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PreprintCited by 280Artificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in Healthcare and Education

How Chat GPT Can Transform Autodidactic Experiences and Open Education?

Open access
Firat, Mehmet·2023
DOI 10.31219/osf.io/9ge8m
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Abstract

Chat GPT, powered by the advanced GPT-3 language model from OpenAI, has reached 1 million users only in 5 days. Facebook, Netflix, Instagram, and Twitter could reach this number in 300, 1200, 75, and 720 days. With 175 billion parameters, GPT-3 can generate writing that closely resembles human language. Chat GPT can engage in multiple ongoing conversations, understand and respond to natural language input, and offer customized and interactive assistance. This makes Chat GPT a promising tool for open education, as it can improve the independence and autonomy of autodidactic learners, while also being both practical and adaptable. By providing personalized support, direction, and feedback, Chat GPT has the potential to increase motivation and engagement among autodidactic learners.

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  • ChatGPT has the potential to offer personalized learning experiences and support an effective learning process by providing students with customized feedback and explanations tailored to their needs and abilities.

    “ChatGPT has the potential to offer personalized learning experiences and support an effective learning process by providing students with customized feedback and explanations tailored to their needs and abilities”

    From A systematic literature review of empirical research on ChatGPT in education (2024) · cites this paper as [26]

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1 of the 12 references couldn’t be matched to a known paper.

Research works printed but not linked: 5

  1. Caswell, T., Henson, S., Jensen, M., & Wiley, D. (2008). Open educational resources: Enabling universal education. International Review of Research in Open and Distributed Learning, 9(1), 1-11.
  2. Gureckis, T. M., & Markant, D. B. (2012). Self-directed learning: A cognitive and computational perspective. Perspectives on Psychological Science, 7(5), 464-481.
  3. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
  4. Manning, L. D., Jones, J. E., Buehlman, V., Deal, J. M., & Showalter, L. J. (2022). A Center- Based Model for Self-Directed Learning in Sustainability: Engaging Campus and Community as a Living Lab. In Self-Directed Learning and the Academic Evolution From Pedagogy to Andragogy (pp. 97-118). IGI Global.
  5. Zhou, Z. H. (2021). Machine learning. Springer Nature.

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Authors
Firat, M.
ORCID
Firat, Mehmet
Publisher
Center for Open Science
Published
2023-01-12
DOI
10.31219/osf.io/9ge8m
OpenAlex
W4316041216
Cited by
280
References
12
Citations by year
2026: 22 · 2025: 61 · 2024: 119 · 2023: 78
Type
posted content
Language
en
Open access
Licence
Locations
  • OSF Preprints (OSF Preprints) (submittedVersion, cc-by)
  • OSF Preprints (OSF Preprints) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
ChatGPT, GPT-3, open education, personalized learning support, learner autonomy, student motivation, student engagement

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