduate students, with most respondents in their second year (40.4%). Most (59.8%) had previous experience using AI for educational purposes, with popular platforms being ChatGPT, Gemini, Canva AI, and Claude. 1.2) Students demonstrated a high level of readiness to use artificial intelligence for distance learning (M = 3.78, S.D. = 1.08), 1.3) Students’ understanding of artificial intelligence was moderate (M = 3.18, S.D. = 1.12), 1.4) Students’ application of AI for distance learning was moderate (M = 3.20, S.D. = 1.18) and 1.5) the ethical and legal use of artificial intelligence for learning at a high level (M = 3.56, S.D. = 1.15). (2) No significant difference was found in the readiness levels between undergraduate and graduate students in using artificial intelligence for distance education (p > 0.05). Students recommended organizing additional courses or training sessions on using artificial intelligence (AI) to enhance their knowledge, practical skills, and awareness of necessary precautions when using AI. Key focus areas include preventing misuse, upholding ethical standards in AI applications, and ensuring data security.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".