Generative AI in higher education psychology programs: a scoping review exploring the opportunities for its use in assessment methods
Notice bibliographique
Résumé
The current literature on Generative Artificial Intelligence (GenAI) in tertiary settings primarily focuses on the risk it poses to academic integrity, and ways to reduce or remove GenAI use in assessments. As psychology graduates enter a workforce where GenAI is present, educators need to prepare students to use GenAI responsibly. This scoping review aims to assess the current state of knowledge in tertiary psychology regarding opportunities for integrating GenAI into assessment methods. A comprehensive literature search identified four studies for inclusion. These were published in Australia, Canada, Switzerland, and the United States, and included two quantitative case studies, a mixed-method case study, and a pedagogical case study. Three themes were generated: 1) GenAI can be used as an effective psychology tutor, 2) GenAI can be used for authentic assessment in undergraduate psychology, and 3) Critiquing GenAI as a form of assessment can enhance student learning and AI literacy. Only four studies were identified, but all indicate that GenAI can be meaningfully incorporated into psychology assessments. However, this is an underdeveloped area and ongoing research with a particular focus on developing evidence-based assessment methods which adapt to the evolving GenAI landscape is needed. What is already known about this topic:Generative Artificial Intelligence (GenAI) has disrupted the education sector, with educators re-evaluating their teaching practices and tertiary institutions revising their policies.Both educators and students use and appreciate the benefits of GenAI for administrative tasks and reducing cognitive load; however, there are ongoing challenges and concerns about its role in student assessment tasks.In Australian psychology programs, where students must meet specific standards under accreditation, educators have implemented bans or adopted alternative assessment methods that minimise or completely remove its capability. Generative Artificial Intelligence (GenAI) has disrupted the education sector, with educators re-evaluating their teaching practices and tertiary institutions revising their policies. Both educators and students use and appreciate the benefits of GenAI for administrative tasks and reducing cognitive load; however, there are ongoing challenges and concerns about its role in student assessment tasks. In Australian psychology programs, where students must meet specific standards under accreditation, educators have implemented bans or adopted alternative assessment methods that minimise or completely remove its capability. What this topic adds:This scoping review is the first to report the findings of a comprehensive search that focus on the opportunities of integrating GenAI into tertiary psychology student assessment methods.The findings offer examples and suggestions for using GenAI as a personal tutor, incorporating and critically evaluating GenAI to develop students’ artificial intelligence literacy, and illustrating how it can be drawn on to develop authentic assessments.We highlight opportunities for future research, given the dearth of work to date, focusing on the need to increase the evidence-base and for designing adaptable assessment methods that evolve with GenAI. This scoping review is the first to report the findings of a comprehensive search that focus on the opportunities of integrating GenAI into tertiary psychology student assessment methods. The findings offer examples and suggestions for using GenAI as a personal tutor, incorporating and critically evaluating GenAI to develop students’ artificial intelligence literacy, and illustrating how it can be drawn on to develop authentic assessments. We highlight opportunities for future research, given the dearth of work to date, focusing on the need to increase the evidence-base and for designing adaptable assessment methods that evolve with GenAI.
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 | 0,030 | 0,114 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,016 | 0,018 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,007 | 0,006 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».