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

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Olaf Zawacki-Richter ◽  
Victoria I. Marín ◽  
Melissa Bond ◽  
Franziska Gouverneur
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Systematic Review ◽  
Adaptive Systems ◽  
Teaching And Learning ◽  
Quantitative Methods ◽  
Empirical Studies ◽  
Tutoring Systems ◽  
Administrative Services ◽  
Artificial Intelligence In Education

Abstract 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.

Bibliographical Analysis of Artificial Intelligence Learning in Higher Education

2021 ◽  
pp. 36-52
Author(s):  
Mohammed Ali ◽  
Mohammed Kayed Abdel-Haq
Keyword(s):  
Higher Education ◽  
Support Services ◽  
Adaptive Systems ◽  
Quantitative Methods ◽  
Intelligent Tutoring ◽  
Academic Support ◽  
Empirical Studies ◽  
Tutoring Systems ◽  
Assessment And Evaluation ◽  
Administrative Services

This chapter provides an overview of research on AI applications in higher education using a systematic review approach. There were 146 articles included for further analysis, based on explicit inclusion and exclusion criteria. The findings show that Computer Science and STEM make up the majority of disciplines involved in AI education literature and that quantitative methods were the most frequently used in empirical studies. Four areas of AI education applications in academic support services and institutional and administrative services were revealed, including profiling and prediction, assessment and evaluation, adaptive systems and personalisation, and intelligent tutoring systems. This chapter reflects on the challenges and risks of AI education, the lack of association between theoretical pedagogical perspectives, and the need for additional exploration of pedagogical, ethical, social, cultural, and economic dimensions of AI education.

scholarly journals The Potential of Artificial Intelligence in Higher Education

2021 ◽  
pp. 1-4
Author(s):  
Helen Crompton ◽  
Donggil Song
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Teaching And Learning ◽  
Faculty Members ◽  
Computing Systems ◽  
Internet Applications ◽  
Tutoring Systems ◽  
Ethical Implications ◽  
Use Of Data ◽  
Automated Grading

Artificial Intelligence (AI) is seeping into many aspects of our everyday lives, with common internet applications, smartphones and even household appliances. Within education, AI is a rapidly emerging field and there is a strong potential for AI to greatly extend and enhance teaching and learning in higher education (Crompton et al., 2020). AI is defined as “computing systems that are able to engage in human-like processes such as learning, adapting, synthesizing, self-correction and use of data for complex processing tasks” (Popenici & Kerr, 2017). In the Horizon Report 2020 report (Brown et al., 2020), AI is listed as one of six technologies with the potential for high impact in higher education. The Horizon Report (2020), is an annual publication that examines the major trends in educational technology that are shaping global higher education. This paper will highlight some of the ways AI is supporting both students and faculty members including bespoke learning, intelligent tutoring systems, facilitating collaboration, and automated grading. This is followed by a section on ethical implications.

scholarly journals Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021

AI and Ethics ◽  
2021 ◽  
Author(s):  
Muhammad Ali Chaudhry ◽  
Emre Kazim
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Tutoring Systems ◽  
Policy Makers ◽  
Tutoring Systems ◽  
Research And Practice ◽  
The Past ◽  
Artificial Intelligence In Education ◽  
Potential Impact ◽  
High Level ◽  
Contextualized Learning

AbstractIn the past few decades, technology has completely transformed the world around us. Indeed, experts believe that the next big digital transformation in how we live, communicate, work, trade and learn will be driven by Artificial Intelligence (AI) [83]. This paper presents a high-level industrial and academic overview of AI in Education (AIEd). It presents the focus of latest research in AIEd on reducing teachers’ workload, contextualized learning for students, revolutionizing assessments and developments in intelligent tutoring systems. It also discusses the ethical dimension of AIEd and the potential impact of the Covid-19 pandemic on the future of AIEd’s research and practice. The intended readership of this article is policy makers and institutional leaders who are looking for an introductory state of play in AIEd.

scholarly journals A Preliminary Scoping Review Study of the Progress of Social Media Adoption as an Educational Tool by Academics in Higher Education

