Editorial: Impact of generative AI on teacher-student interaction
Notice bibliographique
Résumé
The use of generative AI in education is now being explored by various educational institutions.Experiments are increasingly taking place in universities as well as in schools.However, what I find most interesting about this recent introduction is the fact that the first thought is generally that the machine could replace the human.Indeed, it seems to us that this could happen, while it is quite obvious that in most of the experiments the direct relationship between teacher and student remains in all interactions, sometimes supplemented by the support of the AI machine, sometimes guided by the logic of human-machine hybridization, or of the AI co-pilot helping the teacher to carry out his or her task with more information and suggestions than he or she may have had in previous years.This kind of affirmation of the human-human/teacher-learner interaction leads me to understand which studies could provide interesting insights on this topic.Indeed, generative AI (GenAI) is increasingly influencing teacher-student interactions in higher education, presenting both opportunities and challenges.Yeralan and Lee (2023) highlight its widespread use in creating assignments and enhancing self-directed learning, or it can be useful for tasks such as brainstorming and initial drafting.Moy and Feldstein (2024) emphasize the need for professional development workshops to help faculty and students effectively integrate GenAI into teaching and learning.The study also explores how rapid advances in AI are changing the way students and faculty interact with content and each other.McGill and McGill (n.d.) explore its role in engineering education, noting improved student performance when using AI tools for example generation.Following the professor's example conversations, students can ask the chatbot to generate more examples, and the chatbot also attempts to provide and explain solutions.Sekli et al. ( 2024) provide a comprehensive review of GenAI applications, highlighting their potential to improve engagement and personalized learning.Farrelly and Baker ( 2023) discuss concerns about academic integrity and bias, particularly for international students.They emphasize that in order to equip students with the skills, knowledge, and competencies that will allow them to thrive in the 21st century, we must rapidly adapt our programs to incorporate AI literacy and competence across disciplines.Lodge (2024) presents findings from interviews with students suggesting that the integration of GenAI into learning practices varies.Ilieva et al. (2023) propose a framework for using AI chatbots to enhance interactivity and feedback.The results of their study indicate that a significant number of students are aware of the educational potential of this emerging AI technology and have used it.In addition, a significant majority of students) indicated an intention to use AI chatbots and reported satisfaction with generative AI technologies.Chiu (2024) calls for research into the transformative potential of GenAI in education.In most of the papers, there are measures related to privacy and ethics.These may be a solution over time, if a common line is not developed in the development of AI tools dedicated to plagiarism detection and text development and revision.
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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,003 |
| 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 ».