DEEP LEARNING EPISTEMOLOGY: A PHILOSOPHICAL PARADIGM OF SCIENTIFIC UNDERSTANDING IN THE ERA OF ARTIFICIAL INTELLIGENCE

Authors

  • septiana munggarani Universitas Pendidikan Indonesia
  • Yusuf Tri Herlambang Universitas Pendidikan Indonesia
  • Yunus Abidin Universitas Pendidikan Indonesia

DOI:

https://doi.org/10.36526/sosioedukasi.v14i4.6563

Keywords:

Epistemology, deep learning, philosophy of knowledge, artificial intelligence, scientific paradigm

Abstract

Rapid advances in artificial intelligence (AI) have transformed how humans understand, manage, and construct knowledge. In an era of digital technology and automation, it becomes essential to revisit the meaning of knowledge and human cognition, especially as machine learning systems begin to emulate and even surpass human analytical abilities. This study aims to explore how traditional epistemological concepts are transformed through the application of deep learning and its implications for scientific understanding and educational praxis. Using a Systematic Literature Review (SLR) method, this research analyzes recent scholarly works related to epistemology, deep learning, and the philosophy of knowledge within the context of AI. The findings indicate that deep learning not only signifies technological progress but also establishes a new paradigm for acquiring and validating knowledge. This paradigm emphasizes data-driven reasoning, continuous learning, and the construction of knowledge through inferential patterns similar to human thought. In conclusion, deep learning represents a holistic and dynamic model of scientific understanding, promoting synergy between artificial intelligence and human wisdom in developing meaningful and ethical knowledge in the digital era.

References

Akmal, M. M. (2025). Systematic literature review (SLR) in the development of deep learning-based learning in the digital era. . Journal of Educational Science Innovation (JIIP), , 14(2), 87–99.

Bai, J. (2022). Machine learning epistemology. . Philosophy. Sociology, 33(2), 150–161, Lithuanian Academy of Sciences.

Casal-Otero, LE (2023). AI Literacy in K-12: A systematic literature review. International Journal (STEM Education).

Chiu, T. K. (2024). A systematic literature review on ChatGPT in education. Discover Education, 3(24), 1–21.

Desai, C.B. (2022). Epistemology of data science: Five emerging research domains. Synthesis, , 200(3), 1–21.

Domínguez-González, M.J.-R.-T. (2025). Teachers' digital competence: The key to the future of education through systematic review. . Contemporary Educational Technology, , 17(1), 16168.

Duede. (2022). The blurring of deep learning in scientific discovery. arXiv / Philosophy of Science. Cornell University.

education, B.A. (2025). Concerns about Big Tech's influence in knowledge formation. . BusinessInsider/Wired (news summary).

Floridi, L. (. (2022). On the epistemology of data and information. . Synthesis.

Ghamrawi, N. A. (2025). A step-by-step approach to systematic review in educational research. Journal of Educational Review, , 10(1), 1–17.

Herlambang, Y. S. (2024). Education and Technology: A Study of Philosophy in Don Ihde's Perspective. Seroja: Journal of Education, 2(1), 45–57.

Herlambang, Y. T. (2023). The Role of Technology Transformation to Improve Students' Intellectual Knowledge in the Era of Revolution 4.0. Scholars: Journal of Education and Teaching, 1(4), 176–184.

Herlambang, Y. T. (2023). Technological Transformation in Education in the Era of the Industrial Revolution 4.0: The Technological Dilemma in a Philosophical Perspective. . Scholars: Journal of Education and Teaching, 1(5), 219–225.

Herlambang, Y. T. (2023b). Epistemology of Technology and Educational Challenges in the Age of AI. . Indonesian Journal of Philosophy, 4(2), 112–125.

Herlambang, Y. T. (2024b). The Relevance of the Independent Curriculum to the Conception of Ki Hadjar Dewantara: A Critical Study of Reflective Learning. Cetta: Journal of Educational Sciences, 7(1), 33–45.

Herlambang, Y. T. (2025). Digital Transformation in Education: A Critical Review in Philosophical Review. . Ideguru: Journal of Teachers' Scientific Works, 10(2), 1743–1748.

Kitchenham, B. & (2022). Guidelines for conducting a systematic literature review in software engineering. Information Technology and Software.

Kolaski, IL (2023). Tools and best practices for systematic reviews. . Systematic Review, , 12(93), 1–14.

Letelier, LM (2021). A systematic review and its epistemological foundation. Journal of Positive School Psychology.

Library, U. o. (2025). Systematic review guide. Taken from.

Mersha, M. (2024). Explainable Artificial Intelligence: Survey of needs and methods. arXiv.

Face, TK (2023). Improving transparency and rigor in systematic reviews: Critical assessment. Frontiers in Research Metrics and Analytics, 8(1268045), 1–9.

Mustafa, N. (2024). Systematic literature review of artificial intelligence in education: Trends and challenges. Smart Learning Environment, 11(4), 18.

Pers., C. U. (2023). Lack of clarity of deep learning in scientific discovery. . Philosophy of Science, , 90(4), 875–892.

PRISM. (2020). PRISMA Statement 2020: Updated guidelines for reporting systematic reviews. BMJ/PRISMA.

Rifai, I. W. (2025). A Systematic Literature Review on the Integration of Epistemological Theory in Educational Management Practice. . Management in Education Review (MIER), , 7(1), 45–58.

Samaniego, E. & (2023). Deep learning in education: Concepts, factors, models, and measurement. Journal of Educational Research, 12(4), 16461.

Shu, J. (2025). Production and dissemination of knowledge in human-AI collaboration. . HSSR Journal.

Springer. (2022). Data science epistemology: Challenges to transparency and justification. Synthesis, 200(5), 1–15.

Teng, Q. e. (2022). A survey on the interpretability of deep learning in medical diagnosis. . Journal / PMC.

Undiksha. (2021). Artificial intelligence in the context of modern educational epistemology. . Journal of Indonesian Philosophy, , 3(1), 67–78.

Zhai, X. C. (2023). AI literacy in STEM education: A systematic review. . International Journal of STEM Education, 10(4), 1–15.

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Published

2025-12-15

How to Cite

septiana munggarani, Yusuf Tri Herlambang, & Yunus Abidin. (2025). DEEP LEARNING EPISTEMOLOGY: A PHILOSOPHICAL PARADIGM OF SCIENTIFIC UNDERSTANDING IN THE ERA OF ARTIFICIAL INTELLIGENCE. SOSIOEDUKASI : JURNAL ILMIAH ILMU PENDIDIKAN DAN SOSIAL, 14(4), 3414–3425. https://doi.org/10.36526/sosioedukasi.v14i4.6563