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Education Sciences
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Artificial Intelligence (AI) and Education
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Artificial Intelligence (AI) and Education

A topical collection in Education Sciences (ISSN 2227-7102).

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Editors

Dr. Gary K. W. Wong

E-Mail Website
Collection Editor
Faculty of Education, The University of Hong Kong, Hong Kong SAR, China
Interests: curriculum and instruction; technology-enhanced learning; learning sciences; brain and cognition; educational psychology
Special Issues, Collections and Topics in MDPI journals
Dr. Ho-Yin Cheung

E-Mail Website
Collection Editor
Department of Mathematics and Information Technology, The Education University of Hong Kong, 10 Lo Ping Road, Tai Po, New Territories, Hong Kong
Interests: coding education and computational thinking; STEM education; flipped classroom and active learning pedagogies

Topical Collection Information

Dear Colleagues,

Computing technologies have fundamentally altered the way we live and relate to one another. The technological advancements in the post-pandemic era have significantly influenced the development of educational policy, pedagogical practices, and learning. In recent years, empirical and conceptual research has begun to examine two aspects of artificial intelligence (AI) or Generative AI (e.g., ChatGPT) in the context of education: “AI in education” and “AI education”, which are being misinterpreted and confused by educational researchers and educators yet are both emerging areas of educational research. On one hand, research of “AI in education” concerns the application of AI technologies in advancing and improving the educational practices for effective teaching and learning. It takes the advantages of advanced data sciences and learning analytics, and applies AI techniques and concepts to design and develop learning systems for personalization and better learning outcomes.

On the other hand, “AI education” is concerned with teaching and learning of AI, developing a research agenda of educational policy, curriculum design and specific contents to allow new generations to understand the foundations of AI technologies, how AI impacts the society in present and the future, and what ethical and safety implications of AI are behind the development and various applications. New learning designs and technologies are designed to assist students in understanding the abstract concepts and mechanism of AI, such as machine learning (ML), deep learning (DL), natural language processing (NLP), neural networks, and cognitive computing. The research is also interested in exploring what and how students at K-12 or beyond learn AI and develop new multiliteracies in this postdigital era.

This Topical Collection focuses on research aiming to connect educational researchers interested in AI and education, and debate about the use of AI in education and the teaching and learning of AI in schools at all levels, not limited to pre-school, elementary, secondary or pre-college. As we aim to develop a comprehensive research agenda of AI and education, we welcome submissions from diverse populations, with a particular focus on the conceptual understanding of AI development, empirical aspects of designing AI technologies for education, and school-based practices in teaching and learning of AI. Within the broad scope of this issue, prospective authors are encouraged to strengthen both the provision of theoretical foundations and the technical/practical guidelines beyond the existing research.  Moreover, it is even more provocative to consider developing advanced AI-based educational technologies to teach students the basic concept of AI, trying to embrace the possibility of AI in and for education. Educational reviews with meta-analysis and meta-synthesis are also welcome in this Special Issue to examine the existing works and provide the future research agenda for researchers to reference.

The key topics include, but are not limited to, the following:

  • AI and education
    • Policy challenges of implementing AI technologies for education and teaching and learning of AI
    • Review of educational practices and future development of educational technologies with AI
    • Analysis of teaching and learning of AI in school
    • Ethical implications of using AI in education and research trends
  • AI in education
    • Design and development of AI-based educational technologies
    • Different subject learning (e.g., language, STEM, arts, business…) using AI
    • Ethics and safety of using AI in education
    • Intelligent tutoring and learning systems using AI
    • Precision and personalized education using AI
    • Learning emotion detection using AI in physical and remote classroom
  • AI education
    • K-12 curriculum design of AI education at all levels
    • Learning design and pedagogical approaches in AI education
    • Assessment of learning in AI education
    • Pre-service teacher education and professional development for in-service teachers in AI education
    • Cognitive and psychosocial development of children through AI education
    • Interdisciplinary and transdisciplinary approach to AI education with other subjects, such as language, mathematics, and humanities

Dr. Gary K. W. Wong
Dr. Ho-Yin Cheung 
Collection Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the collection website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Education Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence (AI) education
  • AI in education
  • teaching and learning using AI
  • policy review of AI in education

Published Papers (7 papers)

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2024

Jump to: 2023

15 pages, 2068 KiB  
Review
A Primer on Generative Artificial Intelligence
by Faisal Kalota
Educ. Sci. 2024, 14(2), 172; https://doi.org/10.3390/educsci14020172 - 7 Feb 2024
Cited by 13 | Viewed by 10990
Abstract
Many educators and professionals in different industries may need to become more familiar with the basic concepts of artificial intelligence (AI) and generative artificial intelligence (Gen-AI). Therefore, this paper aims to introduce some of the basic concepts of AI and Gen-AI. The approach [...] Read more.
Many educators and professionals in different industries may need to become more familiar with the basic concepts of artificial intelligence (AI) and generative artificial intelligence (Gen-AI). Therefore, this paper aims to introduce some of the basic concepts of AI and Gen-AI. The approach of this explanatory paper is first to introduce some of the underlying concepts, such as artificial intelligence, machine learning, deep learning, artificial neural networks, and large language models (LLMs), that would allow the reader to better understand generative AI. The paper also discusses some of the applications and implications of generative AI on businesses and education, followed by the current challenges associated with generative AI. Full article
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2023

