Artificial Intelligence Ethics in Education and its Implications for 21st-Century Pedagogical Practices

Abstract : The fast adoption of artificial intelligence (AI) tools into the learning setting has dramatically altered the modern pedagogical paradigm and provided novel opportunities of personalised learning, automated evaluation, and data-driven decision-making. But with these innovations in technology, certain ethical and philosophical issues have arisen in the area of algorithmic discrimination, privacy, academic honesty, and the new role of tutors in the AI-mediated learning setting. This paper will explore the ethical issue of artificial intelligence in education and want to discuss the effects of these ethics on the practises of pedagogies in the twenty first century. The study takes a conceptual design that is qualitative based on philosophical and interpretative analysis of the available scholarly literature on AI in education, educational philosophy, and technology ethics. Based on an interdisciplinary theoretical framework, which incorporates the humanistic philosophy of education, ethics of artificial intelligence, and critical pedagogy, the paper examines the main ethical issues related to AI-based educational systems. The results indicate four key themes, which include the alteration of teacher roles and human agency in classes mediated by AI, the problem of algorithmic bias and fairness, the problem of data privacy and the ethical management of educational data, and the increasing demand of human-focused AI pedagogy. The discussion indicates that although AI technologies have the potential to make the instruction process more efficient and focus on individualised learning, these applications should be managed according to the ethical frameworks, which are based on the principles of transparency, accountability, equity, and respect to human autonomy. The conclusion reached by the study is that the responsible integration of AI into the educational process must be balanced and be based on a combination of technological innovation and humanistic educational values, ethical governance, and critical digital literacy. The insights are useful in continuing the discussion of AI ethics in education and offer implications of AI in the digital era on the pedagogical practise and educational policy. Keywords : Artificial Intelligence in Education, AI Ethics, Human-Centered Pedagogy, Algorithmic Bias, Data Privacy, Educational Technology Governance.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-03-31
DOI
https://doi.org/10.5281/zenodo.19600125
Primary Topic
Technology-Enhanced Education Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Artificial Intelligence Ethics in Education and its Implications for 21st-Century Pedagogical Practices

Anirban Kisku
Zenodo (CERN European Organization for Nuclear Research)
Technology-Enhanced Education Studies
article

Artificial Intelligence Ethics in Education and its Implications for 21st-Century Pedagogical Practices

Anirban Kisku
article en

Abstract

Abstract : The fast adoption of artificial intelligence (AI) tools into the learning setting has dramatically altered the modern pedagogical paradigm and provided novel opportunities of personalised learning, automated evaluation, and data-driven decision-making. But with these innovations in technology, certain ethical and philosophical issues have arisen in the area of algorithmic discrimination, privacy, academic honesty, and the new role of tutors in the AI-mediated learning setting. This paper will explore the ethical issue of artificial intelligence in education and want to discuss the effects of these ethics on the practises of pedagogies in the twenty first century. The study takes a conceptual design that is qualitative based on philosophical and interpretative analysis of the available scholarly literature on AI in education, educational philosophy, and technology ethics. Based on an interdisciplinary theoretical framework, which incorporates the humanistic philosophy of education, ethics of artificial intelligence, and critical pedagogy, the paper examines the main ethical issues related to AI-based educational systems. The results indicate four key themes, which include the alteration of teacher roles and human agency in classes mediated by AI, the problem of algorithmic bias and fairness, the problem of data privacy and the ethical management of educational data, and the increasing demand of human-focused AI pedagogy. The discussion indicates that although AI technologies have the potential to make the instruction process more efficient and focus on individualised learning, these applications should be managed according to the ethical frameworks, which are based on the principles of transparency, accountability, equity, and respect to human autonomy. The conclusion reached by the study is that the responsible integration of AI into the educational process must be balanced and be based on a combination of technological innovation and humanistic educational values, ethical governance, and critical digital literacy. The insights are useful in continuing the discussion of AI ethics in education and offer implications of AI in the digital era on the pedagogical practise and educational policy. Keywords : Artificial Intelligence in Education, AI Ethics, Human-Centered Pedagogy, Algorithmic Bias, Data Privacy, Educational Technology Governance.

Zenodo (CERN European Organization for Nuclear Research)
Jadavpur University (IN)
Quality Education
Openalex Percentile: Top 4%
Technology-Enhanced Education Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.