IDEAS home Printed from https://ideas.repec.org/h/spr/fuobcp/978-3-032-06604-6_14.html

Human-Artificial Intelligence (HAI) Teaming in STEM Education: A Conceptual Framework for Skill Development in Industry 5.0

In: Artificial Humans

Author

Listed:
  • Baibhaw Kumar

    (University of Miskolc)

  • Csaba Deák

    (University of Miskolc)

Abstract

Recent transformation in pedagogy has a high potential for human-AI (HAI) teaming in STEM education in the age of fast technological reforms. The objective of this chapter is to investigate how AI tools might be subtly incorporated into teaching methods to improve student experiences, encourage customization, and stimulate creativity in STEM pedagogy for skills required for Industry 5.0. This chapter aims to analyze recently developed AI-driven technologies, such as Intelligent Tutoring Systems, Automated Assessment, and Adaptive Learning platforms, and to examine the role of AI in education for Industry 5.0 skills. These tools’ efficient use in STEM pedagogy was evaluated using a conceptual framework and a structured SWOT analysis, highlighting the obstacles as well as the potential they bring. It was found that cooperation in HAI is advantageous, such as customized learning paths and data-driven insights, but it also needs to be carefully considered in ethical and financial terms. The findings offer a road map for educators, policymakers, and future learners to fully use AI’s capabilities while maintaining inclusivity and equity with a comprehensive HAI framework. To create a more flexible and student-centered learning environment, future analysis should focus on improving existing or upcoming digital tools and investigating novel approaches to integrating AI in all educational scenarios considering human interactions.

Suggested Citation

Handle: RePEc:spr:fuobcp:978-3-032-06604-6_14
DOI: 10.1007/978-3-032-06604-6_14
as

Download full text from publisher

To our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a
for a similarly titled item that would be available.

More about this item

Keywords

;
;
;
;
;

Statistics

Access and download statistics

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:fuobcp:978-3-032-06604-6_14. See general information about how to correct material in RePEc.

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

We have no bibliographic references for this item. You can help adding them by using this form .

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.