Jump to: 2024

19 pages, 7076 KiB  
Article
An Exploratory Study of Helping Undergraduate Students Solve Literature Review Problems Using Litstudy and NLP
by Gary K. W. Wong and Simon Y. K. Li
Educ. Sci. 2023, 13(10), 987; https://doi.org/10.3390/educsci13100987 - 27 Sep 2023
Cited by 2 | Viewed by 2058
Abstract
(1) Many undergraduate students struggle to produce a good literature review in their dissertations, as they are not experienced, do not have sufficient time, and do not have the required skills to articulate information. (2) Subsequently, we deployed Litstudy and NLP tools and [...] Read more.
(1) Many undergraduate students struggle to produce a good literature review in their dissertations, as they are not experienced, do not have sufficient time, and do not have the required skills to articulate information. (2) Subsequently, we deployed Litstudy and NLP tools and developed a recommendation system to analyze articles in an academic database to help the students produce literature reviews. (3) The recommendation system successfully performed three levels of analysis. The elementary-level analysis provided demographic statistical analysis to the students, helping them understand the background information of the selected articles they would review. The intermediate-level analysis provided visualization of citations in network graphs for the students to understand the relationships of the articles’ authors, regions, and institutes so that the flow of ideas, development, and similarity of the selected articles can be better analyzed. The advanced level of analysis provided topic modeling functions for the students to understand the high-level themes of the selected articles to improve productivity as they read through them and simultaneously boost their creativity. (4) The three levels of analysis successfully analyzed the selected articles to provide innovative results and triggered the students to handle literature reviews in a new way. Further enhancement opportunities were identified in integrating the NLP technologies with large language models to facilitate the generation of research ideas/insights. This would be an exciting opportunity to have AI/NLP integrated to help the students with their research. Full article
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12 pages, 1010 KiB  
Article
Evaluating AI Courses: A Valid and Reliable Instrument for Assessing Artificial-Intelligence Learning through Comparative Self-Assessment
by Matthias Carl Laupichler, Alexandra Aster, Jan-Ole Perschewski and Johannes Schleiss
Educ. Sci. 2023, 13(10), 978; https://doi.org/10.3390/educsci13100978 - 26 Sep 2023
Cited by 9 | Viewed by 5297
Abstract
A growing number of courses seek to increase the basic artificial-intelligence skills (“AI literacy”) of their participants. At this time, there is no valid and reliable measurement tool that can be used to assess AI-learning gains. However, the existence of such a tool [...] Read more.
A growing number of courses seek to increase the basic artificial-intelligence skills (“AI literacy”) of their participants. At this time, there is no valid and reliable measurement tool that can be used to assess AI-learning gains. However, the existence of such a tool would be important to enable quality assurance and comparability. In this study, a validated AI-literacy-assessment instrument, the “scale for the assessment of non-experts’ AI literacy” (SNAIL) was adapted and used to evaluate an undergraduate AI course. We investigated whether the scale can be used to reliably evaluate AI courses and whether mediator variables, such as attitudes toward AI or participation in other AI courses, had an influence on learning gains. In addition to the traditional mean comparisons (i.e., t-tests), the comparative self-assessment (CSA) gain was calculated, which allowed for a more meaningful assessment of the increase in AI literacy. We found preliminary evidence that the adapted SNAIL questionnaire enables a valid evaluation of AI-learning gains. In particular, distinctions among different subconstructs and the differentiation constructs, such as attitudes toward AI, seem to be possible with the help of the SNAIL questionnaire. Full article
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18 pages, 845 KiB  
Article
AI Course Design Planning Framework: Developing Domain-Specific AI Education Courses
by Johannes Schleiss, Matthias Carl Laupichler, Tobias Raupach and Sebastian Stober
Educ. Sci. 2023, 13(9), 954; https://doi.org/10.3390/educsci13090954 - 19 Sep 2023
Cited by 9 | Viewed by 6133
Abstract
The use of artificial intelligence (AI) is becoming increasingly important in various domains, making education about AI a necessity. The interdisciplinary nature of AI and the relevance of AI in various fields require that university instructors and course developers integrate AI topics into [...] Read more.
The use of artificial intelligence (AI) is becoming increasingly important in various domains, making education about AI a necessity. The interdisciplinary nature of AI and the relevance of AI in various fields require that university instructors and course developers integrate AI topics into the classroom and create so-called domain-specific AI courses. In this paper, we introduce the “AI Course Design Planning Framework” as a course planning framework to structure the development of domain-specific AI courses at the university level. The tool evolves non-specific course planning frameworks to address the context of domain-specific AI education. Following a design-based research approach, we evaluated a first prototype of the tool with instructors in the field of AI education who are developing domain-specific courses in this area. The results of our evaluation indicate that the tool allows instructors to create domain-specific AI courses in an efficient and comprehensible way. In general, instructors rated the tool as useful and user-friendly and made recommendations to improve its usability. Future research will focus on testing the application of the tool for domain-specific AI course developments in different domain contexts and examine the influence of using the tool on AI course quality and learning outcomes. Full article
