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Cited by 8Artificial Intelligence in EducationEthics and Social Impacts of AIAcademic integrity and plagiarism

Research Insights on the Ethical Aspects of AI-Based Smart Learning Environments: Review on the Confluence of Academic Enterprises and AI

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Pulari, Sini Raj, Jacob, Shomona Gracia·2025·Procedia Computer Science
DOI 10.1016/j.procs.2025.02.122
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Abstract

Dramatic progress in the use of AI for the design of smart learning systems has undoubtedly enhanced the professional competence of students across the globe. However, the sudden access to unlimited autonomy has posed unprecedented risks to moral and ethical practices in educational standards that far outweigh the rewards. In this research, the authors attempt to present a succinct review on the ethical implications of employing artificial intelligence in classroom education both from the educator and the student’s perspective. Summarizing the loopholes in establishing strong ethical practices by virtue of AI leverage, especially in the realm of education is of utmost importance. The findings of this study revealed two main spheres of educational transformation that need to be addressed in smart academic enterprises: (a) automated review generation based on student and evaluator scores (b)infallible system to ensure adherence to ethical practices during assessments.

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Crossref · 10.1016/j.procs.2025.02.122

Cites

17 of 17 references verified0 citation errors · 0 not checkable

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  • Relying on AI for assignments leads to superficial learning, where students produce well-structured responses without demonstrating deep understanding or critical engagement.

    “Moreover, relying on AI for assignments leads to super -ficial learning, where students produce well-structured responses without demonstrating deep understanding or critical engagement [ 66, 67 ].”

    From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [67]

  • Faculty training programs have been developed to help educators recognise AI-generated content, redesign assessment methods, and integrate AI responsibly into teaching and evaluation processes.

    “Faculty training programs have been developed to help educators recognise AI-generated content, redesign assessment methods, and integrate AI responsibly into teaching and evaluation processes [ 62, 67].”

    From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [67]

  • Institutions have shifted toward alternative assessment methods, such as oral exams (viva voce), real-world reflective tasks, scaffolded assignments, reflective journals, Bloom's Taxonomy higher-order thinking questions, and interactive problem-solving tasks, which require direct student engagement and cannot be easily outsourced to AI.

    “To achieve this, institutions have shifted toward alternative assessment methods, such as oral exams (viva voce), real-world reflective tasks, scaffolded assignments, reflective journals, use of Bloom's Taxonomy higher-order thinking questions, and interactive problemsolving tasks, which require direct student engagement and cannot be easily outsourced to AI [ 56, 65, 67, 70, 73, 79, 89, 94, 97-99”

    From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [67]

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Authors
Pulari, S. R., & Jacob, S. G.
Affiliations
Bahrain Polytechnic (BH)
Corresponding author
Pulari, Sini Raj
ORCID
Venue
Procedia Computer Science
Publisher
Elsevier BV
Published
2025
Volume
256
Pages
284-291
ISSN
1877-0509 (ISSN-L)
DOI
10.1016/j.procs.2025.02.122
OpenAlex
W4408338227
Cited by
8
References
17
Citations by year
2026: 6 · 2025: 2
FWCI
9.15
Type
conference paper
Language
en
Open access
Licence
Locations
Procedia Computer Science (publishedVersion, cc-by-nc-nd)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Safety Research › Academic integrity and plagiarism
Keywords
artificial intelligence in education, ethical implications of AI, smart learning environments, academic integrity, automated assessment, automated grading, student independence, educator perspectives, student perceptions, ethics in education

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