Systematic review of research on artificial intelligence applications in higher education – where are the educators?

According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education

Нові розробки в галузі штучного інтелекту та шляхи втілення даної системи навчання у Вищі навчальні заклади

Народження штучного інтелекту належить до 1950-х років, коли Джон Маккарті організував семінар на 2 місяці в Дартмутському коледжі в США, де в пропозиції до семінару Маккарті вперше використав термін "штучний інтелект" в 1956 [1] . Вивчення має ґрунтуватися на припущенні, що кожен аспект навчання в принципі може бути настільки точно описаний, що можна створити машину для його моделювання. Тоді залишиться тільки зрозуміти, як змусити машини використовувати мову, формувати абстракції та концепції, вирішувати види завдань, які зараз призначені для людей, та самовдосконалюватись

A Transparency Index Framework for Machine Learning powered AI in Education

The increase in the use of AI systems in our daily lives, brings calls for more ethical AI development from different sectors including, finance, the judiciary and to an increasing extent education. A number of AI ethics checklists and frameworks have been proposed focusing on different dimensions of ethical AI, such as fairness, explainability and safety. However, the abstract nature of these existing ethical AI guidelines often makes them difficult to operationalise in real-world contexts. The inadequacy of the existing situation with respect to ethical guidance is further complicated by the paucity of work to develop transparent machine learning powered AI systems for real-world. This is particularly true for AI applied in education and training. In this thesis, a Transparency Index Framework is presented as a tool to forefront the importance of transparency and aid the contextualisation of ethical guidance for the education and training sector. The transparency index framework presented here has been developed in three iterative phases. In phase one, an extensive literature review of the real-world AI development pipelines was conducted. In phase two, an AI-powered tool for use in an educational and training setting was developed. The initial version of the Transparency Index Framework was prepared after phase two. And in phase three, a revised version of the Transparency Index Framework was co- designed that integrates learning from phases one and two. The co-design process engaged a range of different AI in education stakeholders, including educators, ed-tech experts and AI practitioners. The Transparency Index Framework presented in this thesis maps the requirements of transparency for different categories of AI in education stakeholders, and shows how transparency considerations can be ingrained throughout the AI development process, from initial data collection to deployment in the world, including continuing iterative improvements. Transparency is shown to enable the implementation of other ethical AI dimensions, such as interpretability, accountability and safety. The 3 optimisation of transparency from the perspective of end-users and ed-tech companies who are developing AI systems is discussed and the importance of conceptualising transparency in developing AI powered ed-tech products is highlighted. In particular, the potential for transparency to bridge the gap between the machine learning and learning science communities is noted. For example, through the use of datasheets, model cards and factsheets adapted and contextualised for education through a range of stakeholder perspectives, including educators, ed-tech experts and AI practitioners

Inteligência artificial na educação e ética

Este capítulo aborda as questões éticas relacionadas com a aplicação da inteligência artificial (IA) na educação, desde os primórdios da inteligência artificial na educação, na década de 1970, até ao estado atual deste domínio, incluindo a crescente sofisticação das interfaces dos sistemas e o aumento da utilização e do abuso de dados. Enquanto nos primeiros tempos a maior parte das ferramentas estava virada para o aluno, atualmente existem ferramentas que estão viradas para o professor, apoiando a sua gestão da sala de aula, e para o administrador, ajudando-o na gestão de grupos de alunos. As ferramentas orientadas para o aluno têm agora em conta os aspetos afetivos e motivacionais da aprendizagem, bem como os aspetos cognitivos. O aumento da recolha de dados e das ferramentas de analítica da aprendizagem que lhe estão associadas tem permitido o desenvolvimento de dashboards para uma gestão dinâmica e a compreensão reflexiva dos alunos, dos professores e gestores. As questões éticas quase não tinham expressão nos primeiros tempos, mas atualmente são muito importantes. As razões devem-se aos receios legítimos de que a autonomia dos alunos e professores seja comprometida, de que os dados sejam recolhidos e desviados para outros fins, e de que a IA introduza nas decisões educacionais preconceitos adicionais aumentando a desigualdade já existente, e, também, devido à reputação assustadora que, em geral, a IA possui.info:eu-repo/semantics/publishedVersio

URI Undergraduate and Graduate Course Catalog 2019-2020

This is a downloadable PDF version of the University of Rhode Island course catalog.https://digitalcommons.uri.edu/course-catalogs/1071/thumbnail.jp

2022-2023 Catalog

The 2022-2023 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation

2023-2024 Catalog

The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation