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Shaping Integrity: Why Generative Artificial Intelligence Does Not Have to Undermine Education

Authors
Myles Joshua Toledo Tan,
Nicholle Mae Amor Tan Maravilla
Date
Publisher
arXiv
This paper examines the role of generative artificial intelligence (GAI) in promoting academic integrity within educational settings. It explores how AI can be ethically integrated into classrooms to enhance learning experiences, foster intrinsic motivation, and support voluntary behavior change among students. By analyzing established ethical frameworks and educational theories such as deontological ethics, consequentialism, constructivist learning, and Self-Determination Theory (SDT), the paper argues that GAI, when used responsibly, can enhance digital literacy, encourage genuine knowledge construction, and uphold ethical standards in education. This research highlights the potential of GAI to create enriching, personalized learning environments that prepare students to navigate the complexities of the modern world ethically and effectively.
Study at a glance
  • Question: How can generative artificial intelligence be ethically integrated into classrooms to support learning and promote academic integrity?
  • Setting and data: Not stated in the abstract
  • Method: Conceptual analysis of ethical frameworks and educational theories
Why it matters

Generative artificial intelligence does not have to undermine education. Examining how AI can be ethically integrated into classrooms helps address how GAI can promote academic integrity and uphold ethical standards.

Key findings
  • In classrooms, responsible use of generative AI can uphold academic integrity while enhancing students' digital literacy and knowledge construction.
  • Ethical integration of generative AI can enhance learning experiences, foster students' internal motivation, and support positive, voluntary behavior changes.
  • Generative AI has the potential to create personalized learning environments that prepare students to navigate modern complexities ethically and effectively.

Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.

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