Criteria of quality in fiction-based research to promote debate about the use of AI and robots in Higher Education

Cox, Andrew; Cameron, David; Checco, Alessandro; Herrick, Tim; Mawson, Maria; Steadman-Jones, Richard

doi:10.1080/07294360.2022.2087603

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…Again addressing RQ1, the multiple affordances of GenAI-facilitated pedagogical innovation in HE are documented in a number of publications, such as Archibald et al ( 2023), Chang et al (2022), Cox et al (2022), Razia et al (2022), Zhang (2022), andZhou (2022), which detail advantages such as content generation for lectures or assessments, assisted data analysis, and greater administrative efficiency. Ghnemat et al (2022) take a similar approach and make a convincing call for further investigation into AI-based learning.…”

Section: Implications Of Genai For He Instructionmentioning

confidence: 99%

A Systematic Review of Generative AI and (English Medium Instruction) Higher Education

Bannister,

Santamaría-Urbieta,

Alcalde-Peñalver

2023

AULA_ABIERTA

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This systematic review investigates the current state of research on Generative Artificial Intelligence (GenAI) and its implications for (EMI) Higher Education. The study employs a methodology based on an evidence-informed and theoretically credible framework to answer two research questions: (1) What studies of relevance to (EMI) Higher Education have been published thus far, considering the most recent developments of GenAI? and (2) Which key areas are currently lacking in extant literature and in need of further scholarly exploration in this regard in (EMI) Higher Education research? The results of the study reveal a limited number of pertinent publications, indicating a sparse scholarly landscape with a dearth of work on the implications of Generative AI in EMI Higher Education. Based on these findings, preliminary recommendations have been made to guide future research in this area. This study contributes to the literature by highlighting the need for further research on the potential of GenAI to enhance the teaching and learning experience in (EMI) Higher Education and provides a theoretical framework to guide future research. These findings may inform researchers and educators interested in exploring how GenAI may be leveraged from different educational perspectives.

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