Supporting students and educators in using generative artificial intelligence
Notice bibliographique
Résumé
The use of generative artificial intelligence (genAI) in university settings is a current topic of debate, with a range of viewpoints regarding the extent to which these tools should be used by students (Ahmad et al., 2023) and the potential applications of genAI tools in higher education (Yu & Guo, 2023). Concerns have also been raised regarding the potential student misuse of genAI tools, and the ability of these tools to score a passing grade in some university subjects (Nikolic et al., 2023). RMIT University’s position is that we must build the capability in our students to engage with AI as part of the current and future requirements of work. The RMIT units responsible for academic quality and for education innovation have created a set of statements that educators can choose from when designing assessment tasks. These statements include there being no restrictions on the use of genAI tools in the assessment task, that genAI tools can be used with limitations, or that genAI tools cannot be used. If students are permitted to use genAI tools in assessment tasks, they must appropriately acknowledge and reference the use of these tools and their outputs. In the library, we were tasked with creating citing and referencing guidelines for AI-generated content for each of the styles used at our institution, including APA 7th, IEEE, Chicago 17th and AGLC4. A challenge of this project was that there was either no specific genAI referencing advice provided by the style manual editors, or the advice was limited to a specific tool, e.g. ChatGPT in the case of APA 7th (McAdoo, 2023) and Chicago 17th (The Chicago Manual of Style Online, n.d.). We adapted the existing style advice for referencing software for the APA 7th, Harvard, Chicago 17th, and IEEE styles, the advice for referencing internet sources for Vancouver, and the advice for referencing personal correspondence for AGLC 4. We created referencing guidelines for both AI-generated text and images, as well as when genAI was used for background research. We also incorporated current Australian copyright advice into these guidelines, in which authorship can only be granted to human creators, and so the creator of the tool was used as the author rather than the tool itself. These guidelines are housed in a subject guide (RMIT, 2023) which has received more than 17,000 views between February and July 2023. We also updated our Academic Integrity Awareness (AIA) microcredential to include educative information about genAI tools. We included guidance relating to the inaccurate information and ethical concerns in some of the current tools, as well as placing these tools within the overall context of academic integrity. This microcredential is used as a component of assessment tasks in many disciplines across our institution. These resources assist students in maintaining academic integrity when using genAI tools in their learning, and when using genAI in their future careers, as they reinforce the central requirement that the work of others (including work that is AI-generated) is appropriately acknowledged. These resources will continue to be updated as genAI tools evolve and become more widely used within learning.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,026 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,008 |
| Communication savante | 0,020 | 0,012 |
| Science ouverte | 0,002 | 0,012 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».