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23 pages, 2812 KiB  
Article
Exploring Computer Science Students’ Perception of ChatGPT in Higher Education: A Descriptive and Correlation Study
by Harpreet Singh, Mohammad-Hassan Tayarani-Najaran and Muhammad Yaqoob
Educ. Sci. 2023, 13(9), 924; https://doi.org/10.3390/educsci13090924 - 11 Sep 2023
Cited by 43 | Viewed by 12897
Abstract
ChatGPT is an emerging tool that can be employed in many activities including in learning/teaching in universities. Like many other tools, it has its benefits and its drawbacks. If used properly, it can improve learning, and if used irresponsibly, it can have a [...] Read more.
ChatGPT is an emerging tool that can be employed in many activities including in learning/teaching in universities. Like many other tools, it has its benefits and its drawbacks. If used properly, it can improve learning, and if used irresponsibly, it can have a negative impact on learning. The aim of this research is to study how ChatGPT can be used in academia to improve teaching/learning activities. In this paper, we study students’ opinions about how the tool can be used positively in learning activities. A survey is conducted among 430 students of an MSc degree in computer science at the University of Hertfordshire, UK, and their opinions about the tool are studied. The survey tries to capture different aspects in which the tool can be employed in academia and the ways in which it can harm or help students in learning activities. The findings suggest that many students are familiar with the tool but do not regularly use it for academic purposes. Moreover, students are skeptical of its positive impacts on learning and think that universities should provide more vivid guidelines and better education on how and where the tool can be used for learning activities. The students’ feedback responses are analyzed and discussed and the authors’ opinions regarding the subject are presented. This study shows that ChatGPT can be helpful in learning/teaching activities, but better guidelines should be provided for the students in using the tool. Full article
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11 pages, 261 KiB  
Essay
Information and Media Literacy in the Age of AI: Options for the Future
by Peter Tiernan, Eamon Costello, Enda Donlon, Maria Parysz and Michael Scriney
Educ. Sci. 2023, 13(9), 906; https://doi.org/10.3390/educsci13090906 - 7 Sep 2023
Cited by 10 | Viewed by 12778
Abstract
The concepts of information and media literacy have been central components of digital literacy since the digitization of information began. However, the increasing influence of artificial intelligence on how individuals locate, evaluate, and create content has significant implications for what it means to [...] Read more.
The concepts of information and media literacy have been central components of digital literacy since the digitization of information began. However, the increasing influence of artificial intelligence on how individuals locate, evaluate, and create content has significant implications for what it means to be information and media literate. This paper begins by exploring the role artificial intelligence plays at the various stages of information retrieval and creation processes. Following this, the paper reviews existing digital literacy frameworks to ascertain their definitions of information and media literacy and the potential impact of artificial intelligence on them. We find that digital literacy frameworks have been slow to react to artificial intelligence and its repercussions, and we recommend a number of strategies for the future. These strategies center around a more agile, responsive, and participatory approach to digital literacy framework development and maintenance. Full article
13 pages, 636 KiB  
Article
From Human to Machine: Investigating the Effectiveness of the Conversational AI ChatGPT in Historical Thinking
by Sergio Tirado-Olivares, Maria Navío-Inglés, Paula O’Connor-Jiménez and Ramón Cózar-Gutiérrez
Educ. Sci. 2023, 13(8), 803; https://doi.org/10.3390/educsci13080803 - 5 Aug 2023
Cited by 7 | Viewed by 4989
Abstract
In the digital age, the integration of technology in education is gaining attention. However, there is limited evidence of its use in promoting historical thinking. Students need to develop critical thinking skills to address post-truth and fake news, enabling them to question sources, [...] Read more.
In the digital age, the integration of technology in education is gaining attention. However, there is limited evidence of its use in promoting historical thinking. Students need to develop critical thinking skills to address post-truth and fake news, enabling them to question sources, evaluate biases, and consider credibility. With the advancement of artificial intelligence (AI), historical thinking becomes even more crucial, as chatbots appear capable of analysing, synthesizing, interpreting, and writing similarly to humans. This makes it more difficult to distinguish between human and AI-generated resources. This mixed study explores the potential of AI in developing an argumentative historical text compared to future teachers. After 103 preservice teachers were instructed in historical thinking, they assessed a text written by a human and an AI-written text without knowing their authors. The obtained results indicate that participants assessed the AI text better based on historical thinking skills. Conversely, when asked about the capability of AI to develop a similar text, they emphasized its impossibility due to the belief that AI is incapable of expressing personal opinions and reflecting. This highlights the importance of instructing them in the correct use and possibilities of AI for future historical teaching. Full article
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