Empowering undergraduates through machine learning

Urtasun, Ainhoa

doi:10.1177/09504222221124498

Cited by 2 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to scholarly works which examine the potential impact on student attainment, such as Ramo et al (2022), Saad and Tounkara (2023), and Urtasun (2022), other scholars have examined questions such as student motivation (Martín-Núñez et al, 2023), learner drop out (e.g., Bañeres et al, 2023;Nagy & Molontay, 2023), and student performance prediction (Jiao et al, 2022).…”

Section: Effects Of Genai On He Student Learningmentioning

confidence: 99%

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

Bannister,

Santamaría-Urbieta,

Alcalde-Peñalver

2023

AULA_ABIERTA

View full text Add to dashboard Cite

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.

show abstract

Section: Effects Of Genai On He Student Learningmentioning

confidence: 99%

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

Bannister,

Santamaría-Urbieta,

Alcalde-Peñalver

2023

AULA_ABIERTA

View full text Add to dashboard Cite

show abstract

Exploring Teacher Awareness of Artificial Intelligence in Education: A Case Study from Northern Cyprus

Güneyli,

Burgul,

Dericioğlu

et al. 2024

EJIHPE

View full text Add to dashboard Cite

This study investigates the level of awareness among teachers regarding the use of artificial intelligence (AI) in education, focusing on whether this awareness varies according to socio-demographic characteristics, access to technology, and specific knowledge and beliefs about AI. Conducted in Northern Cyprus during the 2023–2024 academic year, this study employed a survey model with purposive and snowball sampling methods, involving 164 teachers. Teachers at different levels, namely, primary school, secondary school, high school, and university, were included in this study. The “Artificial Intelligence Awareness Scale”, developed by Ferikoğlu and Akgün (2022), was used to measure AI awareness. Data normality was verified through skewness and kurtosis values, allowing for parametric statistical tests such as t-tests, one-way ANOVA, logistic regression, and chi-square analysis. This study explored the distribution of AI use across different school types and educational levels and assessed the impact of sub-dimensions of AI awareness on its application in teaching. Findings revealed no significant influence of teacher demographics (age, gender, education level, type of school, institution level, and monthly income) on AI awareness. However, usage patterns indicated that university lecturers were more likely to incorporate AI in their teaching, followed by primary and high school teachers, with secondary school teachers using it the least. A Multilayer Neural Network Analysis identified practical knowledge as the most critical factor influencing the use of AI in teaching (importance weight of 0.450), followed by beliefs and attitudes (0.298), relatability (0.148), and theoretical knowledge (0.104). These results highlight the importance of practical knowledge for fostering AI integration in educational practices, underscoring significant implications for teacher training and professional development programs.

show abstract

Empowering undergraduates through machine learning

Cited by 2 publications

References 25 publications

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

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

Exploring Teacher Awareness of Artificial Intelligence in Education: A Case Study from Northern Cyprus

Contact Info

Product

Resources

About