The Readiness Survey of Students in Using Artificial Intelligence for Distance Education in Higher Education
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».