2018 ◽  
Vol 2 ◽  
Author(s):  
Eva Perez ◽  
Mairead Brady
Keyword(s):  
Higher Education ◽  
Systematic Review ◽  
Social Media ◽  
Scoping Review ◽  
Academic Staff ◽  
Empirical Studies ◽  
Pedagogical Issues ◽  
Educational Tool ◽  
Scoping Study ◽  
Time Investment

This paper presents a preliminary scoping review exploring the evidence landscape regarding academic staff experiences and perceptions of social media adoption as an educational tool in higher education. The goal of this paper is to examine 10 empirical studies of social media adoption in teaching and learning by academics in preparation for a proposal for a systematic literature review. Consequently, this scoping study assisted in the development of a review protocol which established the inclusion and exclusion criteria for conducting this systematic review at a future date. This paper will present the first stage of carrying out a systematic review: planning the review and presenting the results of the scoping study. The findings of this scoping study revealed that academics are slow in adopting social media within teaching and academics that have adopted the use of social media do so primarily for sharing relevant information and resources easily with students rather than for teaching purposes. Overall, the adoption of social media as an educational tool is faced with many challenges, such as cultural resistance, pedagogical issues, lack of institutional support and time investment. The results also indicate that teaching styles, demographic factors, privacy issues and previous experience can influence academic staff’s decision to adopt social media for teaching purposes.

Teaching and Learning With Artificial Intelligence

2021 ◽  
pp. 73-98
Author(s):  
Libi Shen ◽  
Irene Chen ◽  
Anne Grey ◽  
Anchi Su
Keyword(s):  
Artificial Intelligence ◽  
Teaching And Learning ◽  
Learning Assessment ◽  
Modern World ◽  
Learning And Teaching ◽  
Curriculum Assessment ◽  
Education Major ◽  
Fast Speed ◽  
Artificial Intelligence In Education ◽  
Teaching Learning

Artificial intelligence (AI) is developing at a fast speed and has incessantly impacted the modern world for decades. AI technologies are beneficial for all kinds of industries, including businesses, economics, transportation, hospitals, schools, universities, and so forth. Many researchers have investigated the development of artificial intelligence in education (AIEd), specifically on how AI assists teaching, learning, assessment, references, and collaboration. Several questions arise. What impact do AI technologies have on education? How do AI technologies assist teaching (e.g., curriculum, assessment, student learning, and teaching practices)? How do teachers cope with AI Technologies in education? What are the ethical concerns of AI technologies? What are the barriers of AI-based learning in education? The purpose of this chapter is to explore the evolution and the challenges of AI technologies in education. Major research on AI from 1999 to 2019 will be reviewed. Problems with AI in education will be raised and solutions for solving the issues will be recommended.

Artificial Intelligence in Education

2021 ◽  
pp. 256-277
Author(s):  
Rashmi Khazanchi ◽  
Pankaj Khazanchi
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Pedagogical Practices ◽  
Student Centered ◽  
Tutoring Systems ◽  
Artificial Intelligence In Education ◽  
K 12 ◽  
Insight Into

Current educational developments in theories and practices advocate a more personalized, student-centered approach to teach 21st-century skills. However, the existing pedagogical practices cannot provide optimal student engagement as they follow a ‘one size fits all' approach. How can we provide high-quality adaptive instructions at a personalized level? Intelligent tutoring systems with embedded artificial intelligence can assist both students and teachers in providing personalized support. This chapter highlights the role of artificial intelligence in the development of intelligent tutoring systems and how these are providing personalized instructions to students with and without disabilities. This chapter gives insight into the challenges and barriers posed by the integration of intelligent tutoring systems in K-12 classrooms.

scholarly journals Domain-General Tutor Authoring with Apprentice Learner Models

2020 ◽  
Author(s):  
Christopher J. MacLellan ◽  
Kenneth R. Koedinger
Keyword(s):  
Artificial Intelligence ◽  
Experimental Design ◽  
Prior Knowledge ◽  
Computational Models ◽  
Tree Model ◽  
Tutoring Systems ◽  
Interactive Machine Learning ◽  
Expert Model ◽  
Wide Range ◽  
Artificial Intelligence In Education

