Tanggung Jawab Etis Penggunaan Artificial Intelligence Di Tanah Pendidikan: Formulasi Paradigma Baru Untuk Teknologi Otonom

Authors

  • Oktaviani Putri Dita Universitas Negeri Malang
  • Radittya Mahasputra Antara Universitas Negeri Malang
  • Agung Winarno Universitas Negeri Malang

DOI:

https://doi.org/10.61132/jumaket.v1i4.388

Keywords:

ethical responsibility, Artificial Intelligence, academic domain, new paradigm, and autonomous technology

Abstract

This study examines the application of Immanuel Kant's ethical principles in the use of autonomous technology based on Artificial Intelligence (AI) in the academic domain. AI technology has significantly impacted academic efficiency and innovation but has also raised serious ethical challenges, such as algorithmic bias, dependency on technology, and threats to privacy and transparency. Using a Kantian ethical approach, this research identifies key issues and proposes an ethical framework that places justice, transparency, and respect for human dignity at the core of the design and implementation of autonomous technologies. The findings indicate that algorithmic bias can exacerbate academic inequities, while excessive reliance on AI risks diminishing the critical and creative capabilities of academics. Additionally, privacy and accountability in the management of academic data emerge as major concerns requiring ethically grounded policy interventions. This study also highlights the need for a new paradigm ensuring that autonomous technologies uphold humanistic values in academic settings, such as inclusivity, intellectual autonomy, and justice.

Downloads

Download data is not yet available.

References

Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency, 149–159. https://doi.org/10.1145/3287560.3287584

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Carr, N. (2020). The shallows: What the Internet is doing to our brains (Updated edition). W.W. Norton & Company.

Crawford, K., & Calo, R. (2016). There is a blind spot in AI research. Nature, 538(7625), 311–313. https://doi.org/10.1038/538311a

Crawford, K., & Schultz, J. (2019). Big data and due process: Toward a framework to redress predictive privacy harms. Boston College Law Review, 55(1), 93–128. https://lawdigitalcommons.bc.edu/bclr/vol55/iss1/4

Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among five approaches (3rd ed.). SAGE Publications.

Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). SAGE Publications.

Kaminski, M. E. (2019). The right to explanation, explained. Berkeley Technology Law Journal, 34(1), 189–218. https://doi.org/10.15779/Z38TD9N83H

Kant, I. (1996). Groundwork of the metaphysics of morals (M. Gregor, Trans.). Cambridge University Press. (Original work published 1785)

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson. https://www.pearson.com/intelligence-unleashed

Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press.

O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown. https://weaponsofmathdestructionbook.com

Raji, I. D., & Buolamwini, J. (2020). Actionable auditing: Investigating the impact of public AI accountability. Proceedings of the AAAI/ACM Conference on AI Ethics and Society, 429–435. https://doi.org/10.1145/3375627.3375832

Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Learning, Media and Technology, 44(2), 103–115. https://doi.org/10.1080/17439884.2019.1668698

Sundararajan, A. (2021). Artificial intelligence and academic autonomy: Impacts on creativity and critical thinking. Journal of Educational Technology & Society, 24(3), 105–117. https://www.jstor.org/stable/27028250

Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222. https://doi.org/10.1111/1467-8551.00375

Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). SAGE Publications.

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education: Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0176-8

Downloads

Published

2024-12-05

How to Cite

Oktaviani Putri Dita, Radittya Mahasputra Antara, & Agung Winarno. (2024). Tanggung Jawab Etis Penggunaan Artificial Intelligence Di Tanah Pendidikan: Formulasi Paradigma Baru Untuk Teknologi Otonom. Jurnal Manajemen Kewirausahaan Dan Teknologi, 1(4), 58–83. https://doi.org/10.61132/jumaket.v1i4.388