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Education Sciences
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Application of AI Technologies in STEM Education
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Application of AI Technologies in STEM Education

A special issue of Education Sciences (ISSN 2227-7102). This special issue belongs to the section "Technology Enhanced Education".

Deadline for manuscript submissions: 20 January 2025 | Viewed by 3054

Special Issue Editors

Prof. Dr. Lisa Dieker

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Guest Editor
Williamson Family Distinguished Professor, Department of Special Education, University of Kansas, Lawrence, KS 66045, USA
Interests: simulation; teaching methods; special education; STEM; professional development
Dr. Eleazar Vasquez

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Guest Editor
School of Teacher Education, University of Central Florida, Orlando, FL 32816, USA
Interests: simulation; teaching methods; special education; STEM; professional development
Dr. Matthew T. Marino

E-Mail Website
Guest Editor
College of Innovation and Education, University of Central Florida, 12494 University Blvd, Orlando, FL 32816, USA
Interests: instructional design for diverse populations; universal design for learning; executive function; transition from school to work

Special Issue Information

Dear Colleagues,

We are inviting you to submit a paper to this Special Issue on artificial intelligence and STEM education that we are guest editing. The aim of this Special Issue is to discuss how artificial intelligence (AI) is being used in STEM education in both higher education and K-12 education. The overall focus is to identify and highlight the current, emerging, and future use of AI in higher education and K-12 education, taking into account the research and practices surrounding the use of AI- in STEM-related areas. The primary reason for collecting these articles into a Special Issue is because with the discussion around AI a hot topic in the field today, the current literature lacks a robust discussion in relation to how AI can be specifically aligned to STEM education in K-12 and higher education settings.

We as Guest Editors see this Special Issue supplementing the existing literature on AI technology. The current literature is either in the form of popular media or the research is about the technology more generally and not the specific use and development of AI tools aligned with STEM education and student or teacher learning. The primary use cases and discussions currently in AI are on generating language or new agencies for the tools. Yet how do these new emerging and existing tools create access, equity, and even disruption in the K-12 learning and higher education spaces in STEM education? This Special Issue will provide needed foundational research, discussions, and case examples for building the bridge between AI and STEM education.

We hope you will consider our request to submit to this Special Issue. If you have any questions, please do reach out to any of us.

Sincerely.

Prof. Dr. Lisa Dieker
Dr. Eleazar Vasquez
Dr. Matthew T. Marino
Guest 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 special issue 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
  • stem education
  • k-12
  • higher education
  • teacher preparation
  • student learning

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

30 pages, 8280 KiB  
Article
Learn with M.E.—Let Us Boost Personalized Learning in K-12 Math Education!
by Norbert Annuš and Tibor Kmeť
Educ. Sci. 2024, 14(7), 773; https://doi.org/10.3390/educsci14070773 - 16 Jul 2024
Viewed by 579
Abstract
The traditional educational system, in certain aspects, limits personalized learning. This is mainly evident in the fact that average students, who do not have any learning difficulties, are required to solve the same tasks from the same textbook in the same order. Artificial [...] Read more.
The traditional educational system, in certain aspects, limits personalized learning. This is mainly evident in the fact that average students, who do not have any learning difficulties, are required to solve the same tasks from the same textbook in the same order. Artificial intelligence and other smart learning tools present great opportunities for implementing a personalized learning system. Our previous surveys and literature reviews also show that educators see the greatest potential in personalized education for the assimilation of artificial intelligence into education. In this context, we have developed educational software called “Learn with M.E. as Math Educator”, which facilitates more personalized teaching of basic mathematical operations. This study presents the structure and operation of this application. We tested the usability of the software in several institutions. Our research target group consists of elementary school students, specifically those aged 11–15. This article provides a detailed overview of the accuracy and educational outcomes of the completed application. We evaluated the application and its effectiveness using both qualitative and quantitative methods. Our research design combined elements of educational technology development and effectiveness assessment. To evaluate student performance, we employed a control group methodology. Data were analyzed by comparing test results between students using the software and those receiving traditional instruction. We examined user satisfaction through survey questionnaires. Teachers’ opinions were gathered through structured interviews, and their responses were categorized using a SWOT analysis. The findings indicated that the use of the software significantly improved students’ mathematics performance compared to the control group. Students provided positive feedback on the software’s user interface, describing it as user-friendly and motivating. Teachers regarded the software as an effective educational tool, facilitating differentiated instruction and enhancing student engagement. The results suggest that digital educational tools, such as the developed software, can provide substantial added value in education. Full article
(This article belongs to the Special Issue Application of AI Technologies in STEM Education)
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20 pages, 293 KiB  
Article
Engineering Education in the Age of AI: Analysis of the Impact of Chatbots on Learning in Engineering
by Flor A. Bravo and Juan M. Cruz-Bohorquez
Educ. Sci. 2024, 14(5), 484; https://doi.org/10.3390/educsci14050484 - 2 May 2024
Cited by 1 | Viewed by 1594
Abstract
The purpose of this paper is to explore the influence of using AI chatbots on learning within the context of engineering education. We framed this study on the principles of how learning works in order to describe the contributions and challenges of AI [...] Read more.
The purpose of this paper is to explore the influence of using AI chatbots on learning within the context of engineering education. We framed this study on the principles of how learning works in order to describe the contributions and challenges of AI chatbots in five categories: (1) facilitating the acquisition, completion, or activation of prior knowledge and helping organize knowledge and making connections; (2) enhancing student motivation to learn; (3) fostering self-directed learning and the acquisition, practice, and application of the skills and knowledge they acquire; (4) supporting goal-directed practice and feedback; and (5) addressing student diversity and creating a positive classroom environment. To elicit the uses, benefits, and drawbacks of using AI chatbots in students’ learning, we conducted a thematic analysis of qualitative data gathered from surveying 38 student volunteers from 5 different electronic and mechatronic engineering courses at a South American university. Based on a literature review and an evidence-based discussion, we offer practical suggestions for instructors who want to promote the use of AI to enhance their students’ learning. Full article
(This article belongs to the Special Issue Application of AI Technologies in STEM Education)
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