Abstract Intelligent tutoring systems are effective for improving students’ learning outcomes (Pane et al. 2013; Koedinger and Anderson, International Journal of Artificial Intelligence in Education, 8, 1–14, 1997; Bowen et al. Journal of Policy Analysis and Management, 1, 94–111 2013). However, constructing tutoring systems that are pedagogically effective has been widely recognized as a challenging problem (Murray 2003; Murray, International Journal of Artificial Intelligence in Education, 10, 98–129, 1999). In this paper, we explore the use of computational models of apprentice learning, or computer models that learn interactively from examples and feedback, for authoring expert-models via demonstrations and feedback (Matsuda et al. International Journal of Artificial Intelligence in Education, 25(1), 1–34 2014) across a wide range of domains. To support these investigations, we present the Apprentice Learner Architecture, which posits the types of knowledge, performance, and learning components needed for apprentice learning. We use this architecture to create two models: the Decision Tree model, which non-incrementally learns skills, and the Trestle model, which instead learns incrementally. Both models draw on the same small set of prior knowledge (six operators and three types of relational knowledge) to support expert model authoring. Despite their limited prior knowledge, we demonstrate their use for efficiently authoring a novel experimental design tutor and show that they are capable of learning an expert model for seven additional tutoring systems that teach a wide range of knowledge types (associations, categories, and skills) across multiple domains (language, math, engineering, and science). This work shows that apprentice learner models are efficient for authoring tutors that would be difficult to build with existing non-programmer authoring approaches (e.g., experimental design or stoichiometry tutors). Further, we show that these models can be applied to author tutors across eight tutor domains even though they only have a small, fixed set of prior knowledge. This work lays the foundation for new interactive machine-learning based authoring paradigms that empower teachers and other non-programmers to build pedagogically effective educational technologies at scale.

scholarly journals Teaching and Learning in Interdisciplinary Higher Education: A Systematic Review

2009 ◽  
Vol 21 (4) ◽  
pp. 365-378 ◽  
Author(s):  
Elisabeth J. H. Spelt ◽  
Harm J. A. Biemans ◽  
Hilde Tobi ◽  
Pieternel A. Luning ◽  
Martin Mulder
Keyword(s):  
Higher Education ◽  
Systematic Review ◽  
Teaching And Learning

scholarly journals Bond, Bedenlier, Marin & Händel (2021) - ERT in HE pre-print

2021 ◽  
Author(s):  
Melissa Bond ◽  
Svenja Bedenlier ◽  
Victoria Marín ◽  
Marion Händel
Keyword(s):  
Higher Education ◽  
Educational Technology ◽  
Undergraduate Students ◽  
Technology Use ◽  
Teaching And Learning ◽  
Empirical Studies ◽  
Research Field ◽  
Collaborative Tools ◽  
Mapping Review ◽  
Systematic Mapping Review

Due to the Covid-19 pandemic that spread globally in 2020, higher education courses were subsequently offered in fully remote, online formats. A plethora of primary studies began investigating a range of topics exploring teaching and learning in higher education, particularly during the initial semester. In order to provide an overview and initial understanding of this emerging research field, a systematic mapping review was conducted that collates and describes the characteristics of 282 primary empirical studies. Findings reveal that research has been carried out mostly descriptively and cross-sectionally, focusing predominantly on undergraduate students and their perceptions of teaching and learning during the pandemic. Studies originate from a broad range of countries, are overwhelmingly published open access, and largely focused on the fields of Health & Welfare and Natural Sciences, Mathematics & Statistics. Educational technology used for emergency remote teaching are most often synchronous collaborative tools, used in combination with text-based tools. The findings are discussed against pre-pandemic research on educational technology use in higher education teaching and learning, and perspectives for further research are provided.

Sign in / Sign up

Export Citation Format

Share Document